Evaluation of Responsibility-Sensitive Safety (RSS) Model based on Human-in-the-loop Driving Simulation

Safety is an important challenge in the development of automated vehicles (AVs). To help with the challenge of achieving higher safety in the decision making of AVs, Intel and Mobileye have proposed a parameterized model named Responsibility-Sensitive Safety (RSS). Previous studies have demonstrated that RSS has the potential to improve the safety performance of automated vehicles. However, RSS could lead to a considerable car-following distance depending on the parameter values chosen for the model, which could reduce traffic efficiency. To improve the efficiency of RSS applied to Adaptive Cruise Control (ACC) systems, previous work proposed an efficiency-optimal (referred as “Efficiency-optimal RSS”) variation of the RSS model that involves different triggering conditions of a proper response. Therefore, in this paper a human-in-the-loop driving simulation experiment was conducted to evaluate the performance and acceptance of different safety methods. The RSS model and the efficiencyoptimal variant were embedded in an ACC system based on Model Predictive Control (MPC) algorithm. Two car-following scenarios with a sudden deceleration of lead vehicle at various time headways were simulated to evaluate the human perception and response of the different models. Results show that the original RSS model improves subjective safety judgment of human drivers. While the Efficiency-optimal RSS variant has a lower subjective safety score when compared to original RSS, it significantly reduces driver's emergency braking reactions when compare to an ACC only system.