Intelligent intersection managers can improve safety by detecting dangerous drivers or failure modes in autonomous vehicles, warning oncoming vehicles as they approach an intersection. In this work, we present FailureNet, a recurrent neural network trained end-to-end on trajectories of both nominal and reckless drivers in a scaled miniature city. FailureNet observes the poses of vehicles as they approach an intersection and detects whether a failure is present in the autonomy stack, warning cross-traffic of potentially dangerous drivers. FailureNet can accurately identify control failures, upstream perception errors, and speeding drivers, distinguishing them from nominal driving. The network is trained and deployed with autonomous vehicles in the MiniCity. Compared to speed or frequency-based predictors, FailureNet's recurrent neural network structure provides improved predictive power, yielding upwards of 84% accuracy when deployed on hardware.

翻译：智能交叉口管理器可通过检测危险驾驶员或自动驾驶车辆中的故障模式，在车辆接近交叉口时向迎面而来的车辆发出预警，从而提高安全性。本研究提出FailureNet——一种在缩尺微型城市中基于正常与鲁莽驾驶员的轨迹进行端到端训练的循环神经网络。FailureNet通过观测车辆接近交叉口时的位姿，检测自主驾驶堆栈中是否存在故障，并向横向来车预警潜在危险驾驶员。该网络能够准确识别控制故障、上游感知错误及超速驾驶行为，将其与正常驾驶区分开来。我们在MiniCity中利用自动驾驶车辆完成了网络的训练与部署。相较于基于速度或频率的预测模型，FailureNet的循环神经网络结构具有更强的预测能力，在硬件部署时准确率可达84%以上。