October 27, 2022 at IROS'22 in Kyoto, Japan !
Autonomous driving research has advanced significantly through continuous improvements in perception, prediction, and planning. Despite this success, autonomous driving is limited to simple, sparse, and homogeneous traffic scenarios such as wide highways and sparsely populated urban cities. Scenarios like navigating unprotected left turns, unsignalized intersections, and roundabouts require autonomous vehicles to negotiate with human drivers and anticipate their actions, which often depend on the drivers’ behavior that autonomous vehicles cannot predict. Human driver behavior ranges from aggressive to conservative and different behaviors produce different styles; autonomous vehicles struggle to navigate these situations, especially in dense and unstructured traffic. Researchers and practitioners, therefore, have begun research at the intersection of human driver behavior modeling, multi-agent systems, and autonomous driving.
These three areas, combined, complement academic and industry work in perception, prediction, and planning. This workshop provides a platform to highlight recent efforts that advance behavior-driven autonomous driving, simulation, and ADAS with applications in human driver behavior modeling, multi-agent systems, and autonomous driving. Through invited talks, panel discussions, and paper presentations, attendees will become familiar with the latest research and network for new collaborations. Our
One of the main goals of our workshop is to bridge the gap between the Multi-agent and Multi-Robot systems, Cognitive Robotics, and the Autonomous Driving community. The organizers and speakers are from top academic institutes (Stanford, Berkeley, CMU, Tsinghua) and industry organizations (Tesla, Waymo, Wayve, TRI) and have a lot of real world experience as well as a strong publishing record in dealing with these aspects. The organizers and speakers have also collected some challenging datasets on autonomous driving which are a great test bed for addressing research problems in cooperation, teaming, driver behavior, and game-theoretic aspects. Finally, we are proud to support great diversity in our team including age, gender, ethnicity, and location.
Tesla
University of California, Berkeley
Toyota Research Institute
Stanford University
Boston University
UIUC
UT Austin, Sony AI
Tsinghua University
Waymo Research
Wayve
Waabi
UMD
We invite participants to submit either short (4+n) or long papers (8+n). We encourage the submission of early ideas, late-breaking results, position papers, or open research questions that are likely to generate interesting discussions. Work published elsewhere is allowed. Accepted papers will be presented in a poster session and selected papers as spotlight talks. All submitted contributions will go through a single blind review process.
The specific goals of the workshop will be to discuss ideas around following topics:
Submissions closed.
We will be awarding 1-2 best student paper awards (sponsored by Wayve)!
Our workshop is endorsed by the following RAS technical committees:
Time | Event | Details |
09:00 - 09:35 | Introductory Remarks | |
09:35 - 10:00 | Talk 1 - Dinesh Manocha | Behavior Modeling in Dense and Heterogeneous Traffic for Autonomous Driving |
10:00 - 10:25 | Talk 2 - Jamie Shotton | Learning a Globally Scalable Driving Intelligence |
10:25 - 10:40 | Coffee Break | Coffee locations (yellow coffee icon) |
10:40 - 11:05 | Talk 3 - Alyssa Pierson | Modeling Interactions within Multi-Agent Autonomous Driving |
11:05 - 11:30 | Talk 4 - Ashok Elluswamy | Scalable Self-Driving |
11:30 - 11:55 | Talk 5 - Peter Stone | Reward (Mis)design for Autonomous Driving and Accumulating Safety Rules from Catastrophic Action Effects |
11:55 - 13:00 | Lunch | |
13:00 - 13:25 | Talk 6 - Rowan McAllister | Robust Behavior Models for Autonomous Driving |
13:25 - 13:50 | Talk 7 - Nick Rhinehart | Contingency Planning with Learned Models of Behavioral and Perceptual Uncertainty |
13:50 - 14:30 | Spotlight Talks | See below for details |
14:30 - 14:55 | Talk 8 - Mac Schwager | Game Theory for Simultaneous Behavior Prediction and Trajectory Planning in AVs |
14:55 - 15:20 | Talk 9 - Hang Zhao | Interactive Motion Prediction and Simulation via Explicit Relation Modeling |
15:20 - 15:35 | Coffee Break | Coffee locations (yellow coffee icon) |
15:35 - 16:00 | Talk 10 - Shimon Whiteson | Learning Realistic & Diverse Agents for Autonomous Driving Simulation | 16:00 - 16:55 | Panel Discussion | See below for details |
16:55 - 17:00 | Concluding Remarks |
"What are some of the research and engineering challenges in terms of pushing AVs to level 3/4/understanding human driver behavior in unstructured environments?"
Google Brain
UT Austin
Toyota Research Institute
Tsinghua University
Waymo Research
The best paper award ($1000, sponsored by Wayve) goes to Imitative Planning using Conditional Normalizing Flow
Shubhankar Agarwal (University of Texas at Austin); Harshit Sikchi (University of Texas at Austin); Cole Gulino (Uber Advanced Technologies Group); Eric Wilkinson (Uber Advanced Technologies Group ); Shivam Gautam (Aurora Innovations Inc.)
Honorable mention to Inverse Reinforcement Learning with Hybrid-weight Trust-region Optimization and Curriculum Learning for Autonomous Maneuvering
Yu Shen (University of Maryland - College Park); Weizi Li (University of North Carolina at Chapel Hill); Ming C Lin (UMD-CP & UNC-CH)