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 jective for this workshop is to amplify the impact of behavior-driven autonomous driving research in both academia and industry, resulting in safer, robust, and confident autonomous vehicles.
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.
