Evaluation and Optimization of Responsibility-Sensitive Safety Models on Autonomous Car-Following Maneuvers

Safety is an important challenge in the development of autonomous vehicles (AVs). To ensure the safety of AVs, Intel and Mobileye have proposed a model called Responsibility-Sensitive Safety (RSS). Previous studies have shown that RSS has the potential to improve the safety performance of AVs, especially for partial autonomous driving algorithms. However, it is been shown that RSS leads to a considerable car-following distance, which has a negative effect on traffic efficiency. To improve the efficiency of RSS when applied to adaptive cruise control (ACC) systems, this paper proposes an improved strategy that involves triggering conditions of RSS. Two triggers of safety distance are defined according to different car-following assumptions. To test the performance of RSS models, original and improved RSS models are embedded in ACC based on model predictive control (MPC) algorithms. Car-following scenarios with a sudden deceleration of the lead vehicle (LV) at various time headways are simulated to evaluate the performance of improved RSS models. Results show that triggering RSS at the boundary of the safety distance calculated by considering the vehicle's intentions is a better RSS model. This improved RSS model has a similar safety improvement effect to the ACC system as the original RSS in most risk scenarios and performs better in car-following efficiency. As the improved RSS model achieves a better trade-off between safety and efficiency, it can be used to improve the safety performance of partial autonomous driving algorithms like ACC on autonomous car-following maneuvers on expressways.