Graph Neural Network Based Relation Learning for Abnormal Perception Information Detection in Self-Driving Scenarios

Engineering notes

Key topics: self-driving, autonomous vehicle, object detection, carla, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Robustness and safety concerns of perception systems are of great importance for autonomous vehicle navigation applications. Recent researches demonstrate that the surrounding dynamic object detection results of current perception systems can be easily interfered or attacked to mislead the navigation performance of the victim vehicle. In this paper, we develop a GNN based relation learning network to detect the abnormal information in the vehicle perception results, by investigating the relations among the surrounding dynamic objects and also the overall scenario information. Our underlying logic is that the motion of each surrounding object is also affected by its neighbors as well as the whole traffic scenario information, so there should exist a certain amount of consistency among those agents. Learning their spatiotemporal relations provides critical information for detecting the abnormal perception information. Experimental results on the standard CARLA simulator demonstrate our effectiveness in various scenarios and scalability to unseen cases.

Links and sources

• Official / arXiv page

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