Safety Evaluation of Responsibility-Sensitive Safety (RSS) on Autonomous Car-Following Maneuvers Based on Surrogate Safety Measurements

Chen Chai1, Xianming Zeng1, Xiangbin Wu2, Xuesong Wang1

  • 1Tongji University
  • 2INTEL

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Special Session

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11:00 - 12:00 | Mon 28 Oct | Gallery Room 4 | MoC-T7

Special Session on Solving the Automated Vehicle Safety Assurance Challenge (I)

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Abstract

This paper evaluates the safety effect of Responsibility-Sensitive Safety (RSS) model on autonomous car-following maneuvers. The RSS model is embedded into Model Predictive Control (MPC) based Adaptive Cruise Control (ACC) algorithm. Car-following scenarios with sudden deceleration of lead vehicle at various front gap and velocity conditions are simulated. Vehicle movements characteristics and surrogate safety measurements are analyzed to evaluate safety performance of simulation before and after RSS is embedded. Results show that ACC safety performance can be improved significantly by RSS at large front gap conditions. As ACC is an optimization-based algorithm, large initial front gap leads to late deceleration decision and thus result in low safety performance. RSS model, which is derived from drivers’ perception and reaction according to emergencies, can be applied as a safety guarantee to improve safety performance of such optimization-based algorithms.

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