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Performance and Safety Evaluation of Responsibility-Sensitive Safety in Freeway Car-Following Scenarios Using the Intelligent Driver Model and Model Predictive Control

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ABSTRACT: This research evaluates the Responsibility-Sensitive Safety (RSS) model and a modified RSS in freeway car-following scenarios extracted from the Shanghai Naturalistic Driving Study (SH-NDS). In this project, 6146 car-following scenarios were extracted and divided into two groups, normal scenarios (5923) and safety-critical events (SCEs) (223), to evaluate the performance and safety of the RSS strategy. RSS was proposed by Mobileye as a mathematical model that defines the real-time safety distance that the automated vehicle needs to maintain from surrounding vehicles. Some modifications were made on the RSS safe distance to reduce its conservativeness without affecting safety. The modified RSS was then embedded into the Intelligent Driver Model (IDM) and the Model Predictive Control (MPC), but these modifications caused some problems, so the IDM and MPC were also modified to match the modified RSS. Vehicle-movement characteristics and surrogate safety measurements were analyzed to evaluate performance and safety. The performance results show that the modified RSS, IDM, and MPC are better than the originals in that they have higher average speeds, smaller relative distances, and better acceleration patterns. To evaluate safety, human drivers were compared with the modified IDM and MPC. The RSS reduced the severity of 88% of the SCEs and increased mean minimum time to collision from 1.53 s and 1.65 s to 3.44 s and 4.08 s for the IDM and MPC, respectively, versus human drivers. Therefore, the RSS model can be applied as a security guarantee to ensure the AV’s response to dangerous car-following situations.

 

Omar Hassanin, Xuesong Wang, Xiangbin Wu. Performance and Safety Evaluation of Responsibility-Sensitive Safety in Freeway Car-Following Scenarios Using the Intelligent Driver Model and Model Predictive Control. Transportation Research Board 100th Annual Meeting, Washington D.C., USA, 2021. 1.25-29.  

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