We present a method to integrate real-time out-of-distribution (OOD) detection for neural network trajectory predictors, and to adapt the control strategy of a robot (e.g., a self-driving car or drone) to preserve safety while operating in OOD regimes. Specifically, we use a neural network ensemble to predict the trajectory for a dynamic obstacle (such as a pedestrian), and use the maximum singular value of the empirical covariance among the ensemble as a signal for OOD detection. We calibrate this signal with a small fraction of held-out training data using the methodology of conformal prediction, to derive an OOD detector with probabilistic guarantees on the false-positive rate of the detector, given a user-specified confidence level. During in-distribution operation, we use an MPC controller to avoid collisions with the obstacle based on the trajectory predicted by the neural network ensemble. When OOD conditions are detected, we switch to a reachability-based controller to guarantee safety under the worst-case actions of the obstacle. We verify our method in extensive autonomous driving simulations in a pedestrian crossing scenario, showing that our OOD detector obtains the desired accuracy rate within a theoretically-predicted range. We also demonstrate the effectiveness of our method with real pedestrian data. We show improved safety and less conservatism in comparison with two state-of-the-art methods that also use conformal prediction, but without OOD adaptation.

翻译：本文提出一种集成实时外分布（OOD）检测的神经网络轨迹预测方法，并据此调整机器人（如自动驾驶汽车或无人机）的控制策略，以保障其在OOD状态下的运行安全。具体而言，我们采用神经网络集成模型预测动态障碍物（如行人）的轨迹，并将集成模型经验协方差矩阵的最大奇异值作为OOD检测信号。利用保形预测方法，我们通过少量预留训练数据对该信号进行校准，从而构建具有概率保证的OOD检测器——在用户指定置信水平下可控制检测器的误报率。在分布内运行时，我们基于神经网络集成预测的轨迹，采用模型预测控制（MPC）策略规避与障碍物的碰撞。当检测到OOD状态时，系统切换至基于可达性分析的控制器，以保证障碍物采取最不利动作时的安全性。我们在行人横穿场景的自动驾驶仿真中进行了广泛验证，结果表明：所提出的OOD检测器在理论预测范围内达到了预期准确率。通过真实行人数据的实验进一步证明了本方法的有效性。与两种同样采用保形预测但未进行OOD自适应的前沿方法相比，本方法在提升安全性的同时降低了控制保守性。