Diagnosis and Prognosis of Chassis Systems in Autonomous Driving Conditions
Diagnosis and Prognosis of Chassis Systems in Autonomous Driving Conditions
复制标题
自动驾驶条件下底盘系统的诊断与预测
DOI：
10.4271/2023-01-0741
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Jongsoo Lee
中科院分区：
文献类型：
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作者：
Kyung;D. Sung;Yong Ha Han;Yeongmin Yoo;Jongsoo Lee
随着专用汽车（PBV）、城市空中交通（UAM）和机器人出租车等各种未来交通工具的扩展，自动驾驶系统（ADS）技术的应用也在不断扩大。ADS的主要目的是通过监控车辆异常来确保安全，以防止功能故障或事故。在这项研究中，一个基于模型的诊断和预测过程中建立了使用自动驾驶模拟过程中产生的退化数据。利用Modelica/Dymola设计了车辆模型，并利用Matlab/Simulink将车道保持辅助系统（LKA）与车辆模型集成，进行了自动驾驶仿真。底盘系统的3个部件（减震器阻尼器、悬架衬套和轮胎）的退化数据被输入到集成仿真模型中。采用K-最近邻模型（K-NN）和高斯混合模型（GMM）对降解行为进行了监测。使用高斯过程估计每个组件的剩余使用寿命（RUL）。设计了一种正常/异常数据分类器对自主车辆仿真模型进行诊断，并在95%的预测区间内估计了RUL。
Expanding various future mobilities such as purpose built vehicle (PBV), urban air mobility (UAM), and robo-taxi, the application of autonomous driving system (ADS) technology is also spreading. The main point of ADS is to ensure safety by monitoring vehicle anomalies to prevent functional failure or accident. In this study, a model-based diagnosis and prognosis process was established using degradation data generated during autonomous driving simulation. A vehicle model was designed using Modelica/Dymola, and autonomous driving simulation was performed by integrating the lane keeping assistant (LKA) system with the vehicle model using Matlab/Simulink. Degradation data for the 3 components (a shock absorber damper, a suspension bush, and a tire) of the chassis system were input into the integrated simulation model. The degradation behavior was monitored with K-nearest neighbor (K-NN) and Gaussian mixture model (GMM). The remaining useful life (RUL) for each component was estimated using a Gaussian process. As a result, a normal/abnormal data classifier was designed to diagnose the autonomous vehicle simulation model, and the RUL was estimated within the 95% prediction interval.