Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding - Hongliang Lu; Meixin Zhu; Chao Lu; Shuo Feng; Xuesong Wang; Yinhai Wang; Hai Yang (Proceedings of the National Academy of Sciences, 2025)
Autonomous vehicles (AVs) will soon cruise our roads as a global undertaking. Beyond completing driving tasks, AVs are expected to incorporate ethical considerations into their operation. However, a critical challenge remains. When multiple road users are involved, their impacts on AV ethical decision-making are distinct yet interrelated. Current AVs lack social sensitivity in ethical decisions, failing to enable both differentiated consideration of road users and a holistic view of their collective impact. Drawing on research in AV ethics and neuroscience, we propose a scheme based on social concern and human-plausible cognitive encoding. Specifically, we first assess the individual impact that each road user poses to the AV based on risk. Then, social concern can differentiate these impacts by weighting the risks according to road user categories. Through cognitive encoding, these independent impacts are holistically encoded into a behavioral belief, which in turn supports ethical decisions that consider the collective impact of all involved parties. A total of two thousand benchmark scenarios from CommonRoad are used for evaluation. Empirical results show that our scheme enables safer and more ethical decisions, reducing overall risk by 26.3%, with a notable 22.9% decrease for vulnerable road users. In accidents, we enhance self-protection by 8.3%, improve protection for all road users by 17.6%, and significantly boost protection for vulnerable road users by 51.7%. As a human-inspired practice, this work renders AVs socially sensitive to overcome future ethical challenges in everyday driving.