Social Intelligence for Autonomous Vehicles Interacting With Vulnerable Road Users

Protecting vulnerable road users (VRUs) has always been a top priority in intelligent transportation research. Safety-oriented pre-collision systems have been developed and implemented for vehicles with lower automation levels to protect vulnerable road users via kinematics-based crash calculations. Many research efforts, including naturalistic driving study and standardized system evaluation, have been made to ensure the performance of such safety functionalities. Although the warnings and automatic emergency braking systems can efficiently reduce crash injuries, they cannot support higher-level autonomous vehicles interacting with VRUs and driving efficiently without human interventions in urban settings. Numerous algorithms predicting pedestrian trajectories and future actions have been developed in the past several years, which still cannot fully solve the VRU interaction dilemma. In this presentation, we discuss the importance of social intelligence for autonomous vehicles, emphasizing the capability to estimate and negotiate intentions with VRUs for effective and safe interactions. We will highlight the differences between intention and existing behavior predictions, especially regarding autonomous vehicles' motion planning during vehicle-VRU interactions. A series of research efforts conducted in the Transportation & Autonomous Systems Institute (TASI) at IUPUI will be presented related to developing intention prediction algorithms and benchmark datasets for all types of VRUs. We will propose and discuss open challenges for developing autonomous vehicles with social intelligence.