eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Driving

TL;DR

This paper introduces eRSS-RAMP, a motion planner based on extended responsibility-sensitive safety rules, improving safety and efficiency in autonomous vehicle lane merging and emergency scenarios.

Contribution

It presents a novel rule-adherence motion planner that extends RSS for better handling of interaction uncertainty and collaborative driving in autonomous vehicles.

Findings

Faster lane merging with 53% shorter merging length.

73.5% reduction in merging time.

More stable steering and smoother paths during emergency avoidance.

Abstract

Driving safety and responsibility determination are indispensable pieces of the puzzle for autonomous driving. They are also deeply related to the allocation of right-of-way and the determination of accident liability. Therefore, Intel/Mobileye designed the responsibility-sensitive safety (RSS) framework to further enhance the safety regulation of autonomous driving, which mathematically defines rules for autonomous vehicles (AVs) behaviors in various traffic scenarios. However, the RSS framework's rules are relatively rudimentary in certain scenarios characterized by interaction uncertainty, especially those requiring collaborative driving during emergency collision avoidance. Besides, the integration of the RSS framework with motion planning is rarely discussed in current studies. Therefore, we proposed a rule-adherence motion planner (RAMP) based on the extended RSS (eRSS) regulation for non-connected and connected AVs in merging and emergency-avoiding scenarios. The simulation results indicate that the proposed method can achieve faster and safer lane merging performance (53.0% shorter merging length and a 73.5% decrease in merging time), and allows for more stable steering maneuvers in emergency collision avoidance, resulting in smoother paths for ego vehicle and surrounding vehicles.