IEEE INTELLIGENT VEHICLES SYMPOSIUM
Anchorage, Alaska, USA
June 4 - 7, 2023
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Online zoom link: https://tudelft.zoom.us/j/91317050923
ATTENTION: IEEE ITSS members will receive a free workshops day only registration.
Scope and Topics:
Scope and Topics:
With the development of intelligent vehicles, various novel methods and algorithms have been developed for interactive behaviors modelling in highly dynamic scenarios. The complicated, critical and interactive conditions in the highway and urban scenarios bring a higher demand on precisely formulating, modelling and understanding behaviors of heterogeneous traffic participants. Meanwhile, when driving on the road, intelligent vehicles often interact with other road users socially via implicit communications. It brings a great challenge for the modelling of social interactions among participants. There have been lots of approaches proposed for social, interactive and safe behaviors modelling and prediction. The explorations cover a broad range of techniques and efforts from different aspects, such as graph-based representation (e.g., graph neural network), mechanism modelling, and adaptive methods (transferable driver behavior learning). The progress also includes the development in benchmark datasets (e.g., INTERACTION Dataset) and simulated environments (e.g., highway-envs). Different methods have their particular advantages and characteristics, which are applied in diverse domains and scenarios. This workshop focuses on the discussion of above aspects, including contributions to methodology (graph learning, model adaptation, mechanism modelling, etc.) and development of toolkit (simulated environment and benchmark).
Researchers in related areas from academia and industry are invited to submit extended abstracts (at least 3-pages long with conference template) or full papers to be presented in the format of spotlight presentation and poster presentation.
The topics of interest within the scope of this workshop include, but not limited to, the following:
Benchmarks on social, interactive and safe behavior prediction for intelligent vehicles;
Novel methods or models on social, interactive and safe behavior prediction for intelligent vehicles;
New applications for social, interactive and safe behaviors modelling;
Transferable and continual driver behavior learning;
The development and evaluation of simulation environments for multi-agent interactive behavior modelling;
Interpretability and generalizability of behavior modeling algorithms;
Novel testing and validation methods in highly interactive and dynamic scenarios;
Risky behavior modelling and analysis;
The development of GNN, GAN, VAE in the prediction of motion and behavior;
Human factors in intelligent vehicle systems;
The application and deployment of novel algorithms on the intelligent vehicle platform;
Systematic survey or review for social, interactive and safe behavior modelling.
Dates
Dates
Paper:
Paper Submission Deadline February 01, 2023
Notification of Acceptance March 30, 2023
Final Paper Submission April 22, 2023
Workshop:
Workshop event June 4, 2023
Program (Finally confirmed):
Program (Finally confirmed):
13:40-14:10 Dr. Chen Tang, UC Berkeley. Diagnosing Social Interaction Modeling for Interactive Autonomous Driving
14:10-14:40 Leon.Tolksdorf, CARISSMA. Risk in Stochastic and Robust Model Predictive Path-Following Control for Vehicular Motion Planning
14:40-15:10 Yuanfei Lin, Technische Universität München. Reliable-by-Repair: Trajectory Repairing for Autonomous Vehicles with Rule Compliance
15:10-15:40 Letian Wang, University of Toronto. Human-Centric Driving Behavior Generation
15:40-16:10 Dr. Jiachen Li, Stanford University (Incoming Assistant Professor at UC Riverside) Toward Trustworthy Interactive Autonomy with Relational Reasoning
16:10-16:40 Dr. Karen Leung, Uni of Washington. Towards responsibility-aware safety for autonomous vehicles
16:40-17:10 Dr. Boris Ivanovic, Nvidia. Effectively Integrating Behavior Prediction within the Modern Robotic Autonomy Stack
Organizers:
Organizers:
Zirui Li, Ph.D Candidate
(Corresponding organizer)
E-mail:zirui.li@mailbox.tu-dresden.de
Homepage: Browse
Zirui Li received the B.S. degree from the Beijing Institute of Technology (BIT), Beijing, China, in 2019, where he is currently pursuing the Ph.D. degree in mechanical engineering under the supervision of Prof. Jianwei Gong. From June, 2021 to July, 2022, he was a visiting researcher in Delft University of Technology (TU Delft) with CSC funding from China. From Aug, 2022. He is the visiting researcher in the Chair of Traffic Process Automation at the Faculty of Transportation and Traffic Sciences “Friedrich List” of the TU Dresden. His research focuses on interactive behavior modeling, risk assessment and motion planning of automated vehicles.
Xinwei Wang is an incoming Lecturer (Assistant Professor) at Queen Mary University of London (QMUL), UK in Feb 2023. He is a Postdoc at TU Delft, working for the EU project SAFE-UP, which aims to address risk analysis for future traffic. Prior to that, he was a Postdoc at QMUL, and he obtained a PhD degree from Beihang University, China in 2019. Over the years, he has integrated artificial intelligence and systems engineering for risk assessment, motion planning and decision making in intelligent systems. He has authored over 20 papers, including those in TR Part C, IEEE T-ITS, IEEE T-VT, IEEE T-AES, etc.
Xiaolin He, Ph.D Candidate (Marie Skłodowska-Curie Early Stage Researcher)
E-mail: x.he-2@tudelft.nl
Homepage: Browse
Xiaolin He received the B.S. and M.S. degrees in vehicle engineering from Jilin University, Changchun in 2016 and Tongji University, Shanghai in 2019 respectively. From 2020, he is pursuing the Ph.D. degree as an Early Stage Researcher on the Marie Skłodowska-Curie research project SHAPE-IT at Delft University of Technology. His main research interests include perceived safety and trust in driving automation.
Meng Wang received the B.Sc. degree from Tsinghua University in 2003, the M.Sc. degree from the Research Institute of Highway (RIOH), Ministry of Transport, in 2006, and the Ph.D. degree (Hons.) from TU Delft in 2014. He was an Assistant Professor (tenured in 2019) at the Department of Transport and Planning of TU Delft, from 2015 to 2021 and the Co-Director of the Electric and Automated Transport Laboratory (hEAT lab). From 2006 to 2009, he was an Assistant Researcher at the National ITS Center of RIOH and a Post-Doctoral Researcher at the Automotive Group, Faculty of Mechanical Engineering, TU Delft, from 2014 and 2015. He is a Full Professor (W3) and the Head of the Chair of Traffic Process Automation with the “Friedrich List” Faculty of Transport and Traffic Sciences, Technische Universität Dresden. His main research interests are traffic flow modelling and control, driver behavior, control design, and impact assessment of connected and automated vehicles. He was a recipient of the IEEE ITS Society Best Ph.D. Dissertation Award in 2015 and the IEEE International Conference on Intelligent Transportation Systems (ITSC) Best Paper Award in 2013. He is an Associate Editor of the journal IEEE TRANSACTIONS OF INTELLIGENT TRANSPORTATION SYSTEMS, IET ITS, and Transportmetrica B and the Editorial Board Member of Transportation Research Part C.
Chao Lu received the B.S. degree in transport engineering from the Beijing Institute of Technology (BIT), Beijing, China, in 2009, and the Ph.D. degree in transport studies from the University of Leeds, Leeds, U.K., in 2015. In 2017, he was a Visiting Researcher with the Advanced Vehicle Engineering Centre, Cranfield University, Cranfield, U.K. He is currently an Associate Professor with the School of Mechanical Engineering, BIT. His research interests include intelligent transportation and vehicular systems, driver behaviour modelling, reinforcement learning, and transfer learning and its applications.
Jianwei Gong received the B.S. degree from the National University of Défense Technology, Changsha, China, in 1992, and the Ph.D. degree from Beijing Institute of Technology, Beijing, China, in 2002. Between 2011 and 2012, he was a Visiting Scientist with the Robotic Mobility Group, Massachusetts Institute of Technology, Cambridge, MA, USA. He is currently a Professor and the Director of the Intelligent Vehicle Research Centre, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China. His research interests include intelligent vehicle environment perception and understanding, decision making, path/motion planning, and control. He served as a co-organizer of the workshop in 2018 IEEE Intelligent Vehicles Symposium and ITSC 2021.
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