Detecting multi-sensor fusion errors in advanced driver-assistance systems
Detecting multi-sensor fusion errors in advanced driver-assistance systems
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DOI：
10.1145/3533767.3534223
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Ziyuan Zhong;Zhisheng Hu;Shengjian Guo;Xinyang Zhang;Zhenyu Zhong;Baishakhi Ray
中科院分区：
文献类型：
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作者：
Ziyuan Zhong;Zhisheng Hu;Shengjian Guo;Xinyang Zhang;Zhenyu Zhong;Baishakhi Ray
近年来，高级驾驶辅助系统（ADAS）蓬勃发展并得到广泛部署。通常，这些系统接收传感器数据，计算驾驶决策，并向车辆输出控制信号。为了消除传感器输出带来的不确定性，他们通常利用多传感器融合（MSF）来融合传感器输出，并对周围环境产生更可靠的理解。然而，MSF无法完全消除不确定性，因为它缺乏关于哪个传感器提供最准确数据以及如何最佳地整合传感器提供的数据的知识。因此，严重后果可能会出乎意料地发生。在这项工作中，我们观察到工业级ADAS中流行的MSF方法可能会误导汽车控制并导致严重的安全隐患。我们定义故障（例如，汽车碰撞）所造成的故障MSF融合错误，并开发一种新的基于进化的领域特定的搜索框架，融合，融合错误的有效检测。我们进一步应用因果关系分析表明，所发现的融合错误确实是由MSF方法引起的。我们评估我们的框架上两个广泛使用的MSF方法在两个驾驶环境。实验结果表明，FusED识别超过150个融合错误。最后，我们提出了几个建议，以改善我们研究的MSF方法。
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.