This work proposes to use passive acoustic perception as an additional sensing modality for intelligent vehicles. We demonstrate that approaching vehicles behind blind corners can be detected by sound before such vehicles enter in line-of-sight. We have equipped a research vehicle with a roof-mounted microphone array, and show on data collected with this sensor setup that wall reflections provide information on the presence and direction of occluded approaching vehicles. A novel method is presented to classify if and from what direction a vehicle is approaching before it is visible, using as input Direction-of-Arrival features that can be efficiently computed from the streaming microphone array data. Since the local geometry around the ego-vehicle affects the perceived patterns, we systematically study several environment types, and investigate generalization across these environments. With a static ego-vehicle, an accuracy of 0.92 is achieved on the hidden vehicle classification task. Compared to a state-of-the-art visual detector, Faster R-CNN, our pipeline achieves the same accuracy more than one second ahead, providing crucial reaction time for the situations we study. While the ego-vehicle is driving, we demonstrate positive results on acoustic detection, still achieving an accuracy of 0.84 within one environment type. We further study failure cases across environments to identify future research directions.
翻译:这项工作提议使用被动声学感知作为智能车辆的一种额外感测方式。我们证明,在这类车辆进入视线前,可以通过声波探测到接近盲角后的车辆。我们装备了一台带有屋顶的麦克风阵列的研究车,并用这个传感器设置所收集的数据显示,墙反射提供了隐蔽接近车辆的存在和方向的信息。提出了一种新颖的方法,对车辆在可见之前行进方向进行分类,使用输入式麦克风阵列数据可有效计算到的抵达方向特征。由于自我飞行器周围的本地几何学影响所觉察到的模式,我们有系统地研究几种环境类型,并调查这些环境的全局性。使用静态自我飞行器,在隐藏车辆分类任务上实现了0.92的准确度。与最先进的视觉探测器相比,更快的R-CNN,我们的输油管在前面超过1秒的准确度相同,为我们研究的情况提供了关键的反应时间。在自我飞行器驱动下,我们系统地研究了若干环境类型,我们仍然在进行声学探测时发现一个未来方向。