This paper proposes a methodology to estimate uncertainties in automated vehicle (AV) dynamics in real time via Bayesian inference. Based on the estimated uncertainty, the method aims to track the car-following (CF) performance of the AV to support strategic actions to maintain desired performance. Our methodology consists of three sequential components: (i) the Stochastic Gradient Langevin Dynamics (SGLD) is adopted to estimate parameter uncertainty relative to vehicular dynamics in real time, (ii) dynamic monitoring of car-following stability (local and string-wise), and (iii) strategic actions for control adjustment if anomaly is detected. The proposed methodology provides means to gauge AV car-following performance in real time and preserve desired performance against real time uncertainty that are unaccounted for in the vehicle control algorithm.

中文翻译：

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处理自动驾驶汽车跟车行为中性能不确定性的战略方法

本文提出了一种通过贝叶斯推理实时估计自动车辆（AV）动力学不确定性的方法。基于估计的不确定性，该方法旨在跟踪自动驾驶汽车的跟车 (CF) 性能，以支持战略行动以保持所需的性能。我们的方法由三个连续的组成部分组成：(i) 采用随机梯度朗之万动力学 (SGLD) 来实时估计与车辆动力学相关的参数不确定性，(ii) 动态监测跟车稳定性（局部和串方向） ，以及 (iii) 如果检测到异常，则采取控制调整的战略行动。所提出的方法提供了实时测量自动驾驶汽车跟车性能的方法，并针对车辆控制算法中未考虑到的实时不确定性保持所需的性能。