Responsibility-Sensitive Safety: an Introduction with an Eye to Logical Foundations and Formalization
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Abstract (arXiv)
Responsibility-sensitive safety (RSS) is an approach to the safety of automated driving systems (ADS). It aims to introduce mathematically formulated safety rules, compliance with which guarantees collision avoidance as a mathematical theorem. However, despite the emphasis on mathematical and logical guarantees, the logical foundations and formalization of RSS are largely an unexplored topic of study. In this paper, we present an introduction to RSS, one that we expect will bridge between different research communities and pave the way to a logical theory of RSS, its mathematical formalization, and software tools of practical use.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2022.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 1.
5 papers.
- 2023Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approachcited by 7
- 2024Compositionally Safe Construction of Autonomous Driving Systemscited by 1
- 2024eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Drivingcited by 0
- 2024Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learningcited by 0
- 2026Real-Time Rulebook-Aware Nonlinear MPC for Autonomous Driving with Priority-Biased Tiered Slackscited by 0
- 2026Real-Time Rulebook-Aware Nonlinear MPC for Autonomous Driving with Priority-Biased Tiered Slacks13 Jul 2026
- 2024Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning4 Dec 2024
- 2024eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Driving4 Sep 2024
- 2024Compositionally Safe Construction of Autonomous Driving Systems20 May 2024
- 2023Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach3 Jul 2023