Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty
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Abstract (arXiv)
Ensuring the safety of autonomous vehicles, given the uncertainty in sensing other road users, is an open problem. Moreover, separate safety specifications for perception and planning components raise how to assess the overall system safety. This work provides a probabilistic approach to calculate safety envelopes under perception uncertainty. The probabilistic envelope definition is based on a risk threshold. It limits the cumulative probability that the actual safety envelope in a fully observable environment is larger than an applied envelope and is solved using iterative worst-case analysis of envelopes. Our approach extends non-probabilistic envelopes - in this work, the Responsibility-Sensitive Safety (RSS) - to handle uncertainties. To evaluate our probabilistic envelope approach, we compare it in a simulated highway merging scenario against several baseline safety architectures. Our evaluation shows that our model allows adjusting safety and performance based on a chosen risk level and the amount of perception uncertainty. We conclude with an outline of how to formally argue safety under perception uncertainty using our formulation of envelope violation risk.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jul 2021.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 2.
4 papers.
- 2022Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faultscited by 1
- 2022Safe AI -- How is this Possible?cited by 0
- 2025Context-aware Risk Assessment and Its Application in Autonomous Drivingcited by 0
- 2025Spatial Envelope MPC: High Performance Driving without a Referencecited by 0
- 2025Spatial Envelope MPC: High Performance Driving without a Reference23 Sep 2025
- 2025Context-aware Risk Assessment and Its Application in Autonomous Driving4 Aug 2025
- 2022Perception Simplex: Verifiable Collision Avoidance in Autonomous Vehicles Amidst Obstacle Detection Faults4 Sep 2022
- 2022Safe AI -- How is this Possible?25 Jan 2022