Log In Sign Up

Autonomous Vehicles Meet the Physical World: RSS, Variability, Uncertainty, and Proving Safety (Expanded Version)

by   Philip Koopman, et al.

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.


page 1

page 2

page 3

page 4


A Finite-Sampling, Operational Domain Specific, and Provably Unbiased Connected and Automated Vehicle Safety Metric

A connected and automated vehicle safety metric determines the performan...

Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty

Ensuring the safety of autonomous vehicles, given the uncertainty in sen...

Probabilistic Safety-Assured Adaptive Merging Control for Autonomous Vehicles

Autonomous vehicles face tremendous challenges while interacting with hu...

Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception

Data-driven models (DDM) based on machine learning and other AI techniqu...

From Hazard Analysis to Hazard Mitigation Planning: The Automated Driving Case

Vehicle safety depends on (a) the range of identified hazards and (b) th...