Highlights • ACC embedded with Responsibility-Sensitive Safety (RSS) was tested with NDS data. • The RSS-embedded ACC model was calibrated to achieve optimal safety performance. • The RSS-embedded ACC model, ACC-only and the unassisted human driver was compared. • The RSS-embedded ACC simulation generated a greater number of valid scenarios. • The RSS model performed best in improving AV safety in emergent cut-in scenarios.

Abstract 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.