Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles

Andrea Piazzoni, Jim Cherian, Martin Slavik, Justin Dauwels
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
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
more » ... ation 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.
doi:10.24963/ijcai.2020/479 dblp:conf/ijcai/TuM020 fatcat:dwov2yxchnevtobgzmcuzmyzne