The ability of automated vehicles (AV) to avoid accidents in complex traffic environments is the focus of considerable public attention. Intel has proposed a mathematical model called Responsibility-Sensitive Safety (RSS) to ensure AVs maintain a safe distance from surrounding vehicles, but testing has, to date, been limited. This study calibrates and evaluates the RSS model based on cut-in scenarios in which minimal time-to-collision (TTC) is less than 3 s. Two hundred cut-in events were extracted from Shanghai Naturalistic Driving Study data, and the corresponding scenario information for each event was imported into a simulation platform. In each scenario, the human driver was replaced by an AV driven by the model predictive control-based adaptive cruise control (ACC) system embedded with the RSS model. The safety performance of three conditions, the human driver, RSS-embedded ACC model, and ACC-only model, were evaluated and compared. Compared to the performance of human drivers and ACC-only algorithm respectively, the RSS model increased the average TTC per event by 2.86 s and 0.94 s, shortened time-exposed TTC by 1.34 s and 0.65 s, and reduced time-integrated TTC by 0.91 s² and 0.72 s². These changes indicate that the RSS-embedded ACC model can improve safety performance in emergent cut-in scenarios. The RSS model can therefore be applied as a security guarantee, that is, to ensure the AV’s timely awareness and response to dangerous cut-in situations, thus mitigating potential conflict.

自动化车辆（AV）避免在复杂交通环境中发生事故的能力是相当大的公众关注的焦点。英特尔已经提出了一种数学模型，称为责任敏感安全性（RSS），以确保自动驾驶汽车与周围车辆保持安全距离，但是迄今为止，测试一直受到限制。本研究基于最小碰撞时间（TTC）小于3 s的切入场景来校准和评估RSS模型。从上海自然驾驶研究数据中提取了200个切入点事件，并将每个事件的相应场景信息导入到模拟平台中。在每种情况下，人类驾驶员都被嵌入了RSS模型的基于模型预测控制的自适应巡航控制（ACC）系统所驱动的AV所取代。评估并比较了三种条件的安全性能，即驾驶员，嵌入RSS的ACC模型和仅ACC模型。与人类驾驶员和仅使用ACC的算法的性能相比，RSS模型将每个事件的平均TTC增加了2.86 s和0.94 s，将时间暴露的TTC缩短了1.34 s和0.65 s，并将时间积分TTC减少了0.91。 s²和0.72 s ²。这些变化表明，嵌入RSS的ACC模型可以提高紧急情况下的安全性能。因此，RSS模型可以用作安全保证，即确保AV及时了解和响应危险的插入情况，从而减轻潜在的冲突。