Efficiency performance and safety evaluation of the responsibility-sensitive safety in freeway car-following scenarios using automated longitudinal controls

Abstract

The Responsibility-Sensitive Safety (RSS) model was proposed by Mobileye as a mathematical model that defines the real-time safety distance that the automated vehicle (AV) needs to maintain from surrounding vehicles. However, RSS strategy tends to be overly conservative. This research made modifications to the RSS safe distance to reduce its conservativeness without affecting safety, and evaluated the modified (RSS_X) and original RSS (RSS_O) in freeway car-following scenarios extracted from the Shanghai Naturalistic Driving Study (SH-NDS). The modifications were replacing the maximum acceleration with the positive previous time-step acceleration and adding standstill gap. In this study, 6,146 car-following scenarios were extracted and divided into two groups, normal scenarios (5,923) and safety-critical events (SCEs, near crashes) (223), to evaluate the efficiency and safety performance of RSS. The RSS_O and RSS_X were then embedded into the intelligent driver model (IDM) and model predictive control (MPC), but because the RSS modifications caused a few acceleration stability problems, the IDM and MPC were also modified to accommodate the RSS_X. The efficiency performance results showed that the modified models (IDM + RSS_X, MPC + RSS_X) performed better than the originals (IDM + RSS_O, MPC + RSS_O) in that they had higher average speeds, more comfortable acceleration pattern, and smaller longitudinal clearance between vehicles, which leads to less conservativeness. To evaluate safety, human drivers were compared with the original and modified models. RSS reduced the severity of at least 80 % of the human driver SCEs. MPC + RSS_X increased the mean minimum time to collision (TTC) for the majority of SCEs from 1.65 s to 4.08 s, and IDM + RSS_X increased the mean minimum TTC for the majority of SCEs from 1.53 s to 3.44 s.

Keywords: Intelligent Driver Model (IDM); Model Predictive Control (MPC); Naturalistic driving study; Responsibility-Sensitive Safety (RSS); Safety-critical event; Surrogate safety measurements.