论文标题
对自动驾驶汽车的强大决策进行建模知觉错误
Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles
论文作者
论文摘要
传感和感知（S＆P）是自主系统（例如机器人）的关键组成部分，尤其是当部署在高度动态的环境中，需要对意外情况做出反应。在公共道路上行驶的自动驾驶汽车（AVS）的情况下，尤其如此。但是，当前的感知算法评估指标通常旨在衡量其准确性本身，并且不解释其对决策子系统的影响。此限制不能帮助开发商和第三方评估人员回答一个关键问题：感知子系统的表现是否足以使决策子系统做出坚固，安全的决策？在本文中，我们提出了一种基于模拟的方法来回答这个问题。同时，我们展示了如何分析不同种类的感应和感知错误对自主系统行为的影响。
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.
