论文标题
自动化车辆跟随汽车的不确定性中的贝叶斯方法：启用战略决策
Bayesian Methods in Automated Vehicle's Car-following Uncertainties: Enabling Strategic Decision Making
论文作者
论文摘要
本文提出了一种通过贝叶斯推论实时估算自动化车辆（AV）动力学的不确定性的方法。基于估计的不确定性，该方法旨在不断监视AV的汽车跟踪（CF）性能，以支持战略行动以保持所需的性能。我们的方法由三个顺序组成组成：（i）随机梯度Langevin动力学（SGLD）用于估计参数不确定性相对于实时的车辆动力学，（ii）对CAR-sollow稳定性（本地和弦线）的动态监测（局部和（III）的策略性动作，用于控制ANOMALALY IF ANOMALIALE的策略性动作。拟议的方法提供了实时评估AV跟踪性能的手段，并保留在车辆控制算法中未划分的实时不确定性的期望性能。
This paper proposes a methodology to estimate uncertainty in automated vehicle (AV) dynamics in real time via Bayesian inference. Based on the estimated uncertainty, the method aims to continuously monitor the car-following (CF) performance of the AV to support strategic actions to maintain a 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.
