Environmental disturbances, such as sensor data noises, various lighting conditions, challenging weathers and external adversarial perturbations, are inevitable in real self-driving applications. Existing researches and testings have shown that they can severely influence the vehicles perception ability and performance, one of the main issue is the false positive detection, i.e., the ghost object which is not real existed or occurs in the wrong position (such as a non-existent vehicle). Traditional navigation methods tend to avoid every detected objects for safety, however, avoiding a ghost object may lead the vehicle into a even more dangerous situation, such as a sudden break on the highway. Considering the various disturbance types, it is difficult to address this issue at the perceptual aspect. A potential solution is to detect the ghost through relation learning among the whole scenario and develop an integrated end-to-end navigation system. Our underlying logic is that the behavior of all vehicles in the scene is influenced by their neighbors, and normal vehicles behave in a logical way, while ghost vehicles do not. By learning the spatio-temporal relation among surrounding vehicles, an information reliability representation is learned for each detected vehicle and then a robot navigation network is developed. In contrast to existing works, we encourage the network to learn how to represent the reliability and how to aggregate all the information with uncertainties by itself, thus increasing the efficiency and generalizability. To the best of the authors knowledge, this paper provides the first work on using graph relation learning to achieve end-to-end robust navigation in the presence of ghost vehicles. Simulation results in the CARLA platform demonstrate the feasibility and effectiveness of the proposed method in various scenarios.

翻译：在真实自动驾驶应用中，环境干扰（如传感器数据噪声、多变光照条件、恶劣天气及外部对抗性扰动）不可避免。现有研究与测试表明，这些干扰会严重影响车辆的感知能力与性能，其中核心问题之一是误检（即幻影物体）——这些物体实际不存在或出现在错误位置（如不存在的车辆）。传统导航方法倾向于避让所有检测到的物体以确保安全，然而避让幻影物体可能导致车辆陷入更危险的境地（例如在高速公路上急刹）。考虑到干扰类型的多样性，在感知层面解决此问题较为困难。一种潜在解决方案是通过全场景关系学习识别幻影，并构建集成化的端到端导航系统。我们的核心逻辑是：场景中所有车辆的行为均受其邻近车辆影响，正常车辆的行为符合逻辑规律，而幻影车辆则不然。通过学习周围车辆的时空关系，我们为每个检测到的车辆学习信息可靠性表征，进而构建机器人导航网络。与现有工作不同，我们引导网络自主学习如何表征可靠性及如何聚合所有含不确定性的信息，从而提升效率与泛化能力。据作者所知，本文首次提出利用图关系学习实现存在幻影车辆情况下的端到端鲁棒导航。在CARLA平台上的仿真实验结果验证了所提方法在多场景下的可行性与有效性。