Autonomous Vehicles Meet the Physical World: RSS, Variability, Uncertainty, and Proving Safety (Expanded Version)
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Abstract (arXiv)
The Responsibility-Sensitive Safety (RSS) model offers provable safety for vehicle behaviors such as minimum safe following distance. However, handling worst-case variability and uncertainty may significantly lower vehicle permissiveness, and in some situations safety cannot be guaranteed. Digging deeper into Newtonian mechanics, we identify complications that result from considering vehicle status, road geometry and environmental parameters. An especially challenging situation occurs if these parameters change during the course of a collision avoidance maneuver such as hard braking. As part of our analysis, we expand the original RSS following distance equation to account for edge cases involving potential collisions mid-way through a braking process. We additionally propose a Micro-Operational Design Domain (μODD) approach to subdividing the operational space as a way of improving permissiveness. Confining probabilistic aspects of safety to μODD transitions permits proving safety (when possible) under the assumption that the system has transitioned to the correct μODD for the situation. Each μODD can additionally be used to encode system fault responses, take credit for advisory information (e.g., from vehicle-to-vehicle communication), and anticipate likely emergent situations.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 3; 2026: 2.
10 papers.
- 2023Data-centric Operational Design Domain Characterization for Machine Learning-based Aeronautical Productscited by 4
- 2025CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Predictioncited by 4
- 2025LLM-based Realistic Safety-Critical Driving Video Generationcited by 2
- 2023System Theoretic View on Uncertaintiescited by 1
- 2024A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Modelcited by 1
- 2025Incremental Validation of Automated Driving Functions using Generic Volumes in Micro- Operational Design Domainscited by 1
- 2026Defining Operational Conditions for Safety-Critical AI-Based Systems from Datacited by 1
- 2021Testing Autonomous Systems with Believed Equivalence Refinementcited by 0
- 2022Safe AI -- How is this Possible?cited by 0
- 2026VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Drivingcited by 0
- 2026VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving6 Jul 2026
- 2026Defining Operational Conditions for Safety-Critical AI-Based Systems from Data29 Jan 2026
- 2025Incremental Validation of Automated Driving Functions using Generic Volumes in Micro- Operational Design Domains12 Dec 2025
- 2025LLM-based Realistic Safety-Critical Driving Video Generation2 Jul 2025
- 2025CooperRisk: A Driving Risk Quantification Pipeline with Multi-Agent Cooperative Perception and Prediction18 Jun 2025
- 2024A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model23 Aug 2024
- 2023Data-centric Operational Design Domain Characterization for Machine Learning-based Aeronautical Products15 Jul 2023
- 2023System Theoretic View on Uncertainties7 Mar 2023
- 2022Safe AI -- How is this Possible?25 Jan 2022
- 2021Testing Autonomous Systems with Believed Equivalence Refinement8 Mar 2021