Abstract
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.
Abstract (translated)
智能交叉口管理器可以通过检测自动驾驶车辆中的危险驾驶员或失效模式,提高安全性,并在车辆接近交叉口时发出警告。在本研究中,我们介绍了失败网络(FailureNet),该网络通过对小型城市中的虚构和不负责任的驾驶员的行驶轨迹进行端到端的训练。失败网络在车辆接近交叉口时观察其姿态,并检测自动驾驶栈是否存在故障,同时警告交叉交通中的潜在的危险驾驶员。失败网络可以准确地识别控制故障、前向感知错误以及加速驾驶员,并将他们与虚构驾驶区分开来。该网络在小型城市中与自动驾驶车辆一起训练和部署。与基于速度或频率的预测相比,失败网络的循环神经网络结构提供了改进的预测能力,在硬件部署时可以达到84%的准确率。
URL
https://arxiv.org/abs/2303.12224
