Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles

Abstract

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.

11 pages, 8 figures. Preprint of an article published at IJCAI2020 update: fixed title and metadata

Cited by in corpus (5)

• A Review of Testing Object-Based Environment Perception for Safe Automated Driving
• Vehicle-to-Everything Cooperative Perception for Autonomous Driving
• ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles
• PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles
• EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners