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.
Responsibility-Sensitive Safety: an Introduction with an Eye to Logical Foundations and Formalization
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abstract
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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Incorporating System-level Safety Requirements in Perception Models via Reinforcement Learning
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.