一句话：提出面向 ADAS 多传感器融合错误的演化式领域特定搜索框架 FusED，并结合因果分析定位成因。
高级驾驶辅助系统依赖多传感器融合（MSF）整合传感器输出以降低不确定性，但 MSF 无法完全消除不确定性，可能误导车辆控制并造成严重安全风险。作者将此类由 MSF 缺陷导致的故障（如碰撞）定义为 fusion error，并提出新型演化式领域特定搜索框架 FusED，用于高效检测工业级 ADAS 中的融合错误。文中还利用因果分析验证所发现的融合错误确实由 MSF 方法导致。作者在两种驾驶环境、两种常用 MSF 方法上评估 FusED，结果检测出 150 多个融合错误，并据此给出改进相应 MSF 方法的建议。
Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the uncertainties brought by sensor outputs, they usually leverage multi-sensor fusion (MSF) to fuse the sensor outputs and produce a more reliable understanding of the surroundings. However, MSF cannot completely eliminate the uncertainties since it lacks the knowledge about which sensor provides the most accurate data and how to optimally integrate the data provided by the sensors. As a result, critical consequences might happen unexpectedly. In this work, we observed that the popular MSF methods in an industry-grade ADAS can mislead the car control and result in serious safety hazards. We define the failures (e.g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors. We further apply causality analysis to show that the found fusion errors are indeed caused by the MSF method. We evaluate our framework on two widely used MSF methods in two driving environments. Experimental results show that FusED identifies more than 150 fusion errors. Finally, we provide several suggestions to improve the MSF methods we study.
DOI 原文 ·
@inproceedings{paperbot7392,
title = {Detecting multi-sensor fusion errors in advanced driver-assistance systems},
author = {Ziyuan Zhong and Zhisheng Hu and Shengjian Guo and Xinyang Zhang and Zhenyu Zhong and Baishakhi Ray},
booktitle = {ISSTA},
year = {2022},
doi = {10.1145/3533767.3534223}
}