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

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arxiv 2206.03418 v1 pith:J5JV4NFW submitted 2022-06-07 cs.RO cs.LO

Ichiro Hasuo This is my paper · ORCID
classification cs.ROcs.LO
keywords logicalsafetyformalizationmathematicalfoundationsguaranteesintroductionresponsibility-sensitive
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning

    cs.RO 2024-12 conditional novelty 6.0 of 10

    A reinforcement learning framework that uses formal rulebook-based safety scores as rewards can fine-tune a perception model to reduce simulated driving rule violations.

  2. Real-Time Rulebook-Aware Nonlinear MPC for Autonomous Driving with Priority-Biased Tiered Slacks

    cs.RO 2026-07 conditional novelty 5.5 of 10

    A tiered-slack NMPC matches expert safety/regulatory compliance on Waymo closed-loop rollouts once log-imitation metrics are separated from driving quality.

  3. Weight Certificates for Convex Multi-Objective MPC: Geometric Characterization, $\ell^1$ Construction, and $\ell^2$ Foreclosure

    math.OC 2026-08 conditional novelty 5.0 of 10

    A weighted sum equals the lexicographic cascade in convex MPC exactly when the weight supports the upper image at the lexicographic point; such certificates are computable under ℓ1 hinges but expire from tick to tick.

  4. REACT: Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving

    cs.RO 2025-05 reject novelty 4.0 of 10

    REACT uses a kinetic-energy risk field and grid-based directional warnings to trigger collision avoidance, reporting 100% success in four vehicle trials.

  5. RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios

    cs.RO 2025-04 reject novelty 4.0 of 10

    RiskNet couples a direction-weighted interaction field with GNN-based multimodal trajectory prediction to produce probabilistic risk maps for autonomous driving.

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