Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles
This addresses a critical safety gap for autonomous vehicle developers and evaluators by linking perception errors to decision-making robustness, though it is incremental as it builds on existing simulation approaches.
The paper tackles the problem that current perception algorithm metrics do not assess their impact on decision-making in autonomous vehicles, proposing a simulation-based methodology to evaluate if perception performance enables robust, safe decisions and analyzing the effects of various sensing and perception errors.
Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react to unexpected situations. This is particularly true in case of Autonomous Vehicles (AVs) driving on public roads. However, the current evaluation metrics for perception algorithms are typically designed to measure their accuracy per se and do not account for their impact on the decision making subsystem(s). This limitation does not help developers and third party evaluators to answer a critical question: is the performance of a perception subsystem sufficient for the decision making subsystem to make robust, safe decisions? In this paper, we propose a simulation-based methodology towards answering this question. At the same time, we show how to analyze the impact of different kinds of sensing and perception errors on the behavior of the autonomous system.