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.

本研究在小型城市的真实场景中对正常驾驶和危险驾驶进行了轨迹记录，通过构建一种循环神经网络FailureNet来检测自动驾驶系统中的故障，为交叉道路的驾驶提供提醒。与速度或基于频率的预测器相比，FailureNet的循环神经网络结构提供了更好的预测能力，在硬件上部署时可以达到84％的准确度。

基于基础设施的端到端学习及司机失效预防