# Responsibility-Sensitive Safety: an Introduction with an Eye to Logical Foundations and Formalization

arXiv: https://arxiv.org/abs/2206.03418 · HTML: https://mlsanity.com/paper/2206.03418/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2022.

## Built on by (most cited first, 6 of 6)

- [Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2307.01316/) (2023)
- [GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning](https://mlsanity.com/paper/2403.19062/) (2024)
- [Compositionally Safe Construction of Autonomous Driving Systems](https://mlsanity.com/paper/2405.11995/) (2024)
- [eRSS-RAMP: A Rule-Adherence Motion Planner Based on Extended Responsibility-Sensitive Safety for Autonomous Driving](https://mlsanity.com/paper/2409.02503/) (2024)
- [Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning](https://mlsanity.com/paper/2412.02951/) (2024)
- [Real-Time Rulebook-Aware Nonlinear MPC for Autonomous Driving with Priority-Biased Tiered Slacks](https://mlsanity.com/paper/2607.10975/) (2026)

## 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.