Program for 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

Time Etoile (2F) Cherie (2F) Kohaku (2F) Ruby (2F) Ruby-1 (2F) Ruby-2 (2F) Hisui (2F) Sakura (3F) Yuri (3F) Bara (3F) Rainbow (7F) Bohyoh (7F)

Monday, October 16

09:00-09:10   WS05-0: Deep Learning for Autonomous Driving WS02: Human Factors in Intelligent Vehicles WS06: Artificial Transportation Systems and Simulation     WS03-1: Eco-drive at Intersections with Connected,Cooperative and Automated Vehicle Technology         WS09: Transportation 5.0
09:10-09:40   WS05-1: Deep Learning for Autonomous Driving            
09:40-10:10   WS05-2: Deep Learning for Autonomous Driving            
10:10-10:40   WS05-3: Deep Learning for Autonomous Driving            
10:45-11:00         WS03-2: Eco-drive at Intersections with Connected,Cooperative and Automated Vehicle Technology        
11:00-11:10               WS07-0: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility
11:10-11:35   WS05-4: Deep Learning for Autonomous Driving     WS03-3: Eco-drive at Intersections with Connected,Cooperative and Automated Vehicle Technology       WS07-1: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility
11:40-12:00               WS07-2: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility
12:00-12:20                     WS07-3: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility  
12:20-12:40                     WS07-4: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility  
12:55-13:00                       WS10-0: International workshop on Large-Scale Traffic Modeling and Management
13:00-13:10       WS04-0: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems     WS01-0: Industry Panel: Connected, cooperative and automated transport         WS10-1: International workshop on Large-Scale Traffic Modeling and Management
13:10-13:15   WS05-5: Deep Learning for Autonomous Driving WS08: Behavioural Change Support Intelligent Transportation Applications     WS01-1: Industry Panel: Connected, cooperative and automated transport        
13:15-13:25   WS04-1: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems            
13:25-13:50               WS10-2: International workshop on Large-Scale Traffic Modeling and Management
13:50-14:00               WS10-3: International workshop on Large-Scale Traffic Modeling and Management
14:00-14:10             WS07-5: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility
14:10-14:15   WS05-6: Deep Learning for Autonomous Driving       WS01-2: Industry Panel: Connected, cooperative and automated transport        
14:15-14:40     WS04-2: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems           WS10-4: International workshop on Large-Scale Traffic Modeling and Management
14:40-14:55   WS05-7: Deep Learning for Autonomous Driving           WS10-5: International workshop on Large-Scale Traffic Modeling and Management
15:10-15:30                   WS10-6: International workshop on Large-Scale Traffic Modeling and Management
15:30-15:40     WS04-3: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems     WS01-3: Industry Panel: Connected, cooperative and automated transport          
15:40-15:45   WS05-8: Deep Learning for Autonomous Driving              
15:45-15:50               WS10-7: International workshop on Large-Scale Traffic Modeling and Management
15:50-16:00             WS07-6: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility
16:00-16:10       WS01-4: Industry Panel: Connected, cooperative and automated transport      
16:10-16:30   WS05-9: Deep Learning for Autonomous Driving           WS10-8: International workshop on Large-Scale Traffic Modeling and Management
16:30-16:35     WS04-4: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems          
16:35-16:50               WS10-9: International workshop on Large-Scale Traffic Modeling and Management
16:50-17:00               WS07-7: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility
17:10-17:30   WS05-10: Deep Learning for Autonomous Driving                
17:30-17:35       WS04-5: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems                

Tuesday, October 17

09:30-10:30 KS-1: Keynote Speech I                    
11:00-12:00 KS-2: Keynote Speech II                    
13:30-15:20 SS-1: Special Session I Tu02-1: Transportation Networks (1) Tu03-1: Autonomous Driving (1)   Tu04-1: Vehicle Localization (1) Tu05-1: Modeling, Control and Simulation (1) Tu06-1: Vision and Environment Perception (1) Tu07-1: ITS (1) Tu08-1: Human Factors (1) Tu09-1: Cooperative Technology (1) Tu10-1: Big Data (1) Tu11-1: Intelligent Vehicle (1)
16:00-17:50 SS-2: Special Session II Tu02-2: Transportation Networks (2) Tu03-2: Autonomous Driving (2)   Tu04-2: Vehicle Localization (2) Tu05-2: Modeling, Control and Simulation (2) Tu06-2: Vision and Environment Perception (2) Tu01-2: Sensing, Detectors and Actuators (1) Tu08-2: Human Factors (2) Tu09-2: Cooperative Technology (2) Tu10-2: Big Data (2) Tu11-2: Intelligent Vehicle (2)

Wednesday, October 18

09:00-10:00 KS-3: Keynote Speech III                    
10:30-12:00 SS-3: Special Session III We03-1: Autonomous Driving (3)   We04-1: Vehicle Localization (3) We05-1: Modeling, Control and Simulation (3) We06-1: Vision and Environment Perception (3)   We08-1: Travel Behavior We09-1: Traffic Flow for Automated Vehicle We10-1: Big Data (3) We11-1: Intelligent Vehicle (3)
13:20-15:10 We01-2: Sensing, Detectors and Actuators (2) We02-2: Transportation Networks (3) We03-2: Autonomous Driving (4)   We04-2: Vehicle Localization (4) We05-2: Modeling, Control and Simulation (4) We06-2: Vision and Environment Perception (4)   We08-2: Commercial Vehicle We09-2: Traffic Management We10-2: Big Data (4) We11-2: Intelligent Vehicle (4)
15:40-17:10 We01-3: Sensing, Detectors and Actuators (3) We02-3: Transportation Networks (4) We03-3: Autonomous Driving (5)   We04-3: Vehicle Localization (5) We05-3: Modeling, Control and Simulation (5) We06-3: Vision and Environment Perception (5)   We08-3: ITS (2) We09-3: Public Transportation We10-3: Big Data (5) We11-3: Intelligent Vehicle (5)

Thursday, October 19

09:00-10:30 AS-1: Award Speech                    
11:00-12:50     Th03-1: Autonomous Driving (6)   Th04-1: Motion Planning (1) Th05-1: Modeling, Control and Simulation (6) Th06-1: Vision and Environment Perception (6) Th07-1: Advanced Driving Assistant System Th08-1: Connected Car (1) Th09-1: Rail Traffic Management Th10-1: Big Data (6) Th11-1: Intelligent Vehicle (6)
14:10-16:00 Th01-2: Electronic Vehicle Th02-2: Communication in ITS Th03-2: Autonomous Driving (7)   Th04-2: Motion Planning (2) Th05-2: Traffic Theory Th06-2: Vision and Environment Perception (7)   Th08-2: Connected Car (2) Th09-2: Air Traffic Management Th10-2: Big Data (7) Th11-2: Intelligent Vehicle (7)

KS-1: Keynote Speech I

SIP Automated Driving

The Cross-ministerial Strategic Innovation Promotion Program (SIP) is a newly established program for achieving science, technology and innovation as a result of the Council for Science, Technology and Innovation exercising its headquarters function to accomplish its role in leading science, technology and innovation beyond the framework of government ministries and traditional disciplines from 2014.

The Council for Science, Technology and Innovation (CSTI) selects projects that answer critical social needs and offer competitive advantage to Japanese industry and the economy as the cross-ministerial initiatives. SIP promotes focused, end-to-end research and development, from basic research to practical application and commercialization, and utilize results in regulations, systems, special wards, government procurement, etc.

3 prioritized societal issues (Energy, Next-Generation Infrastructures, Local Resources) and 11 projects have been selected as follows,

< Energy > Innovative combustion technology, Next-generation power electronics, Innovative structural materials, Energy carrier, Next-generation ocean resources development technologies < Next-Generation Infrastructures > Automated driving system, Technologies for maintenance/upgrading/ management of infrastructures, Reinforcement of resilient function for preventing and mitigating disasters, Cyber-Security for Critical Infrastructure < Local Resources> Technologies for creating next-generation agriculture, forestry and fisheries, Innovative design/manufacturing technologies

Intellectual property management system facilitating strategic corporate use of research results is used. PDs (Program Directors) have been selected by invitation from among top-class leaders in industry and academy. Program Directors break through ministerial silos, managing programs from a cross-ministerial perspective.

"Automated Driving System" is one of the SIP activities. PD is Seigo KUZUMAKI, Toyota Motor Company. 3 Sub-PDs are supporting him, Tateo ARIMOTO / Japan Science and Technology Agency, Masao FUKUSHIMA / Nissan Motor Co., Ltd, Yoichi SUGIMOTO / Honda Motor Co., Ltd. SIP-Automated Driving System (SIP-AD) is acting under the slogan of "In Pursuit of a Transport Society That Brings Everyone a Smile" undertaking activities with the goal of reducing traffic congestion, reducing traffic accident fatalities, and realizing, popularizing and deploying the automated driving system, as a result of utilizing automated driving technologies

Research & Development plans of SIP-AD are as follows, Dynamic map, Human factors, Cyber security, Next generation transportation, Pedestrian traffic safety using Vehicle to Pedestrian communication, V2X for AD cars such as Machine to Machine / Infrastructure communication, Low speed automated mini-buses, so on. These activities are reported to and checked by "Promoting Committee and Working Group" by the industry experts, government and academia members.

In this key note speech, the over-view of SIP-AD project is introduced.

KS-2: Keynote Speech II

From the World's 1st Car Navigation, towards Connected and Automated Driving in the future

Aiming at the collision-free society with the joy and freedom of mobility, Honda has been proactively working on research and developments of intelligent vehicle technologies. As one of its achievements, the World's 1st car navigation system was introduced in 1981, which localizes a subject vehicle's position using dead reckoning with a high precision gas-rate gyro and shows it to a driver with a map on a CRT. It was evolved to one combined also with GNSS and a digital map later, which is the basis of modern car navigation systems. Further, it was connected to servers, uploading floating car data, and made it possible to provide precise real-time traffic information. This map and localization technology has become one of the essential technologies for ITS and Automated Driving. In parallel, Honda has been also developing ADAS (Advanced Driver Assistance System) technologies with environmental sensors such as radars and cameras, and introduced the World's 1st AEB (Automatic Emergency Braking) in 1983, which is also one of the essential technologies for Automated Driving. This presentation reviews the history of the technologies for navigation and ADAS, which Honda has been working on, and introduces prospects on Connected and Automated Driving in the future.

SS-1: Special Session I

International Collaborative Research on Automated Vehicles

Automated and Connected Vehicle technologies CV technologies are viewed as the next generation of Intelligent Transportation Systems that hold promise to provide substantial improvements in safety, mobility, and the environment. The development of Connected and Automated vehicles have gaining momentum. Substantial public and private sector resources are now being invested in Europe, Asia, Europe and the United States, and elsewhere in the world. In this increasingly globalized world, scientists, engineers and innovators must be able to connect, collaborate and create with counterparts around the world. This session discuss about a few new initiatives involving international collaborative research on autonomous driving, covering the areas of trajectory planning, sensing and localization and functional safety. Panelist and topics:

  1. Research on Trajectory Planning under the Chair Drive for All
    Arnaud de La Fortelle, Mines ParisTech (France)

  2. Functional Safety of Automated Driving Systems and AI/Machine Learning - A Dialogue
    Ching-yao Chan, California PATH Program, University of California at Berkeley

  3. V2V Cooperation and Interaction with VRUs in the BRAVE and AutoDrive Projects
    Miguel A. Sotelo Vázquez, University of Alcalá

  4. Integrated environmental sensing and localization
    Shunsuke Kamijo, the University of Tokyo

Tu02-1: Transportation Networks (1)

Lane-Level Route Planning Based on a Multi-Layer Map Model
Chaoran Liu, Kun Jiang, Zhongyang Xiao, Zhong Cao and Diange Yang
External Cost Continuous Type Wardrop Equilibria in Routing Games
Dan Calderone, Roy Dong and Shankar Sastry
Infinite-Horizon Average-Cost Markov Decision Process Routing Games
Dan Calderone and Shankar Sastry
Multi-period Planning of Road Trips in a Cooperative Environment
Giulia Calore, Claudia Caballini, Simona Sacone and Silvia Siri
Bi-Objective Eco-Routing in Large Urban Road Networks
Giovanni De Nunzio, Laurent Thibault and Antonio Sciarretta

Tu03-1: Autonomous Driving (1)

Cognitive Map-based Model: Toward a Developmental Framework for Self-driving Cars
Shitao Chen, Jinghao Shang, Songyi Zhang and Nanning Zheng
Trajectory Planning of Automated Vehicles in Tube-like Road Segments
Mogens Graf Plessen
High-Speed Trajectory Planning for Autonomous Vehicles Using a Simple Dynamic Model
Florent Altche, Philip Polack and Arnaud de La Fortelle
Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming
Mogens Graf Plessen, Pedro Lima, Jonas Mårtensson, Alberto Bemporad and Bo Wahlberg
How Good is My Prediction? Finding a Similarity Measure for Trajectory Prediction Evaluation
Jannik Quehl, Haohao Hu, Omer Tas, Eike Rehder and Martin Lauer

Tu04-1: Vehicle Localization (1)

Failure Detection for Laser-based SLAM in Urban and Peri-Urban Environments
Zayed Alsayed, Guillaume Bresson, Anne Verroust-Blondet and Fawzi Nashashibi
Linear-complexity Stochastic Variational Bayes Inference for SLAM
Xiaoyue Jiang, Michael Hoy, Hang Yu and Justin Dauwels
Autonomous Vehicle Self-Localization Based on Probabilistic Planar Surface Map and Multi-channel LiDAR in Urban Area
Ehsan Javanmardi, Mahdi Javanmardi, Yanlei Gu and Shunsuke Kamijo
GPS-independent Localization for Off-road Vehicles Using Ultra-wideband (UWB)
Hannes Stoll, Peter Zimmer, Frank Hartmann and Eric Sax
Joint Graph Optimization Towards Crowd Based Mapping
Frank Schuster, Wei Zhang, Christoph G Keller, Martin Haueis and Cristobal Curio

Tu05-1: Modeling, Control and Simulation (1)

Structural Observability of Multi-Lane Traffic with Connected Vehicles
Nikolaos Bekiaris-Liberis, Claudio Roncoli and Markos Papageorgiou
Multi-lane Reduction: A Stochastic Single-lane Model for Lane Changing
Cathy Wu, Eugene Vinitsky, Aboudy Kreidieh and Alexandre Bayen
Safety and Mobility Trade-off Assessment of a Microscopic Variable Speed Limit Model
Charles Conran and Montasir Abbas
Comparison of Feedback Linearization & Model Predictive Techniques for Variable Speed Limit Control
Yihang Zhang, Isik Sirmatel, Faisal Alasiri, Petros Ioannou and Nikolas Geroliminis
Partial Speed Trajectory Optimization for Urban Rail Vehicle with Considerations on Motor Efficiency
Shaofeng Lu, Jie Yang, Fei Xue, Tiew On Ting and Huaiying Zhu

Tu06-1: Vision and Environment Perception (1)

Cyclist Detection in LIDAR Scans Using Faster R-CNN and Synthetic Depth Images
Khaled Saleh, Mohammed Hossny, Ahmad H Hossny and Saeid Nahavandi
Pedestrian Intention Recognition by Means of a Hidden Markov Model and Body Language
Raul Quintero, Ignacio Parra, Javier Lorenzo, David Fernández-Llorca and Miguel Angel Sotelo
Natural Vision Based Method for Predicting Pedestrian Behaviour in Urban Environments
Pavan Vasishta, Dominique Vaufreydaz and Anne Spalanzani
VRID-1: A Basic Vehicle Re-identification Dataset for Same Type Vehicles
Xiying Li, Minxian Yuan, Qianyin Jiang and Li Guoming
Fast On-road Object Detector with the Fusion of Object and Scene CNN Features
Zong-Ying Shen, LiChen Fu and Pei-Yung Hsiao

Tu07-1: ITS (1)

Simulation of Cut-In by Manually Driven Vehicles in Platooning Scenarios
Maytheewat Aramrattana, Tony I Larsson, Cristofer Englund, Jonas Jansson and Arne Nåbo
New Estimation of Pedestrian's Rushing Out in Front of Cars by Pressure and Direction Sensors in ITS
Kento Nakamura, Tomotaka Wada and Susumu Kawai
Reducing the Intrusive Driving Behaviour in LDAS Using Machine Learning Approach
Khairul Zulkepli, Mohd Azizi Abdul Rahman and Hairi Zamzuri
Implementation of a Real-Time Data Driven System to Provide Queue Alerts to Stakeholders
Michelle Mekker, Howell Li, Mischa Kachler, John McGregor and Darcy Bullock
How Safe is Automated Driving? Human Driver Models for Safety Performance Assessment
Christian Roesener, Johannes Hiller, Hendrik Weber and Lutz Eckstein

Tu08-1: Human Factors (1)

Modelling the Effect of Human Anticipation on Driving Maneuvers in Lane Changing Process
Li Li, Yun-Tao Chang, Dong Zhang and Hong-Feng Xu
Driving Data Distribution of Human Drivers in Urban Driving Condition
Rui Liu
Using Eye-tracking Technology and Google Street View to Understand Cyclists' Perceptions
William Brazil, Anthony O'Dowd and Brian Caulfield
Drivers' Avoidance Patterns in Near-Collision Intersection Conflicts
Mengxia Hu and Yibing Li
Actions Speak Louder: Effects of a Transforming Steering Wheel on Post-Transition Driver Performance
Brian Mok, Mishel Johns, Stephen Yang and Wendy Ju

Tu09-1: Cooperative Technology (1)

Autonomous Cooperative Driving Using V2X Communications in Off-Road Environment
Ahmed Hussein, Pablo Marin Plaza, Fernando Garcia and José María Armingol
Eco-Platooning of Autonomous Electrical Vehicles Using Distributed Model Predictive Control
Aaron Lelouvier, Jacopo Guanetti and Francesco Borrelli
Forward-looking Automated Cooperative Longitudinal Control
Bernd Schäufele, Kay Massow, Oliver Sawade, Sebastian Bunk, Dennis Pfahl, Ilja Radusch and Birgit Henke
Intra-Platoon Vehicle Sequence Optimization for Eco-Cooperative Adaptive Cruise Control
Peng Hao, Ziran Wang, Guoyuan Wu, Kanok Boriboonsomsin and Matthew J Barth
Optimization of Vehicle Connections in V2V-based Cooperative Localization
Macheng Shen, Ding Zhao and Jing Sun

Tu10-1: Big Data (1)

Vessel Traffic Flow Separation-Prediction Using Low-Rank and Sparse Decomposition
Wen Liu, Jinwei Chen, Zhao Liu, Yan Li, Yi Liu and Jingxian Liu
Data-driven Multi-Agent System for Maritime Traffic Safety Management
Zhe Xiao, Xiuju Fu, Liye Zhang, Loganathan Ponnambalam and Rick Siow Mong Goh
Summarizing Large Scale 3D Point Cloud for Navigation Tasks
Imeen Ben Salah, Sébastien Kramm, Cédric Demonceaux and Pascal Vasseur
Integration of GPS and Satellite Images for Detection and Classification of Fleet Hotspots
Francesco Sambo, Samuele Salti, Luca Bravi, Matteo Simoncini, Leonardo Taccari and Alessandro Lori
Big-video Mining of Road Appearances in Full Spectrums of Weather and Illuminations
Guo Cheng, Zheyuan Wang and Jiang Yu Zheng

Tu11-1: Intelligent Vehicle (1)

LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
Luca Caltagirone, Mauro Bellone, Lennart Svensson and Mattias Wahde
Accurate Vertical Road Profile Estimation Using v-Disparity Map and Dynamic Programming
Ji-yeol Park, Se-Song Kim, Chee Sun Won and Seung-Won Jung
Dynamic Vehicle Velocity Prediction Based on Sensor Data Fusion for Enabling Predictive Functions
Indrasen Raghupatruni
Real-Time Lane Detection Using Spatio-Temporal Incremental Clustering
Ayesha Choudhary and Any Gupta
Framework for Control and Deep Reinforcement Learning in Traffic
Cathy Wu, Kanaad Parvate, Nishant Kheterpal, Leah Dickstein, Ankur Mehta, Eugene Vinitsky and Alexandre Bayen

SS-2: Special Session II

Naturalistic Driving Data Acquisition for Smart Mobility Application

Session abstract: Recently,naturalistic driving data becomes more important roles, not only for investigation of the misbehavior of drivers to develop advanced driver assistant systems (ADAS), but also for development of artificial intelligence for smart mobility related fields. So far, deep learning of big data have been used for interpreting sensor and image data of other vehicles, pedestrians, bicycles, ans so on. Similar learning approach can be applicable to judgement, actions, and evaluations by collecting and learning many traffic situations and drivers' actions for each situation. This special session introduce latest research activities of naturalistic driving study and the platform development for the self driving cars and other applications.

  1. Naturalistic Driving Studies for Driver Assistance and Autonomous Driving: Team LISA perspective
    Mohan M. Trivedi(1), Kevan Yuen(1), Akshay Rangesh(1)
    (1) Laboratory for Intelligent and Safe Automobiles, University of California, San Diego

  2. Developing Driving Behavior Database for Naturalistic Driving Study
    Chiyomi Miyajima(1) and Kazuya Takeda(1)
    (1) Institutes of Innovation for Future Society, Nagoya University

  3. Automan: An Open Service Platform for Autonomous Driving AI
    Shinpei Kato(1), Kazumasa Sakiyama(1), Yuki Tsuji(1), Shunya Seiya(1), Daisuke Fukutomi(1), and Yuki Iida(1)
    (1) University of Tokyo

  4. An Open Platform for Driving and Lifelog Data Collection and Utilization
    Hirofumi Aoki(1), Takahiro Tanaka(1), Masayuki Shimizu(1), Taisuke Sato(2), and Yuki Umemoto(3)
    (1) Institutes of Innovation for Future Society, Nagoya University (2) Kurusugawa Computer Inc. (3) Arc Co., Ltd.

Tu01-2: Sensing, Detectors and Actuators (1)

Stereo Based 3D Reconstruction of Potholes by a Fast, Hybrid Matching Scheme
Uzair Ulhaq, Moeez Ashfaque, Adeel Ahmed, Senthan Mathavan and Khurram Kamal
All Weather Road Edge Identification Based on Driving Video Mining
Zheyuan Wang, Guo Cheng and Jiang Yu Zheng
Introduction to Rain and Fog Attenuation on Automotive Surround Sensors
Sinan Hasirlioglu and Andreas Riener
Automatic Extrinsic Calibration for Lidar-Stereo Vehicle Sensor Setups
Carlos Guindel, Jorge Beltrán, David Martin Gomez and Fernando Garcia

Tu02-2: Transportation Networks (2)

Joint Perimeter and Signal Control of Urban Traffic via Network Utility Maximization
Negar Mehr, Jennie Lioris, Roberto Horowitz and Ramtin Pedarsani
Multimodal Transit Scheduler: An Actor-based Concurrent Approach
Prasad Talasila, Aparajita Haldar, Suhas Pai, Neena Goveas and Bharat Deshpande
Train Scheduling with Short Turning Strategy for an Urban Rail TransitLine with Multiple Depots
Miao Zhang
Real-time Estimation of Aggregated Traffic States of Multi-region Urban Networks
Anastasios Kouvelas, Mohammadreza Saeedmanesh and Nikolas Geroliminis

Tu03-2: Autonomous Driving (2)

Towards a Skill- And Ability-Based Development Process for Self-Aware Automated Road Vehicles
Marcus Nolte, Gerrit Bagschik, Inga Jatzkowski, Torben Stolte, Andreas Reschka and Markus Maurer
Robust Long-Range Teach-and-Repeat in Non-Urban Environments
Felix Ebert, Dennis Fassbender, Benjamin Naujoks and Hans-Joachim Wuensche
Generation and Validation of Virtual Point Cloud Data for Automated Driving Systems
Alexander Schaermann, Timo Hanke, Matthias Geiger, Konstantin Weiler, Nils Hirsenkorn, Stefan Alexander Schneider, Erwin Biebl and Andreas Rauch
How MuCAR Won the Convoy Scenario at ELROB 2016
Carsten Fries, Patrick Burger, Jan Kallwies, Benjamin Naujoks, Thorsten Luettel and Hans-Joachim Wuensche
When to Use What Data Set for Your Self-Driving Car Algorithm: An Overview of Publicly Available Driving Datasets
Hang Yin and Christian Berger

Tu04-2: Vehicle Localization (2)

Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation
Varun Murali, Han-Pang Chiu, Supun Samarasekera and Rakesh Kumar
Behavior-Based Relative Self-Localization in Intersection Scenarios
Benedict Flade, Edoardo Casapietra, Christian Goerick and Julian Eggert
Ego-Lane Estimation by Modeling Lanes and Sensor Failures
Augusto Luis Ballardini, Daniele Cattaneo, Domenico G. Sorrenti, Ignacio Parra, Miguel Angel Sotelo and Rubén Izquierdo
Cross-season Vehicle Localization Using Bag of Local 3D Features
Yoshiki Takahashi, Kanji Tanaka and Yichu Fang
Applying Low Cost WiFi-based Localization to In-Campus Autonomous Vehicles
Noelia Hernández, Ahmed Hussein, Daniel Cruzado, Ignacio Parra and José María Armingol

Tu05-2: Modeling, Control and Simulation (2)

Visualizing Vehicle Arrivals in Coordinated Arterials Using a Colored PCD Concept
Qichao Wang and Montasir Abbas
Estimating Travel Time Distributions Using Copula Graphical Lasso
Anatolii Prokhorchuk, Vishnu Prasad Payyada, Justin Dauwels and Patrick Jaillet
Relocation in Car Sharing Systems with Shared Stackable Vehicles: Modelling Challenges and Outlook
Chiara Boldrini, Riccardo Incaini and Raffaele Bruno
Ridepooling with Trip-Chaining in a Shared-Vehicle Mobility-on-Demand System
Samitha Samaranayake, Kevin Spieser, Harshith Guntha and Emilio Frazzoli
Reducing Range Estimation Uncertainty with a Hybrid Powertrain Model and Online Parameter Estimation
Stefan Sautermeister, Florian Ott, Moritz Vaillant and Frank Gauterin

Tu06-2: Vision and Environment Perception (2)

ORB-SLAM Based Semi-Dense Mapping with Monocular Camera
Boshi Wang, Haiying Wang, Yuan Yu and Limin Zong
Improving the Performance of ADAS Application in Heterogeneous Context: a Case of Lane Detection
Xiebing Wang, Mingyue Cui, Kai Huang, Alois Knoll and Long Chen
Pollution Discrimination on Rail Surface for Adhesion Evaluation Using Hyperspectral Signatures
Claire Nicodeme, Romain Ceolato and Bogdan Stanciulescu
Free-hand Gesture Recognition with 3D-CNNs for In-car Infotainment Control in Real-time
Fabian Sachara, Thomas Kopinski, Alexander Gepperth and Uwe Handmann
Landmarks Based Human-like Guidance for Driving Navigation in an Urban Environment
Bihao Wang, Quentin Stafford-Fraser, Peter Robinson, Eduardo Dias and Lee Skrypchuk

Tu08-2: Human Factors (2)

Optimizing Interaction Between Humans and Autonomy via Information Constraints on Interface Design
Tara Rezvani, Katherine Driggs-Campbell and Ruzena Bajcsy
Driving Behavior Classification Based on Sensor Data Fusion Using LSTM Recurrent Neural Networks
Khaled Saleh, Mohammed Hossny and Saeid Nahavandi
Deployment of Public Bus HMI Displays for Intelligent Junction Safety: A Field Trial
Mark Rice, Chee Wei Ang, Vasudha R., Jerry Kah Eng Hoe, Jian Huang and Saito Hiroki
Distracted Pedestrians Crossing Behaviour: Application of an Immersive Head Mounted Virtual Reality
Anae Sobhani, Bilal Farooq and Zihui Zhong
Pedestrian Positioning in Urban Environment by Integration of PDR and Traffic Mode Detection
Dailin Li, Yanlei Gu and Shunsuke Kamijo

Tu09-2: Cooperative Technology (2)

Cooperative Parking Search: Reducing Travel Time by Information Exchange Among Searching Vehicles
Matthias Rybarsch, Malte Aschermann, Fabian Bock, Anne Goralzik, Felix Köster, Madlen Ringhand and Aleksandar Trifunovic
Smart Mobility for All - A Global Federated Market for Mobility-as-a-Service Operators
Franco Callegati, Maurizio Gabbrielli, Saverio Giallorenzo, Andrea Melis and Marco Prandini
Multiagent-based Cooperative Vehicle Routing Using Node Pressure and Auctions
Madhavan Seshadri, Zhiguang Cao, Hongliang Guo, Jie Zhang and Ulrich Fastenrath
Cooperative Starting Intention Detection of Cyclists Based on Smart Devices and Infrastructure
Maarten Bieshaar, Stefan Zernetsch, Malte Depping, Bernhard Sick and Konrad Doll
Enhanced Vehicle Positioning in Cooperative ITS by Joint Sensing of Passive Features
Gloria Soatti, Monica Nicoli, Nil Garcia, Benoit Denis, Ronald Raulefs and Henk Wymeersch

Tu10-2: Big Data (2)

A Privacy Design Problem for Sharing Transport Service Tour Data
Yueshuai He, Joseph Chow and Mehdi Nourinejad
Validation of a Per-Lane Traffic State Estimation Scheme for Highways with Connected Vehicles
Sofia Papadopoulou, Claudio Roncoli, Nikolaos Bekiaris-Liberis, Ioannis Papamichail and Markos Papageorgiou
Time of Arrival Cumulative Probability in Public Transportation Travel Assistance
Alessio Pagani, Francesco Bruschi and Vincenzo Rana
Big Data Management and Processing in the Context of the System Wide Information Management
Alessandro Leite, Li Weigang, Jose Fregnani and Italo Oliveira
Acquisition of Precise Probe Vehicle Data in Urban City Based on Three-Dimensional Map Aided GNSS
Yanlei Gu, Li-Ta Hsu and Shunsuke Kamijo

Tu11-2: Intelligent Vehicle (2)

Identifying Major Accident Scenarios in Intersection and Evaluation of Collision Warning System
Yeeun Kim, Sehyun Tak, JeongYun Kim and Hwasoo Yeo
Driver's Gaze Prediction in Dynamic Automotive Scenes
Julian Schwehr and Volker Willert
Inertial Sensor Driven Smartphone and Automobile Coordinate System Alignment
Robin Larsson, Isaac Skog and Peter Händel
Driver Classification in Vehicle-Following Behavior by Using Dynamic Potential Field Method
Hanwool Woo, Yonghoon Ji, Yusuke Tamura, Yasuhide Kuroda, Takashi Sugano, Yasunori Yamamoto, Atsushi Yamashita and Hajime Asama
Systematic Literature Review on Driving Behavior
Afonso Vilaça, Pedro Cunha and André Ferreira

KS-3: Keynote Speech III

Smart Cities: An Intelligent Vehicles Perspective

Smart City related initiatives have been gaining popularity around the world. These all provide a showcase for technologists to test their ideas and developments related to ITS (Intelligent Transportation Systems) and IV (Intelligent Vehicles) and understand both the practical deployment issues and the acceptance by the public.

One such testbed/deployment program (possibly one of the largest) is in Columbus, Ohio, USA, where the city has won a large ($50M) grant from the US DoT, almost tripled by cost sharing. An aspect of this project has been understanding the maturity and deployability of numerous technologies and use cases that we all hear about. What is ready to run (not for a couple of demo days, but continuously)? And what is still in a development/proof of concept stage?

After a brief look at the subprojects considered for Columbus deployment, I will try to address the question of "what is a direct application of general ITS/IV R&D?" and "what is special about the Smart City that can orient our IV research?"

Smart Cities can collect and distribute extensive data helping mobility and safety. We can specifically think of use cases in automated parking, in complex intersections and specialized transportation. One aspect that is important in cities is the dense population of pedestrians, and their interaction with automated vehicles. I shall outline our work at the Ohio State University, both in developing small platforms for providing mobility to the impaired, and also the safe motion of such platforms in dense pedestrian environments.

SS-3: Special Session III

QZSS: Quasi-Zenith Satellite System

This special session focuses the recent developments in QZSS, Quasi-Zenith Satellite System. The QZSS is a regional navigation satellite system which cooperates with GPS and improve position accuracy. Presentations from the government and contractors will cover the latest program status, launch of navigation satellites, navigation performance, payload design/characteristics, technical validation results, and application development activities. Attending the special session, you will be fully informed about the QZSS to be operational in 2018.

  1. The Latest Status of QZSS: Service, System, Applications
    Satoshi Kogure, National Space Policy Secretariat of Japan

  2. High Accurate Centimeter-level Positioning Solutions using Quasi-Zenith Satellite System
    Ryoichiro Yasumitsu, Mitsubishi Electric Corp.

  3. Japanese SBAS Service: Status and Future Plan
    Masashi Giho, Civil Aviation Bureau, Ministry of Land, Infrastructure, Transport and Tourism

  4. Some QZSS Demonstrations in Japan
    Yoshiyuki Murai, NEC Corp.

We03-1: Autonomous Driving (3)

A Multi-period Analysis of Taxi Drivers' Behaviors Based on GPS Trajectories
Weiwei Jiang, Jing Lian, Max Shen and Lin Zhang
Predicting Driver Left-Turn Behavior from Few Training Samples Using a Maximum-a-posteriori Method
Dennis Orth, Dorothea Kolossa and Martin Heckmann
Interaction-Aware Driver Maneuver Inference in Highways Using Realistic Driver Models
David Sierra Gonzalez, Victor Romero-Cano, Jilles Dibangoye and Christian Laugier
Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior
Jeremy Morton and Mykel J Kochenderfer

We04-1: Vehicle Localization (3)

Lane-Level Vehicle Self-Localization in Under-Bridge Environments Based on Multi-Level Sensor Fusion
Lijia Xie, Yanlei Gu and Shunsuke Kamijo
Vehicle Trajectory and Lane Change Prediction Using ANN and SVM Classifiers
Rubén Izquierdo, Ignacio Parra, Jesús Muñoz-Bulnes, David Fernández-Llorca and Miguel Angel Sotelo
Autonomous Driving Based on Accurate Localization Using Multilayer LiDAR and Dead Reckoning
Naoki Akai
LIDAR Scan Matching in Off-Road Environment
Rui Yu, Hao Fu and Xiangjing An

We05-1: Modeling, Control and Simulation (3)

Online State and Multidimensional Parameter Estimation for a Macroscopic Model of a Traffic Junction
Luana Chetcuti Zammit, Simon Fabri and Kenneth Scerri
A Multi-method Simulation of a High-frequency Bus Line
Thierry van der Spek
Dynamic Signal Optimization for Traffic Networks
Elvira Thonhofer, Martin Kozek and Stefan Jakubek
Distributed State Observer Based Traffic Density Estimation of Urban Freeway Network
Yuqi Guo, Yangzhou Chen, Wei Li and Chiyuan Zhang

We06-1: Vision and Environment Perception (3)

Region Segmentation Using LiDAR and Camera
M. Hossein Daraei, Anh Vu and Roberto Manduchi
Super-sensor for 360-degree Environment Perception: Point Cloud Segmentation Using Image Features
Robert Varga, Arthur Costea, Horatiu Florea, Ion Giosan and Sergiu Nedevschi
Joint Spatial-Temporal Saliency with Discriminative Correlation Filters
Dawei Zhao, Liang Xiao, Hao Fu, Tao Wu and Bin Dai
Vertical Digital Beamforming Versus Vertical Doppler Beam Sharpening
Amir Laribi, Markus Hahn, Juergen Dickmann and Christian Waldschmidt

We08-1: Travel Behavior

Reconstructing Fixed Time Traffic Light Cycles by Camera Data Analytics
Marco Schönfelder, Valentin Protschky and Thomas Bäck
A Hidden Markov Model for Route and Destination Prediction
Yassine Lassoued, Julien Monteil, Yingqi Gu, Giovanni Russo, Robert Shorten and Martin Mevissen
Model Based Cyclist Energy Prediction
Romain Goussault, Alexandre Chasse and Frédéric Lippens
Traffic Optimization via Road Pricing for Queuing and Flow Congestion
Yusuke Kuboi, Jun-ichi Imura, Tomohisa Hayakawa, Hideaki Tanaka and Yuki Mae

We09-1: Traffic Flow for Automated Vehicle

A Cooperative Intersection Control for Automated Vehicles
Shuo Feng
Rating Cooperative Driving: A Scheme for Behavior Assessment
Christoph Burger, Piotr F. Orzechowski, Omer Tas and Christoph Stiller
A Time Gap-Based Spacing Policy for Full-Range Car-Following
Carlos Flores, Vicente Milanes and Fawzi Nashashibi
Impact of Connected and Automated Vehicles on Traffic Flow
Jackeline Rios-Torres and Andreas Malikopoulos

We10-1: Big Data (3)

Analysis of Speed Characteristics of Urban Expressways Under Rainy Conditions
Xinyu Liu, Bing Wu, Yi Zhao and Lingyu Zheng
Predicting Hazardous Events in Work Zones Using Naturalistic Driving Data
Yohan Chang and Praveen Edara
Identifying Passenger Distribution Corridor for the Transportation Hub Based on Mobile Phone Data
Gang Zhong, Tingting Yin, Jian Zhang, Xiaoxuan Chen, Donghao Yu and Bin Ran
Basic Study of the Estimation of the Cognitive Level Using Senior Driver's Driving Behaviors
Chisa Takahashi, Yurie Iribe, Yasuhiko Nakano, Haruki Kawanaka and Koji Oguri

We11-1: Intelligent Vehicle (3)

Neural Network for Lane Change Prediction Assessing Driving Situation, Driver Behavior and Vehicle Movement
Veit Leonhardt and Gerd Wanielik
Convolution Neural Network-based Lane Change Intention Prediction of Surrounding Vehicles for ACC
Donghan Lee, Youngwook Kwon, Sara McMains and John Hedrick
Time-to-Lane-Change Prediction with Deep Learning
Hien Dang, Johannes Fürnkranz, Maximilian Hoepfl and Alexander Biedermann
Intent Detection and Semantic Parsing for Navigation Dialogue Language Processing
Yang Zheng, Yongkang Liu and John Hansen

We01-2: Sensing, Detectors and Actuators (2)

Efficient Ground Object Segmentation in 3D LIDAR Based on Cascaded Mode Seeking
Michael Hoy, Justin Dauwels and Junsong Yuan
Precise Mobile Laser Scanning for Urban Mapping Utilizing 3D Aerial Surveillance Data
Mahdi Javanmardi, Ehsan Javanmardi, Yanlei Gu and Shunsuke Kamijo
Prediction of Driving Behavior Based on Sequence to Sequence Model with Parametric Bias
Hideaki Misawa, Kazuhito Takenaka, Tomoya Sugihara, HaiLong Liu, Tadahiro Taniguchi and Takashi Bando
Multiple Moving Target Tracking with Hypothesis Trajectory Model for Autonomous Vehicles
Weijie Mei, Guangming Xiong, Jianwei Gong, Yong Zhai, Huiyan Chen and Huijun Di
Automated Rotational Calibration of Multiple 3D LIDAR Units for Intelligent Vehicles
John M Maroli, Ümit Özgüner, Keith Redmill and Arda Kurt

We02-2: Transportation Networks (3)

Multiple Imputation for Incomplete Traffic Accident Data Using Chained Equations
Linchao Li, Jian Zhang, Yonggang Wang and Bin Ran
Auto-Adaptive Multi-Hop Clustering for Hybrid Cellular-Vehicular Networks
Julian P Garbiso, Ada Diaconescu, Marceau Coupechoux and Bertrand Leroy
Unmanned Air Traffic Network Design Concepts
Aaron McFadyen and Troy Bruggemann
How Far is Traffic from User Equilibrium?
Mehmet Yildirimoglu and Osman Kahraman
Structure of Traffic Information Related to Suddenly Occurring Events on Expressway
Masahito Takizawa, Hideki Takahashi, Hajime Oshima, Kouji Yamamoto and Kazutoshi Tago

We03-2: Autonomous Driving (4)

Implementation of Dynamic Boundary on Multiple Kalman Tracking Using Radar
Min-Shiu Hsieh, Siang-You Luo, Po-Hsiang Liao and De-Qiang Ye
Automated Vehicle System Architecture with Performance Assessment
Omer Tas, Stefan Hörmann, Bernd Schäufele and Florian Kuhnt
Minimizing Long Vehicles Overhang Exceeding the Drivable Surface via Convex Path Optimization
Pedro Lima, Rui Oliveira, Jonas Mårtensson and Bo Wahlberg
Multi Object-based Predictive Virtual Lane
Geon Il Lee, Chang Mook Kang, Seung-Hi Lee and Chung Choo Chung
Negotiation of Drivable Areas of Cooperative Vehicles for Conflict Resolution
Stefanie Manzinger and Matthias Althoff

We04-2: Vehicle Localization (4)

A Particle Filter for Vehicle Tracking with Lane Level Accuracy Under GNSS-Denied Environments
Zhong Xionghu, Ramtin Rabiee, Yongsheng Yan and Wee Peng Tay
Object Tracking based on an Extended Kalman Filter in High Dynamic Driving Situations
Alexander Kamann, Jonas Bielmeier, Sinan Hasirlioglu, Ulrich Schwarz and Thomas Brandmeier
Estimating Localization Uncertainty Using Multi-hypothesis Map-Matching on High Definition Road Maps
Franck Li, Philippe Bonnifait and Javier Ibañez-Guzmán
GNSS Mulitpath Detection Using a Machine Learning Approach
Li-Ta Hsu
Hybrid Urban Navigation for Smart Cities
Oisín Moran, Robert Gilmore, Rodrigo H. Ordonez-Hurtado and Robert Shorten

We05-2: Modeling, Control and Simulation (4)

Randomness in Transportation Utility Models
Brian Subirana, Miguel Perez-Sanchis and Sanjay Sarma
Adaptive Traffic Light Control on Highway Entrances
Andrey Alekseenko, Yaroslav Kholodov, Aleksander Kholodov, Yuri Chekhovich and Vsevolod Starozhilets
String Stability of Heterogeneous Platoons with Non-connected Automated Vehicles
Meng Wang, Honghai Li, Jian Gao, Zichao Huang, Bin Li and Bart van Arem
Constructing Multi-Labelled Decision Trees for Junction Design Using the Predicted Probabilities
Erwin Bezembinder, Luc Wismans and Eric van Berkum
Calibration and Evaluation of Car Following Models Using Real-World Driving Data
Mitra Pourabdollah, Eric Bjarkvik, Florian Fuerer, Bjorn Lindenberg and Klaas Burgdorf

We06-2: Vision and Environment Perception (4)

A Context Aware and Video-Based Risk Descriptor for Cyclists
Miguel Costa, Beatriz Quintino Ferreira and Manuel Marques
Exciting Trajectories for Extrinsic Calibration of Mobile Robots with Cameras
Bogdan Khomutenko, Gaëtan Garcia and Philippe Martinet
Wide-baseline Omni-Stereo at Junctions: Extrinsic Auto-Calibration, Trajectory and Speed Estimation
Sokèmi Datondji, Yohan Dupuis, Peggy Subirats and Pascal Vasseur
Classification and Tracking of Traffic Scene Objects with Hybrid Camera Systems
Ipek Baris and Yalin Bastanlar
Transferring Visual Knowledge for a Robust Road Environment Perception in Intelligent Vehicles
Wei Zhou, Roberto Arroyo, Alex Zyner, James Ward, Stewart Worrall, Eduardo Nebot and Luis M. Bergasa

We08-2: Commercial Vehicle

A Multi-objective Model for Dial-a-Ride Problems with Service Quality and Eco-efficiency
Ta-Yin Hu, Guan-Chun Zheng and Tsai-Yun Liao
Multi-Criterion Optimization of Heavy-Duty Powertrain Design
Michael Fries, Maximilian Lehmeyer and Markus Lienkamp
Vehicle Weight Estimation Based on Piezoelectric Sensors Used at Traffic Enforcement Cameras
Timothy Pedersen
Longitudinal Evaluation of the System Performance of a Junction Safety System with Public Buses
Chee Wei Ang, Vasudha R., Jian Huang and Saito Hiroki
Fuel Consumption Prediction for Heavy-duty Vehicles Using Digital Maps
Luis Leon, Alexandre Chasse and Romain Goussault

We09-2: Traffic Management

Hierarchical Coordination of Trains and Traction Substation Storages for Energy Cost Optimization
Hrvoje Novak, Vinko Lešić and Mario Vašak
A Speed Trajectory Optimization Model for Rail Vehicles Using Mixed Integer Linear Programming
Zhaoxiang Tan, Shaofeng Lu, Fei Xue and Kai Bao
Supervisory Multi-Class Event-Triggered Control for Congestion and Emissions Reduction in Freeways
Simona Sacone, Cecilia Pasquale, Silvia Siri and Antonella Ferrara
Optimization for Train Speed Trajectory Based on Pontryagin's Maximum Principle
Kai Bao, Zhaoxiang Tan, Shaofeng Lu and Fei Xue
ART-UTC: An Adaptive Real-Time Urban Traffic Control Strategy
Rob van Kooten, Pieter Imhof, Karst Brummelhuis, Maurits van Pampus, Anahita Jamshidnejad and Bart De Schutter

We10-2: Big Data (4)

Realization of Highly Anticipative Driving in a Partially Connected Vehicle Environment
Md Abdus Samad Kamal, Tomohisa Hayakawa and Jun-ichi Imura
Comparison of Non-linearities in Urban & Highway Traffic Data
Bidroha Basu and Bidisha Ghosh
Integration of Loop and Probe Data for Traffic State Estimation on Freeway and Arterial Links
Amir Hosein Valadkhani, Yang Hong and Mohsen Ramezani
Detection Method of Wide-Area Incident with Massive Probe Vehicle Data
Takahiko Kusakabe
Network-Wide Link Flow Estimation Through Probe Vehicle Data Supported Count Propagation
Richard Brunauer, Stefan Henneberger and Karl Rehrl

We11-2: Intelligent Vehicle (4)

Personalized Maneuver Prediction at Intersections
Viktor Losing, Barbara Hammer and Heiko Wersing
Step Up/Down Motion for a Four-Wheel-Type Vehicle
Shuro Nakajima
Robust Multi-Model Longitudinal Tire-force Estimation Scheme: Experimental Data Validation
Angel Gabriel Alatorre Vazquez, Alessandro Victorino and Ali Charara
Driver Adaptive Predictive Velocity Control
Ulrich Vögele, Johannes Ziegmann and Christian Endisch
Energy and Mobility Benefits from Connected Ecodriving for Electric Vehicles
Xuewei Qi, Peng Wang, Guoyuan Wu, Kanok Boriboonsomsin and Matthew J Barth

We01-3: Sensing, Detectors and Actuators (3)

Low Complexity Techniques for Robust Real-time Traffic Incident Detection
Kratika Garg, Alok Prakash and Srikanthan Thambipillai
Anomaly Detection for Automotive Visual Signal Transition Estimation
Tobias Weis, Martin Mundt, Patrick Harding and Visvanathan Ramesh
Challenges in Head Pose Estimation of Drivers in Naturalistic Recordings Using Existing Tools
Sumit Jha and Carlos A Busso
Probabilistic Estimation of the Driver's Gaze from Head Orientation and Position
Sumit Jha and Carlos A Busso

We02-3: Transportation Networks (4)

Transport Infrastructure Optimization Method Based on a Memetic Algorithm
Olga Saprykina and Oleg Saprykin
Fast Identification of Critical Roads by Neural Networks Using System Optimum Assignment Information
Jordan Ivanchev, Daniel Zehe, Suraj Nair and Alois Knoll
Impact of Mobility-on-Demand on Traffic Congestion: Simulation-basedStudy
David Fiedler, Michal Čáp and Michal Čertický
An Adaptive Control Scheme for Local and Coordinated Ramp Metering
Maria Kontorinaki, Iasson Karafyllis, Markos Papageorgiou and Yibing Wang

We03-3: Autonomous Driving (5)

Ensuring Drivability of Planned Motions Using Formal Methods
Bastian Schürmann, Daniel Heß, Jan Eilbrecht, Olaf Stursberg, Frank Köster and Matthias Althoff
Optimal Path Selection in a Continuous-Time Route Reservation Architecture
Charalambos Menelaou, Stelios Timotheou, Panayiotis Kolios, Christos Panayiotou and Marios Polycarpou
Comparison of Trajectory Tracking Controllers for Autonomous Vehicles
Davide Calzolari, Bastian Schürmann and Matthias Althoff
Exploiting Dream-Like Simulation Mechanisms to Develop Safer Agents for Automated Driving
Mauro Da Lio, Alessandro Mazzalai, Serge Thill, Henrik Svensson, Kevin Gurney, Sean Anderson, David Windridge, Mehmed Yüksel, Andrea Saroldi, Luisa Andreone and Hermann-Josef Heich

We04-3: Vehicle Localization (5)

Efficient Global Localization Using Vision and Digital Offline Map
Martin Buczko and Volker Willert
Momo: Monocular Motion Estimation on Manifolds
Johannes Graeter, Tobias Strauss and Martin Lauer
Compensating Drift of Mono-Visual Odometry Using Road Direction Sign Database
Chanhee Jang, Jae-Eun Park and Young-Keun Kim
Map-based Curvilinear Coordinates for Autonomous Vehicles
Elwan Héry, Stefano Masi, Philippe Xu and Philippe Bonnifait

We05-3: Modeling, Control and Simulation (5)

Rollover Prevention Using Active Suspension System
Abbas Chokor, Reine Talj, Moustapha Doumiati, Ali Charara and Abdelhamid Rabhi
Sensitivity of Traffic Accidents Mitigation Policies Based on Fuzzy Modeling: A Case Study
Amr M. Wahaballa, Aboelkasim Diab, Muhammad Gaber and Ayman Othman
Path Planning for Wheel Loaders: a Discrete Optimization Approach
Beichuan Hong and Xiaoliang Ma
Towards a Qualitative Spatial Model for Road Traffic in Urban Environment
Kamaldeep Singh Oberoi, Géraldine Del Mondo, Yohan Dupuis and Pascal Vasseur

We06-3: Vision and Environment Perception (5)

Detecting the Road Surface Condition by Using Mobile Crowdsensing with Drive Recorder
Bin Piao and Kenro Aihara
Real Time Driver Body Pose Estimation for Novel Assistance Systems
Manuel Martin, Stephan Stuehmer, Michael Voit and Rainer Stiefelhagen
A Hybrid Approach of Candidate Region Extraction for Robust Traffic Light Recognition
Xiaotong Shen, Hans Andersen, Marcelo H Ang, Jr. and Daniela Rus
Fast Reducing Perspective Distortion for Road Marking Recognition
Min Feng, Bin Fang and Weibin Yang

We08-3: ITS (2)

Congestion Barcodes: Exploring the Topology of Urban Congestion Using Persistent Homology
Yu Wu, Gabriel Shindnes, Vaibhav Karve, Derrek Yager, Daniel Work, Arnab Chakraborty and Richard Sowers
Learning to Tell Brake and Turn Signals in Videos Using CNN-LSTM Structure
Han-Kai Hsu, Yi-Hsuan Tsai, Xue Mei, Kuan-Hui Lee, Naoki Nagasaka, Danil Prokhorov and Ming-Hsuan Yang
A Hierarchical Co-Simulation Optimization Control System for Multimodal Freight Routing
Yanbo Zhao, Petros Ioannou and Maged Dessouky

We09-3: Public Transportation

User Context Estimation for Public Travel Assistance and Intelligent Service Scheduling
Alessio Pagani, Francesco Bruschi, Vincenzo Rana and Marcello Restelli
Optimal Design of Transit Demand Management Strategies
Zhenliang Ma and Haris Koutsopoulos
Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning
Ying Liu, Lei Liu and Wei-Peng Chen
Automatic Train Supervision for a CBTC Suburban Railway Line Using Multiobjective Optimization
Juliette Pochet, Sylvain Baro and Guillaume Sandou

We10-3: Big Data (5)

A New Modelling Framework over Temporal Graphs for Collaborative Mobility Recommendation Systems
Bogdan Toader, Assaad Moawad, Francois Fouquet, Thomas Hartmann, Mioara Popescu and Francesco Viti
Impact of Surrounding Cyclists on Car Driver Behavior Recognition at Roundabouts
Min Zhao, David Käthner, Dirk Söffker, Meike Jipp and Karsten Lemmer
Characterizing Activity Patterns Using Co-Clustering and User-Activity Network
Ali Arian, Alireza Ermagun and Yi-Chang Chiu
Mobility Knowledge Discovery to Generate Activity Pattern Trajectories
Mahdieh Allahviranloo, Ludovic Chastanet De Castaing and Jakob Rehmann

We11-3: Intelligent Vehicle (5)

Simultaneous Tracking and Shape Estimation Using a Multi-Layer Laserscanner
Stefan Kraemer, Mohamed Essayed Bouzouraa and Christoph Stiller
Fog Detection for De-fogging of Road Driving Images
Kwang Yeon Choi, Kyeong Min Jeong and Byung Cheol Song
Parking Slots Detection on the Equivalence Sphere with a Progressive Probabilistic Hough Transform
Giulio Bacchiani, Marco Patander, Alessandro Cionini and Domenico Giaquinto
Learning and Predicting On-road Pedestrian Behavior Around Vehicles
Nachiket Deo and Mohan M Trivedi

AS-1: Award Speech

Th03-1: Autonomous Driving (6)

Towards Affordable On-track Testing for Autonomous Vehicle - A Kriging-based Statistical Approach
Zhiyuan Huang, Ding Zhao and Henry Lam
Validation of Collision Frequency Estimation Using Extreme Value Theory
Daniel Åsljung, Jonas Nilsson and Jonas Fredriksson
TrafficNet: An Open Naturalistic Driving Scenario Library
Ding Zhao, Yaohui Guo and Yunhan Jia
Benchmark for Road Marking Detection: Dataset Specification and Performance Baseline
Xiaolong Liu, Zhidong Deng, Hongchao Lu and Lele Cao
V2V and On-Board Sensor Fusion for Road Geometry Estimation
Ahmed Hamdi Sakr, Gaurav Bansal, Vladimeros Vladimerou, Kris Kusano and Miles J Johnson

Th04-1: Motion Planning (1)

Interaction-Aware Occupancy Prediction of Road Vehicles
Markus Koschi and Matthias Althoff
Determining the Maximum Time Horizon for Vehicles to Safely Follow a Trajectory
Silvia Magdici, Zhenzhang Ye and Matthias Althoff
A Leader-Following Approach Based on Probabilistic Trajectory Estimation and Virtual Train Model
Mao Shan, Ying Zou, Mingyang Guan and Changyun Wen
Trajectory Optimization for Autonomous Overtaking with Visibility Maximization
Hans Andersen, Wilko Schwarting, Felix Naser, You Hong Eng, Marcelo H Ang, Jr., Daniela Rus and Javier Alonso-Mora
Static Free Space Detection with Laser Scanner Using Occupancy Grid Maps
Hesham Eraqi, Jens Honer and Sebastian Zuther

Th05-1: Modeling, Control and Simulation (6)

Optimal Powertrain Control of a Heavy-Duty Vehicle Under Varying Speed Requirements
Manne Held, Oscar Flärdh and Jonas Mårtensson
Quantifying the Impact of Limited Information and Control Robustness on Connected Automated Platoons
Robert Dollar and Ardalan Vahidi
Robust Tube-Based MPC for Automotive Adaptive Cruise Control Design
Bijan Sakhdari, Ebrahim Moradi Shahrivar and Nasser L Azad
Method for Comprehensive and Adaptive Risk Analysis for the Development of Automated Driving
David Wittmann, Cheng Wang and Markus Lienkamp
Object Detection in Adaptive Cruise Control Using Multi-Class Support Vector Machine
HyunSoo Park, Daejung Kim, Chang Mook Kang, Seok Cheol Kee and Chung Choo Chung

Th06-1: Vision and Environment Perception (6)

Characterization and Simulation of the Effect of Road Dirt on the Performance of a Laser Scanner
Jose Vargas Rivero, Mario Berk, Ilir Tahiraj, Olaf Schubert, Christoph Glassl, Boris Buschardt and Jia Chen
Saliency Guided Region Proposal Network for CNN Based Object Detection
Ann-Katrin Fattal, Michelle Karg, Christian Scharfenberger and Jürgen Adamy
DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet
Alireza Asvadi, Luis Garrote, Cristiano Premebida, Paulo Peixoto and Urbano Nunes
The Volcanormal Density for Radar-Based Extended Target Tracking
Peter Brosseit, Bharanidhar Duraisamy and Juergen Dickmann

Th07-1: Advanced Driving Assistant System

Multi-Level Vehicle Dynamics Modeling and Export for ADAS Prototyping in a 3D Driving Environment
Róbert Lajos Bücs, Juan Sebastián Reyes Aristizábal, Rainer Leupers and Gerd H. Ascheid
Torque Based Lane Change Assistance with Active Front Steering
Monimoy Bujarbaruah, Ziya Ercan, Vladimir Ivanovic, H. Eric Tseng and Francesco Borrelli
Testing of Autonomous Vehicles Using Surrogate Models and Stochastic Optimization
Halil Beglerovic, Michael Stolz and Martin Horn
A Hybrid Fusion Based Frontal-Lateral Collaborative Pedestrian Detection and Tracking
Ivana Shopovska, Ljubomir Jovanov, Peter Veelaert, Wilfried Philips, Merwan Birem and Kris Lehaen
Comparative Analysis on Information Fusion Conflict Redistribution and Driving Risk Classification
Gultekin Gunduz, Cagdas Yaman, Ali Ufuk Peker and Tankut Acarman

Th08-1: Connected Car (1)

Towards a Real-Time Driver Identification Mechanism Based on Driving Sensing Data
Sasan Jafarnejad, German Castignani and Thomas Engel
Robust Model Predictive Cooperative Adaptive Cruise Control Subject to V2V Impairments
Ellen van Nunen, Jan Verhaegh, Emilia Silvas, Elham Semsar-Kazerooni and Nathan van de Wouw
Automatic Background Filtering and Lane Identification with Roadside LiDAR Data
Jianqing Wu, Hao Xu and Jianying Zheng
Cellular Communication of Traffic Signal State to Connected Vehicles for Arterial Eco-Driving
Grant Mahler, Andreas Winckler, Seyed Alireza Fayazi, Ardalan Vahidi and Martin Filusch
Spatial Stochastic Vehicle Traffic Modeling for VANETs
Yibing Wang, Yongyang Liu and Jingqiu Guo

Th09-1: Rail Traffic Management

Improved Control Strategy of Energy Storage System Considering Train Operation States
Zhihong Yang, Zhongping Yang, Fei Lin and Huan Xia
Comparison of Multi-Objective Optimization Approaches for the Train Load Planning Problem
Silvia Siri and Daniela Ambrosino
Performance Analysis and Evaluation of EDGE System for High-speed Railway
Nan Feng, Jian-wen Ding, Gang Zhu, Siyu Lin, Jiaying Song, Shichao Li and Xiaobing Guo
Prediction Algorithms for Train Arrival Time in Urban Rail Transit
Yafei Liu, Tao Tang and Jing Xun
Scene-segmentation Based Control Strategy of Energy Storage System for Urban Railway
Feiqin Zhu, Zhongping Yang, Fei Lin and Huan Xia

Th10-1: Big Data (6)

Estimating Urban Road Traffic States Using Mobile Network Signaling Data
Thierry Derrmann, Raphael Frank, Francesco Viti and Thomas Engel
Traffic State Estimation Method with Efficient Data Fusion Based on the Aw-Rascle-Zhang Model
Toru Seo and Alexandre Bayen
Short-term Travel Time Prediction by Deep Learning: A Comparison of Different LSTM-DNN Models
Yangdong Liu, Yizhe Wang, Xiaoguang Yang and Linan Zhang
Road Network State Estimation Using Random Forest Ensemble Learning
Yi Hou, Praveen Edara and Yohan Chang

Th11-1: Intelligent Vehicle (6)

Autonomous Braking System via Deep Reinforcement Learning
Hyunmin Chae, Chang Mook Kang, ByeoungDo Kim, Jaekyum Kim, Chung Choo Chung and Jun Won Choi
Predicting Steering Actions for Self-Driving Cars Through Deep Learning
Chaojie Ou, Safaa Bedawi, Arief Koesdwiady and Fakhri Karray
Integrating End-to-End Learned Steering into Probabilistic Autonomous Driving
Christian Hubschneider, André Bauer, Jens Doll, Michael Weber, Sebastian Klemm, Florian Kuhnt and J. Marius Zöllner
Multi-Point Turn Decision Making Framework for Human-Like Automated Driving
Chun-Wei Chang, Chen Lv, Huaji Wang, Dongpu Cao and Efstathios Velenis
Looking Ahead: Anticipatory Interfaces for Driver-Automation Collaboration
Mishel Johns, Brian Mok, Walter Talamonti Jr, Srinath Sibi and Wendy Ju

Th01-2: Electronic Vehicle

Can Small Smart Swappable Battery EVs Outperform Gas Powertrain Economics?
Brian Subirana, Matias Puig-Cortada and Sanjay Sarma
A Simulation-based Heuristic for City-scale Electric Vehicle Charging Station Placement
Ran Bi, Jiajian Xiao, Dominik Pelzer, David Ciechanowicz, David Eckhoff and Alois Knoll
On Charge Point Anxiety and the Sharing Economy
Eoin Thompson, Rodrigo H. Ordonez-Hurtado, Wynita Griggs, Jia Yuan Yu, Brian Mulkeen and Robert Shorten
Energy Consumption Model of Electric Scooter for Routing Applications: Experimental Validation
Konstantinos Genikomsakis, Georgios Mitrentsis, Dimitrios Savvidis and Christos S. Ioakimidis

Th02-2: Communication in ITS

Vehicular Dynamics Based Plausibility Checking
Chaitanya Yavvari, Zoran Duric and Duminda Wijesekera
Roadside Units Deployment in Hybrid VANETs with Synchronous Communication
Taís R. Silva, João Sarubbi and Flávio V C Martins
Modified K-best Receiver for Multi-antenna Vehicular Networks
Shuangshuang Han, Fenghua Zhu, Yingchun Wang, Dongpu Cao, Gang Xiong and Feiyue Wang
Research on Multi - Agent Traffic Signal Control System Based on VANET Information
Xiao Huang, Qi Zhang and Yu Wang
A Memetic Algorithm Approach to Deploy RSUs Based on the Gamma Deployment Metric
Marcelo F. Faraj, João Sarubbi, Flávio V C Martins and Cristiano M. Silva

Th03-2: Autonomous Driving (7)

Interactive Ramp Merging Planning in Autonomous Driving: Multi-Merging Leading PGM (MML-PGM)
Chiyu Dong, John M. Dolan and Bakhtiar Litkouhi
Safe Autonomous Lane Change in Dense Traffic
Rajashekar Chandru, Yuvaraj Selvaraj, Mattias Brännström, Roozbeh Kianfar and Nikolce Murgovski
Towards Tactical Behaviour Planning Under Uncertainties for Automated Vehicles in Urban Scenarios
Mohsen Sefati, Jayesh R Chandiramani, Kai Kreiskoether, Achim Kampker and Simone Baldi
Personalization in Advanced Driver Assistance Systems and Autonomous Vehicles: A Review
Martina Hasenjaeger and Heiko Wersing
Aggressive Vehicle Control Using Polynomial Spiral Curves
Assylbek Dakibay

Th04-2: Motion Planning (2)

Maneuver Planning for AutonomousVehicles, with Clothoid Tentacles for LocalTrajectory Planning
Alia Chebly, Reine Talj and Ali Charara
Using Evidential Occupancy Grid for Vehicle Trajectory Planning Under Uncertainty with Tentacles
Hafida Mouhagir, Véronique Cherfaoui, Reine Talj, François Aioun and Franck Guillemard
Constrained Iterative LQR for On-Road Autonomous Driving Motion Planning
Jianyu Chen, Wei Zhan and Masayoshi Tomizuka
HC Steer: A Novel Extend Fct. for Sampling-Based Nonholonomic Motion Planning in Tight Environments
Holger Banzhaf, Luigi Palmieri, Dennis Nienhüser, Thomas Schamm, Steffen Knoop and J. Marius Zöllner
Automated Driving: Safe Motion Planning Using Positively Invariant Sets
Karl OE Berntorp, Avishai Weiss, Claus Danielson, Ilya Kolmanovsky and Stefano Di Cairano

Th05-2: Traffic Theory

Urban Network Travel Time Prediction via Online Multi-Output Gaussian Process Regression
Hector Rodriguez-Deniz
Fundamental Diagram Estimation Using GPS Trajectories of Probe Vehicles
Yutaka Kawasaki, Toru Seo, Takahiko Kusakabe and Yasuo Asakura
Short-term Traffic Flow Prediction with LSTM Recurrent Neural Network
Danqing Kang, Yisheng Lv and Yuan-yuan Chen
Intersection Analysis Using the Ideal Flow Model
Kardi Teknomo and Roselle Wednesday Gardon

Th06-2: Vision and Environment Perception (7)

Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
Mennatullah Siam, Sara Elkerdawy, Martin Jagersand and Senthil Yogamani
Convolutional Gated Recurrent Networks for Video Semantic Segmentation in Automated Driving
Mennatullah Siam and Senthil Yogamani
A Combined Recognition and Segmentation Model for Urban Traffic Scene Understanding
Malte Oeljeklaus, Frank Hoffmann and Torsten Bertram
Continuous Stereo Camera Calibration in Urban Scenarios
Georg R. Mueller and Hans-Joachim Wuensche

Th08-2: Connected Car (2)

A Car-following Model for Connected Vehicles Under the Bidirectional-leader Following Topology
Yongfu Li and Hang Zhao
A Simple Distributed Algorithm for Lightless Intersection Control Based on Non-linear Interactions
Bo Yang and Christopher Monterola
Evaluation of an MPC Strategy for Motorway Traffic Comprising Connected and Automated Vehicles
Georgia Perraki, Claudio Roncoli, Ioannis Papamichail and Markos Papageorgiou
Traffic Responsive Intersection Control Algorithm Using GPS Data
Craig Rafter, Bani Anvari and Simon Box
Trajectory Prediction of Traffic Agents at Urban Intersections Through Learned Interactions
Atrisha Sarkar, Krzysztof Czarnecki, Matt Angus, Changjian Li and Steven Waslander

Th09-2: Air Traffic Management

Multi-objective Fuzzy Rule-Based Prediction and Uncertainty Quantification of Aircraft Taxi Time
Jun Chen, Michal Weiszer, Elham Zareian, Mahdi Mahfouf and Olusayo Obajemu
Key Distribution Scheme for Aircraft Equipped with Secure ADS-B IN
Thabet Kacem, Duminda Wijesekera and Paulo C.G. Costa
A Type-2 Fuzzy Modelling Framework for Aircraft Taxi-Time Prediction
Olusayo Obajemu and Mahdi Mahfouf
Fast and Near-optimal Trajectory Planning for Fixed-wing UAVs in Urban Area
Zhong Wang and Yan Li

Th10-2: Big Data (7)

A Holistic Framework for Acquisition, Processing and Evaluation of Vehicle Fleet Test Data
Michael Wittmann, Jürgen Lohrer, Johannes Betz, Benedikt Jäger, Maria Kugler, Manfred Klöppel, Adam Waclaw, Moritz Hann and Markus Lienkamp
A Continuum Approach to Assessing the Impact of Spatio-Temporal EV Charging to Distribution Grids
Yoshihiko Susuki, Naoto Mizuta, Akihiko Kawashima, Yutaka Ota, Atsushi Ishigame, Shinkichi Inagaki and Tatsuya Suzuki
Application of Data Analytics to Transport Corridor Diagnostics and Performance Benchmarking
Alwyn Hoffman and Willie Venter
Counting Public Transport Passenger Using WiFi Signatures of Mobile Devices
Tor A Myrvoll, Jan Erik Håkegård, Tomoko Matsui and François Septier
A Convolutional Neural Network for Traffic Information Sensing from Social Media Text
Yuan-yuan Chen, Yisheng Lv, Xiao Wang and Fei-Yue Wang

Th11-2: Intelligent Vehicle (7)

Introducing ASIL Inspired Dynamic Tactical Safety Decision Framework for Automated Vehicles
Siddartha Khastgir, Hakan Sivencrona, Gunwant Dhadyalla, Peter Billing, Stewart Birrell and Paul Jennings
Analysis of Potential Co-Benefits for Bicyclist Crash Imminent Braking Systems
David Good, Kerry Krutilla, Stanley Chien, Lingxi Li and Yaobin Chen
Collision Risk Assessment for Possible Collision Vehicle in Occluded Area Based on Precise Map
Minchul Lee, Myoungho Sunwoo and Kichun Jo
Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge
Pin Wang and Ching-Yao Chan
Adaptability of Automated Driving Systems to the Hazardous Nature of Road Networks
Yrvann Emzivat, Javier Ibañez-Guzmán, Philippe Martinet and Olivier H. Roux

WS01-0: Industry Panel: Connected, cooperative and automated transport


WS01-1: Industry Panel: Connected, cooperative and automated transport

Paper presentations
Vision Based Lane Change Detection Using True Flow Features
Jöran Zeisler, Fabian Schönert, Marcel Johne and Vladimir Haltakov
Leveraging Cloud Intelligence for Hybrid Vehicular Communications
Takamasa Higuchi and Onur Altintas

WS01-2: Industry Panel: Connected, cooperative and automated transport

Invited speeches 1

Invited speeched: Haesik Kim, VTT (14:10-14:25); Satoshi Nagata, NTT DOCOMO (14:25-14:40); Feiyue Wang , Huawei (14:40-14:55)

WS01-3: Industry Panel: Connected, cooperative and automated transport

Invited speeches 2

Invited speeches: Yasin Ferdous Ahmed Khan Ericsson (15:30-15:45); Onur Altintas, Toyota (15:45-16:00)

WS01-4: Industry Panel: Connected, cooperative and automated transport

Industry Panel

Panelists: Tim Leinmüller, Denso / Onur Altintas, Toyota / Satoshi Nagata, NTT DOCOMO / Feiyue Wang, Huawei / TBD China Mobile / Yasin Ferdous Ahmed Khan, Ericsson / Koichi Sakai, University of Tokyo / Meng Lu, Dynniq

WS02: Human Factors in Intelligent Vehicles

Hypovigilance in Limited Self-Driving Automation: Peripheral Visual Stimulus for a Balanced Level of Automation and Cognitive Workload
Cristina Olaverri-Monreal and Jusuf Çapalar
Adaptive Behaviour Selection for Autonomous Vehicle Through Naturalistic Speed Planning
Maradona Rodrigues, Andrew McGordon, Graham Gest and James Marco
Cost-Efficient Brainwave Controller for Automated Vehicles Route Decisions
Armando Astudillo, Francisco Miguel Moreno, Ahmed Hussein and Fernando Garcia
Driver's Decision Analysis in Terms of Pedestrian Attributes -A Case Study in Passing by a Pedestrian-
Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, Ichiro Ide, Hiroshi Murase and Hironobu Fujiyoshi
Coffee break
Effect on Driving Performance of Two Visualization Paradigms for Rear-End Collision Avoidance
Cristina Olaverri-Monreal, Marko Gvozdic and Bharathiraja Muthurajan
Individualized Driver Action Anticipation Using Deep (Bidirectional) Recurrent Neural Network
Oluwatobi O Olabiyi, Eric Martinson, Vijay Chintalapudi and Rui Guo
Evaluating Passenger Characteristics for Ride Comfort in Autonomous Wheelchairs
Taishi Sawabe
A Collision Mitigation Strategy for Intelligent Vehicles to Compensate for Human Factors Affecting Manually Driven Vehicles
Raj Haresh Patel, Jérôme Härri and Christian Bonnet

WS03-1: Eco-drive at Intersections with Connected,Cooperative and Automated Vehicle Technology

Signal Plan Stabilization to Enable Eco-Driving
Robbin Blokpoel and Meng Lu
Cluster-Wise Cooperative Eco-Approach and Departure Application Along Urban Signalized Arterials
Ziran Wang, Guoyuan Wu, Peng Hao and Matthew J Barth
Connected and Autonomous Vehicles Coordinating Method at Intersection Utilizing Preassigned Slots
Linguo Chai, Bai-gen Cai, Wei ShangGuan and Jian Wang
Coffee break

WS03-2: Eco-drive at Intersections with Connected,Cooperative and Automated Vehicle Technology

Invited Session : An Extensive Investigation on Cooperative Eco-approach Controller Under Partially Connected and Automated Vehicle Environment

This study evaluated the performance of a cooperative eco-approach control system at signalized intersections under partially connected and automated vehicles environment. The controller was recently published and is the first eco-approach controller that is able to function with existence of surrounding human-driven traffic. The purpose of this work is to further conduct an extensive test on the controller in order to identify the room for improvement. Two different networks were tested, including an isolated signalized intersection and a corridor with two signalized intersections. The measurements of effectiveness (MOE) adopted are throughput, fuel consumption and CO2 emissions. All the before-and-after MOEs have been tested for statistical significance using t-tests, and the Pearson correlation of the MOEs with respect to various traffic-related and infrastructure-related factors was assessed to identify factors that significantly impact controller's performance. The results indicate the eco-approach control generally reduces fuel consumption and emission without harming mobility, and can be equipped at isolated signalized intersections or on signalized arterials. The performance of eco-approach control is affected by several traffic-related and infrastructure-related factors, including market penetration rate of connected and automated vehicles, congestion level, green ratio of traffic signal and minimum speed of connected and automated vehicle. Excessive small distance between adjacent intersections causes significant gating effect and has negative impact on the performance of eco-approach control.

WS03-3: Eco-drive at Intersections with Connected,Cooperative and Automated Vehicle Technology

Oral Session 2
Multi-Camera System for Traffic Light Detection: About Camera Setup and Mapping of Detections
Julian Müller, Andreas Fregin and Klaus Dietmayer

WS04-0: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems

Welcome and opening remarks

WS04-1: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems

Keynote: Smart move

Abstract: In our research we look at demand-responsive urban transportation: next-gen smart mobility. The focus is on concepts of economic feasibility, and on raising its attraction and trust. I'll give an overview of the ongoing work.

Bio: Stephan Winter is Professor and Discipline Leader, Geomatics, at the Department of Infrastructure Engineering, The University of Melbourne. He is a world-wide recognized researcher in spatial information science, specializing in human spatial cognition and interaction, and intelligent mobility / transport. He leads the smart city agenda of the Melbourne School of Engineering. He is member of IEEE.

WS04-2: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems

Session 1: Demand estimation for MoD
Analysis on Supply and Demand of Shared Autonomous Vehicles Considering Household Vehicle Ownership and Shared Use
Mingyang Hao and Toshiyuki Yamamoto
A Critical Analysis of Travel Demand Estimation for New One-Way Carsharing Systems
Reza Vosooghi, Jakob Puchinger, Marija Jankovic and Goknur Sirin

WS04-3: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems

Session 2: Operations of MoD
DeepSpeedometer: Vehicle Speed Estimation from Accelerometer and Gyroscope Using LSTM Model
Qianlong Wang, Yanlei Gu, Jingwen Liu and Shunsuke Kamijo
Rebalancing Shared Mobility-on-Demand Systems: a Reinforcement Learning Approach
Jian Wen, Jinhua Zhao and Patrick Jaillet
Stackable VS Autonomous Cars for Shared Mobility Systems: a Preliminary Performance Evaluation
Chiara Boldrini and Raffaele Bruno

WS04-4: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems

Session 3: Simulation of MoD
Agent-Based Simulation of a Shared, Autonomous and Electric On-Demand Mobility Solution
Benedikt Jäger, Fares Agua and Markus Lienkamp
Development of a Simulation Platform to Implement Vehicle Routing Algorithms for Large Scale Fleet Management Systems
Sarat Chandra Nagavarapu, Twinkle Tripathy and Justin Dauwels
City-wide Shared Taxis:A Simulation Study in Berlin
Joschka Bischoff, Michal Maciejewski and Kai Nagel

WS04-5: Modelling, Analysis and Control of Intelligent Mobility-on-Demand Systems

Closing remarks

WS05-0: Deep Learning for Autonomous Driving

Opening Remarks

WS05-1: Deep Learning for Autonomous Driving

Invited Talk #1 : Virtual Worlds for the Perception and Control of Self-Driving Vehicles

Deep learning has emerged as a key enabling technology for developing autonomous driving under two main paradigms. On the one hand, we can find modular approaches with explicit tasks for detecting the free road, the dynamic objects, etc. and then plan for a safe vehicle maneuver according to particular control laws.  These tasks rely on deep models. On the other hand, there are end-to-end driving approaches able to output vehicle motion commands by processing the raw data with a deep model, without explicit tasks for detecting the free road, the dynamic objects or running a specific control law. In the former case, perception and control are separated, in the latter they are not; but in both cases some sort of ground truth (GT) is required for training and testing the self-driving AI agents. In fact, deep models are very data hungry (raw and GT). Our research group at the CVC/UAB has been investigating during the last eight years how virtual worlds and simulation can help training and testing advanced driver assistance systems (ADAS) first, and self-driving AI agents nowadays. In this talk we review all this research presenting last news about our well-known SYNTHIA environment, as well as new simulation environments such as CARLA. Focusing on how these environments can contribute to both self-driving paradigms, as well as to other tasks related to vision zero traffic accidents.

WS05-2: Deep Learning for Autonomous Driving

Invited Talk #2 : From Deep Learning to Autonomous Driving

Deep Learning and Autonomous Driving are emerging research topics that become more and more interweaved. Besides continuously upcoming new achievements in learning, we see successful approaches in the domain of autonomous vehicles reaching from learning individual components of the overall system, over several components at once, up to directly learning vehicle control commands from visual sensor input. However, when bringing these approaches to real world autonomous driving, the question on how to safely incorporate those techniques into production-grade vehicles arises. This issue can be considered manageable when learning techniques are used for highly dedicated perception tasks with a single learning step, but becomes more complex with increasing responsibilities of the learning system. If vehicles are controlled by learning-based approaches directly, rare failures will have immediate impact and thus more severe consequences. This emphasizes the importance for research towards the additional integration of expert knowledge in order to constrain vehicle behaviors in terms of safety and reliability.  his presentation will outline the power and computational expressiveness of deep learning approaches in autonomous driving. Furthermore, the potential of current end-to-end learning concepts for vehicle control using supervised and unsupervised methods will be discussed. This will be followed by potential methods to combine such learning algorithms with model driven and probabilistic approaches in order to gain comprehensiveness and accountability. Experiments and results from real world scenarios with our autonomous research car CoCar will be shown.

WS05-3: Deep Learning for Autonomous Driving

Invited Talk #3 : Faster Convolutional Architecture Search for Semantic Segmentation

Designing deep learning architectures is a complex task and requires expert knowledge. Convolutional Neural Networks involve different network topologies, layers, layer parameters. In order to automate the design process our approach is based on MetaQNN, the Q-learning agent design architectures by selecting CNN layers. We extend the approach for semantic segmentation task, where architecture involves encoder-decoder layers. To speed up the search process, we use a Hyperband-like technique. Our experiments are evaluated on CamVid urban street scene semantic segmentation dataset. The architectures designed by the Q-learning agent for semantic segmentation task are better than some commonly used hand-designed architectures with similar number of parameters.

WS05-4: Deep Learning for Autonomous Driving

Oral Paper Session #1
Learning Temporal Features with CNNs for Monocular Visual Ego Motion Estimation
Michael Weber, Christoph Rist and J. Marius Zöllner
Speed and Steering Angle Prediction for Intelligent Vehicles Based on Deep Belief Network
Chunqing Zhao, Jianwei Gong, Chao Lu, Guangming Xiong and Weijie Mei
Adding Navigation to the Equation: Turning Decisions for End-to-End Vehicle Control
Christian Hubschneider, André Bauer, Michael Weber and J. Marius Zöllner

WS05-5: Deep Learning for Autonomous Driving

Poster Session
Intent Prediction of Vulnerable Road Users from Motion Trajectories Using Stacked LSTM Network
Khaled Saleh, Mohammed Hossny and Saeid Nahavandi
Feature Detectors for Traffic Light Recognition
Andreas Fregin, Julian Müller and Klaus Dietmayer
An LSTM Network for Highway Trajectory Prediction
Florent Altche and Arnaud de La Fortelle
Deep Fully Convolutional Networks with Random Data Augmentation for Enhanced Generalization in Road Detection
Jesús Muñoz-Bulnes, Carlos Fernandez, Ignacio Parra, David Fernández-Llorca and Miguel Angel Sotelo
Deep Convolution Long-Short Term Memory Network for LIDAR Semantic Segmentation
Ahmad Al Sallab, Khaled Elmadawy, Mostafa Gamal, Moemen Abdelrazek and Hesham Eraqi
Fast Semi-Dense 3D Semantic Mapping with Monocular Visual SLAM
Xuanpeng LI

WS05-6: Deep Learning for Autonomous Driving

Invited Talk #4 : Deep Learning Implementation and Optimization on DRIVE PX Autonomous Driving Platform

DL (Deep Learning) offers a superhuman image recognition performance today, and this is the strong motivation to apply this technology for autonomous driving perception. Additionally the capability of driving smoothly in various conditions is expected to be implemented by the End-to-End DL. NVIDIA has developed an autonomous driving platform Drive PX that include both of those DL features. This platform also implement massive parallel computing that is necessary to execute human written algorithms for image processing, localization, path planning etc. In this talk, we will introduce our DRIVE PX platforms based on Parker and Xavier SoCs (System On a Chip), DL and GPU computing implemented on those platforms and key technologies including TensorRT, DLA(DL Accelerators) that enables the DN execution in a practical performance/watt level.

WS05-7: Deep Learning for Autonomous Driving

Invited Talk #5 : Towards Deep Understanding of the Vulnerable Road User

Daimler introduced an advanced set of driver assistance functions starting from 2013 in its Mercedes-Benz S-, E-, and C-Class models using stereo vision. Already included was a pedestrian safety component which facilitates fully automatic emergency braking. During the last years our research focused on developing the next-generation of active pedestrian and cyclist safety systems. These systems extract higher-level visual cues and use more sophisticated motion models for path prediction - enabling a deeper traffic situation understanding. The potential to react earlier in dangerous traffic situations, without increasing the false alarms, make such systems essential for autonomous driving in our cities. In this talk we provide an overview over current research projects and show how these works can greatly improve the perception of difficult traffic situations by making use of modern machine learning methods.

WS05-8: Deep Learning for Autonomous Driving

Invited Talk #6 : Deep learning journey: from driver assistance to driver replacement

According to recent expectations, fully autonomous commercial vehicles should start rolling in the streets in less than three years. This is the fruit of at least a decade of research and development work. In this talk, we will trace this interesting journey focusing on its machine vision aspect. It all started with detecting individual objects from images and videos - especially pedestrians. Soon after, with the successful introduction of convolutional neural networks, pixel segmentation of multiple objects simultaneously was deemed possible to assist drivers in unfavorable weather conditions. Furthermore, another line of research focused on monitoring the driver to detect alertness and concentration. Recently, deep learning has been active in estimating the correct steering angle from camera feeds. Hopefully, integrating all these components together can help enjoying the ride safely in autonomous cars be very soon.

WS05-9: Deep Learning for Autonomous Driving

Oral Paper Session #2
Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
ByeoungDo Kim, Chang Mook Kang, Jaekyum Kim, Seung-Hi Lee, Chung Choo Chung and Jun Won Choi
A Survey on Leveraging Deep Neural Networks for Object Tracking
Sebastian Krebs, Bharanidhar Duraisamy and Fabian Flohr
Imitation Learning for Vision-based Lane Keeping Assistance
Christopher Innocenti, Henrik Lindén, Ghazaleh Panahandeh, Lennart Svensson and Nasser Mohammadiha

WS05-10: Deep Learning for Autonomous Driving

Closing Remarks & discussion

WS06: Artificial Transportation Systems and Simulation

Short-term Traffic Flow Forecasting Based on Wavelet Transform and Neural Network
Liwei Ouyang, Fenghua Zhu, Gang Xiong, Hongxia Zhao and Feiyue Wang
Hybrid Calibration of Agent-Based Travel Model Using Traffic Counts and AVI Data
Peijun Ye and Feiyue Wang
Teaching Self-Driving Cars to Dream: A Deeply Integrated, Innovative Approach for Solving the Autonomous Vehicle Validation Problem
Elias Rocklage
Automated Scenario Generation for Regression Testing of Autonomous Vehicles
Elias Rocklage, Heiko Kraft, Abdullah Karatas and Jörg Seewig
Efficient Expression and Deep Analysis Platform of Massive Traffic Video Data
Xisong Dong, Gang Xiong, Bin Hu, Fenghua Zhu and Zhen Shen
Coffee break
Relaying Algorithm Based on Soft Estimated Information for Cooperative V2X Networks
Shuangshuang Han, Yingchun Wang, Tingting Yao, Fenghua Zhu, Gang Xiong, Dongpu Cao and Feiyue Wang
Behavioral Trajectory Planning for Motion Planning in Urban Environments
Wonteak Lim, Seongjin Lee, Kichun Jo and Myoungho Sunwoo
Market-based Approach for Cooperation and Coordination Among Multiple Autonomous Vehicles
Andras Kokuti, Ahmed Hussein, Arturo de la Escalera and Fernando Garcia

WS07-0: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

Opening Session

WS07-1: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

SESSION I - Demand Patterns

"Future Public Transport Fare Structures: Behavioural and Social Impacts of IST"

Automatic Fare Collection (AFC) systems such as smart cards, or mobile phone based charging are allowing us to collect long-term data records of user behaviour. This information can be used to optimize a transport network including network structure and fares, though understanding and predicting demand elasticities remains a challenging topic. At the same time, new AFC technologies are not only a data source but also allow operators to introduce complex fare structures including spatial, temporal and user group distinguished charging. The talk will discuss some of these trends and possibilities, such as changes from flat or zonal to distance-based fares and price capping. Potential impacts for single travelers as well as specific parts of a hypothetical city will be discussed to illustrate that some cities understandably hesitate to use the full potential of now possible charging structures.

WS07-2: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

SESSION I - Demand Patterns
Daily Metro Origin-Destination Pattern Recognition Using Dimensionality Reduction and Clustering Methods
Chao Yang, Fenfan Yan and Xiangdong Xu

WS07-3: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

SESSION I - Demand Patterns

Predicting the Next Trip of Individual Public Transportation Passengers * by Zhan Zhao, Haris N. Koutsopoulos and Jinhua Zhao

WS07-4: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

SESSION I - Demand Patterns
Short & Long Term Forecasting of Multimodal Transport Passenger Flows with Machine Learning Methods
Florian Toqué, Mostepha Khouadjia, Etienne Côme, Martin Trépanier and Latifa Oukhellou

WS07-5: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

SESSION II - Operations and Traffic Dynamics
Novel C-ITS Support for Electric Buses with Opportunity Charging
Marcin Seredynski and Francesco Viti
Adjusting Bus Timetables Considering Observed Delays and Passenger Numbers
Toshiyuki Nakamura, Jan-Dirk Schmöcker, Nobuhiro Uno, Takenori Iwamoto and Yusuke Watanabe
Fault Diagnosis Method of the On-board Equipment of Train Control System Based on Rough Set Theory
Wei ShangGuan, Junzheng Zhang, Juan Feng and Bai-gen Cai
Non-stationary Traffic Flow Prediction Using Deep Learning
Arief Koesdwiady, Safaa Bedawi, Chaojie Ou and Fakhri Karray

WS07-6: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

SESSION III - Network Dynamics
Resiliency Assessment of Urban Rail Transit Networks: A Case Study of Shanghai Metro
Ming Li, Hongwei Wang and Huashen Wang
Using Mobile Phone Data Analysis for the Estimation of Daily Urban Dynamics
Danya Bachir, Vincent Gauthier, Mounim A El Yacoubi and Ghazaleh Khodabandelou
Simulation of Demand and Supply of Urban Rail in a Multimodal Environment
Kenneth Koh, Carlos Lima Azevedo, Kakali Basak and Moshe Ben-Akiva

WS07-7: The Third International Workshop on Intelligent Public Transports -- Toward the Next Generation of Urban Mobility

Closing Session

WS08: Behavioural Change Support Intelligent Transportation Applications

Implementation of a Mobility Behavior Change Support System in Manila Philippines
Varsolo Sunio, Jan-Dirk Schmöcker and Ma. Regina Justina Estuar
Exploring Influential Factors on Transition Process of Vehicle Ownership in Developing Asian City- A Case Study in Bogor City Indonesia
Sudarmanto Budi Nugroho
Pricing as a Tool to Influence Behaviour
David Lupton
Personalized Persuasion Services for Route Planning Applications
Evangelia Anagnostopoulou, Babis Magoutas, Efthimios Bothos and Gregoris Mentzas
Classifying Aggressive Drivers for Better Traffic Signal Control
Shaimaa Hegazy and Mohamed Moustafa
Coffee break
Impact of Autonomous Vehicles on Pedestrians' Safety
Brian Caulfield
Assessing Passenger Feedback Reliability in Crowd-Sourced Measurement of Transit Ride Quality
Der-Horng Lee and Chandrasekar Parsuvanathan
Reliable Feeder Bus Schedule Optimization in A Multi-mode Transit System
Lu Ling and Feng Li
Incident Detection Using Data from Social Media
Panagiotis Georgakis, Angelica Salas and Ioannis Petalas

WS09: Transportation 5.0

Transportation 5.0 in CPSS: Towards ACP-based Society-Centered Intelligent Transportation
Fei-Yue Wang and Jun Zhang
CPSS Models and Spatiotemporal Collaborative Optimization of Urban Public Transport Dynamic Network
Gang Xiong, Bin Hu, Xisong Dong, Fenghua Zhu, Zhen Shen and Xipeng Zhang
A Hybrid Deep Learning Approach for Urban Expressway Travel Time Prediction Considering Spatial-Temporal Features
Zhihao Zhang, Peng Chen, Yun-peng Wang and Guizhen Yu
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction
Xingyuan Dai, Rui Fu, Yilun Lin, Fei-Yue Wang and Li Li
Risk Diagnosis Model for the Disaster Spread of Highway Traffic Network
Qizhou Hu, Xianghong Li, Minjia Tan and Xiaoning Wang
Memetic Algorithm with Adaptive Local Search for Capacitated Arc Routing Problem
Tingting Yao, Xin Yao, Shuangshuang Han, Yingchun Wang, Dongpu Cao and Feiyue Wang
Lane-changing Rules Based on Cellular Automaton Traffic Flow Model Under V2V Environment
Liang Zhang, Guorong Zheng, Xiaoming Liu and Ang Li
Improved Single Image Haze Removal for Intelligent Driving
Yi Lai, Yuanqi Su, Yuehu Liu and Ying Liu
Scene-Specific Pedestrian Detection Based on Parallel Vision
Wenwen Zhang, Kunfeng Wang, Hua Qu, Jihong Zhao and Fei-Yue Wang
Visual Lane Detection Based on the Hierarchical Grouping Structure
Yuanqi Su, Bonan Cuan and Yuehu Liu
The ParallelEye Dataset: Constructing Large-Scale Artificial Scenes for Traffic Vision Research
Xuan Li, Kunfeng Wang, Yonglin Tian, Lan Yan and Fei-Yue Wang
An Improved Method of Real-time Camera Pose Estimation Based on Descriptor Tracking
Limin Zong, Haiying Wang, Boshi Wang, Qiaochu Fu and Sun Xin
Accelerate the Autonomous Vehicles Reliability Testing in Parallel Paradigm
WuLing Huang, Yisheng Lv, Long Chen and Fenghua Zhu
An Accelerated Testing Approach for Automated Vehicles with Background Traffic Described by Joint Distributions
Zhiyuan Huang, Henry Lam and Ding Zhao

WS10-0: International workshop on Large-Scale Traffic Modeling and Management


WS10-1: International workshop on Large-Scale Traffic Modeling and Management

Macroscopic fundamental diagram for coordinated ramp metering

Recent studies on aggregated traffic modeling of urban networks have shown the existence of a well-defined and low-scatter Macroscopic Fundamental Diagram (MFD) that links network aggregated flow and density. However, the MFD of freeway networks typically exhibits high scatter and hysteresis loops that challenge the monitoring capability and control performance of MFD-based congestion management schemes in freeways. This paper investigates the effect of density heterogeneity and capacity drop on characteristics of freeway MFD based on field traffic data. In addition, we introduce a model to capture the evolution of density heterogeneity which is essential to reproduce the dynamics of freeway MFD accurately. The proposed model is incorporated in a hierarchical control structure for coordinated ramp metering on a freeway stretch with multiple congestion bottlenecks and on- and on-ramps. At the upper level, a model predictive control (MPC) approach is developed to optimize total inflow from on-ramps to the freeway stretch, where the prediction model is formulated based on the proposed MFD model. The lower level controller distributes the optimal total inflows to each on-ramp of the freeway based on local traffic state feedback. The proposed ramp metering framework shows desirable performance to reduce the total time spent and eliminate congestion. The control approach is compared with other coordinated ramp metering controllers based on the MPC framework with different prediction models (e.g. CTM and METANET). Simulation outcomes highlight that the MFD-based hierarchical controller (i) is better able to overcome the modeling mismatch between the prediction model and the plant (process model) in the MPC framework and (ii) requires less computation effort than other nonlinear controllers.

WS10-2: International workshop on Large-Scale Traffic Modeling and Management

Hierarchical management of large-scale networks via path assignment and regional route guidance

Alleviating congestion via manipulation of traffic flows or assignment of vehicles to specific paths has a great potential in achieving efficient network usage. Motivated by this fact, this paper proposes a hierarchical traffic management scheme that combines a network-level regional route guidance model predictive control (MPC) scheme with a region-level subregional path assignment mechanism. The route guidance MPC optimizes network performance based on actuation via regional split ratios, whereas the path assignment mechanism recommends paths for vehicles to follow, realizing the regional split ratios sent by the MPC in order to achieve said performance. Simulation results in a detailed 49 subregion model indicates potential of the proposed scheme in achieving coordination and efficient use of network capacity, leading to increased mobility.

WS10-3: International workshop on Large-Scale Traffic Modeling and Management

A macroscopic approach for optimizing road space allocation of bus lanes in multimodal urban networks through simulation analysis: An Application to the Tokyo CBD Network
A Macroscopic Approach for Optimizing Road Space Allocation of Bus Lanes in Multimodal Urban Networks Through Simulation Analysis: An Application to the Tokyo CBD Network
Takao Dantsuji, Nan Zheng and Daisuke Fukuda

WS10-4: International workshop on Large-Scale Traffic Modeling and Management

3D clustering methods to estimate travel times at large urban scale

This presentation is about partitioning link speed data every 10 min into 3D clusters that proposes a parsimonious sketch of the congestion patterns at the whole city scale. Days with similar patterns are then gathered using consensus clustering methods to produce a unique global pattern that fits multiple days. We show that Amsterdam's network over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. A new travel time estimation method can be derived from such patterns. By matching the current observation to historical consensual 3D speed maps, an efficient real-time method is designed. It successfully predicts 84% trips travel times with an error margin below 25%.

WS10-5: International workshop on Large-Scale Traffic Modeling and Management

A Case Study of a Continuous Flow Intersection and Its Impact on Public Transport
A Case Study of a Continuous Flow Intersection and Its Impact on Public Transport
Andrea Bedini, Lele Zhang and Timothy Garoni

WS10-6: International workshop on Large-Scale Traffic Modeling and Management

Off the Shortest Path: Betweenness on Street Network Level to Study Pedestrian Movement
Off the Shortest Path: Betweenness on Street Network Level to Study Pedestrian Movement
Wai Pan Stephen Law and Martin Traunmueller

WS10-7: International workshop on Large-Scale Traffic Modeling and Management

Multi-Agent Based Road Traffic Control Optimization
Multi-Agent Based Road Traffic Control Optimization
Jayani Withanawasam and Ashoka Karunananda

WS10-8: International workshop on Large-Scale Traffic Modeling and Management

Bus Arrival Time Prediction at Any Distance of Bus Route Using Deep Neural Network Model
Bus Arrival Time Prediction at Any Distance of Bus Route Using Deep Neural Network Model
Wichai Treethidtaphat, Wasan Pattara-Atikom and Sippakorn Khaimook

WS10-9: International workshop on Large-Scale Traffic Modeling and Management


With the expansion of cities and the increasing of traffic demand, the scale of the physics of congestion increases. The macroscopic fundamental diagram (MFD) is widely applied for understanding these physics, and serves as a tool monitoring and control of large-scale networks. The MFD is also becoming a topic of increasing concern. The existence of a well-defined MFD requires spatial homogeneity in traffic distribution in a region, which necessitates transportation network partitioning an essential step in MFD-type applications. This paper moves forward with this direction and investigates the partitioning criteria and methods. Firstly, two partition factors, namely link average density and average speed are defined; Secondly, an extended NCut algorithm is built up and partitions are performed based on the two factors respectively. Then, both partitions are applied and tested in the Sioux Falls network. We employ a variance coefficient to evaluate the performance and a diversity coefficient to qualify the sensitivity of the proposed partitions to the variation of traffic demand. The results reveal that (1) the two methods shall not be regarded as equivalent for obvious differences quantified up to 30%, (2) the density-based method shows advantages in effective and steady identification of traffic patterns under slow-varying demand scenarios and (3) a threshold on the variation of demand can be found, exceeding which the demand profile experiences significant change and the speed-based partition method outperforms the density-based method with respective to partitioned homogeneity. This paper is among the few pioneer works targeting the issues of network partition based on the MFD state variables.

Program last updated on no date/time given