Self-Driving Cars: Formalization and Verification Of The Responsibility-Sensitive Safety (RSS) Model

A technical paper titled “Slow Down, Move Over: A Case Study in Formal Verification, Refinement, and Testing of the Responsibility-Sensitive Safety Model for Self-Driving Cars” was published by researchers at Carnegie Mellon University.

Abstract:

“Technology advances give us the hope of driving without human error, reducing vehicle emissions and simplifying an everyday task with the future of self-driving cars. Making sure these vehicles are safe is very important to the continuation of this field. In this paper, we formalize the Responsibility-Sensitive Safety model (RSS) for self-driving cars and prove the safety and optimality of this model in the longitudinal direction. We utilize the hybrid systems theorem prover KeYmaera X to formalize RSS as a hybrid system with its nondeterministic control choices and continuous motion model, and prove absence of collisions. We then illustrate the practicality of RSS through refinement proofs that turn the verified nondeterministic control envelopes into deterministic ones and further verified compilation to Python. The refinement and compilation are safety-preserving; as a result, safety proofs of the formal model transfer to the compiled code, while counterexamples discovered in testing the code of an unverified model transfer back. The resulting Python code allows to test the behavior of cars following the motion model of RSS in simulation, to measure agreement between the model and simulation with monitors that are derived from the formal model, and to report counterexamples from simulation back to the formal model.”

Find the technical paper here. Published: May 2023 (preprint)

Strauss, Megan, and Stefan Mitsch. “Slow Down, Move Over: A Case Study in Formal Verification, Refinement, and Testing of the Responsibility-Sensitive Safety Model for Self-Driving Cars.” arXiv preprint arXiv:2305.08812 (2023).

Source: https://semiengineering.com/self-driving-cars-formalization-and-verification-of-the-responsibility-sensitive-safety-rss-model/

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