Autonomous driving (AD) systems have been thriving in recent years. In general, they receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the uncertainties brought by sensor inputs, AD systems usually leverage multi-sensor fusion (MSF) to fuse the sensor inputs 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. As a result, critical consequences might happen unexpectedly. In this work, we observed that the popular MSF methods in an industry-grade Advanced Driver-Assistance System (ADAS) can mislead the car control and result in serious safety hazards. Misbehavior can happen regardless of the used fusion methods and the accurate data from at least one sensor. To attribute the safety hazards to a MSF method, we formally define the fusion errors and propose a way to distinguish safety violations causally induced by such errors. Further, we develop a novel evolutionary-based domain-specific search framework, FusionFuzz, for the efficient detection of fusion errors. We evaluate our framework on two widely used MSF methods. %in two driving environments. Experimental results show that FusionFuzz identifies more than 150 fusion errors. Finally, we provide several suggestions to improve the MSF methods under study.
翻译:近年来,自主驾驶(AD)系统一直蓬勃发展。一般来说,它们接收了传感器数据,计算驾驶决定和车辆的输出控制信号。为了消除传感器输入带来的不确定性,AD系统通常会利用多传感器聚合(MSF)来连接传感器输入,并产生对周围环境的更可靠的了解。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的知识。结果,关键后果可能会意外发生。在这项工作中,我们发现工业级高级驾驶辅助系统(ADAS)中流行的MSF方法可以误导汽车控制,导致严重的安全危险。不管使用何种组合方法和至少一个传感器的准确数据,Misbehavior都会发生。为了将安全危险归结为MSF方法,我们正式定义了聚合错误错误的错误,并提出了一种区分因此类错误而导致的违反安全行为的方法。此外,我们开发了一个新型的基于进化的特定域搜索框架,即FusionFuzz(Fususuzz),以有效检测聚合错误。我们评估了我们关于FILOFLOF LOF LOF LOF FOF LOF LOF FIES提供的两种最新方法,我们发现两种方法。