Autonomous vehicles rely heavily upon their perception subsystems to see the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, development of robust autonomous vehicle subsystems requires repeatable, controlled testing - while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real-world being developed. In the context of the complete autonomous vehicle control pipeline, subsystems downstream of perception need to be tested with accurate recreations of the perception system output, rather than focusing on subjective visual realism of the input - whether in simulation or the real world. This study develops the untapped potential of a lightweight weather augmentation method in an autonomous racing vehicle - focusing not on visual accuracy, but rather the effect upon perception subsystem performance in real time. With minimal adjustment, the prototype developed in this study can replicate the effects of water droplets on the camera lens, and fading light conditions. This approach introduces a latency of less than 8 ms using compute hardware well suited to being carried in the vehicle - rendering it ideal for real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.
翻译:不幸的是,天气条件的变化对物体检测算法提出了重大挑战,因此,必须在可能经历的所有条件下对车辆进行广泛测试。然而,开发稳健的自主车辆子系统需要反复进行有控制的测试,而真正的天气是无法预测的,无法安排的。在不利条件下进行现实世界测试是一项昂贵和耗时的任务,往往需要进入专家设施。模拟通常是一种替代,对现实世界的描述越来越现实。在完全自主的车辆控制管道中,下游的次系统需要用准确的视觉模拟系统产出来测试,而不是侧重于投入的主观直观现实主义----无论是模拟还是现实世界。这项研究开发了在自主的赛车中轻量的天气增强方法的未开发潜力,不注重视觉准确性,而是实时对感知子绩效的影响。在进行最小的调整后,本研究中开发的原型可以复制水滴对摄影镜头的效应,在实际测试中,在快速的模拟条件下,在快速的汽车测试中,可以使实际操作更不适应。