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.

安全是自动驾驶汽车（AVs）开发中的重要挑战。为了确保AV的安全性，英特尔和Mobileye提出了一种称为“责任敏感安全性（RSS）”的模型。先前的研究表明，RSS有可能提高AV的安全性能，尤其是对于部分自动驾驶算法而言。然而，事实表明，RSS导致相当大的跟车距离，这对交通效率产生负面影响。为了提高RSS应用于自适应巡航控制（ACC）系统时的效率，本文提出了一种涉及RSS触发条件的改进策略。根据不同的跟车假设，定义了两个安全距离触发因素。为了测试RSS模型的性能，基于模型预测控制（MPC）算法，将原始和改进的RSS模型嵌入到ACC中。模拟了在不同时间行进中领先车辆（LV）突然减速的跟车情况，以评估改进的RSS模型的性能。结果表明，在考虑车辆意图计算的安全距离边界处触发RSS是更好的RSS模型。这种改进的RSS模型在大多数风险情况下与原始的RSS具有与ACC系统类似的安全改进效果，并且在跟车效率方面表现更好。由于改进的RSS模型在安全性和效率之间实现了更好的权衡，因此可以用于提高部分自动驾驶算法（如ACC）在高速公路上的自动跟车操作中的安全性能。