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:环境扰动,如传感器数据噪音、各种照明条件、具有挑战性的天气和外部对抗性扰动等,在真正的自我驱动应用中是不可避免的。现有的研究和测试表明,它们能够严重影响车辆的感知能力和性能,主要问题之一是虚假的正面探测,即不存在或发生于错误位置的幽灵物体(如不存在的车辆),传统导航方法往往避免每一个被检测到的物体的安全,但是,避免幽灵物体可能使车辆陷入更危险的局面,如高速公路突然破裂。考虑到各种扰动类型,很难在视觉方面解决这一问题。一个潜在的解决办法是通过在整个情景中学习关系来探测幽灵,并开发一个综合端对端导航系统。我们的基本逻辑是,现场所有车辆的行为都受到邻居的影响,正常车辆的行为是合乎逻辑的,而幽灵车辆则没有这样做。首先通过了解周围车辆间的各种时空关系,信息可靠性说明如何在视觉方面解决这一问题。通过在整个情景中学习每个被检测到的车辆的准确性,从而鼓励整个导航网络的可靠性,从而使得整个飞行器的可靠性。我们通过学习整个轨道的准确性,从而了解现有的精确性。