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Semiautonomous Vehicle Risk Analysis: A Telematics-Based Anomaly Detection Approach

机译:半自动车辆风险分析:基于远程信息处理的异常检测方法

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摘要

The transition to semiautonomous driving is set to considerably reduce road accident rates as human error is progressively removed from the driving task. Concurrently, autonomous capabilities will transform the transportation risk landscape and significantly disrupt the insurance industry. Semiautonomous vehicle (SAV) risks will begin to alternate between human error and technological susceptibilities. The evolving risk landscape will force a departure from traditional risk assessment approaches that rely on historical data to quantify insurable risks. This article investigates the risk structure of SAVs and employs a telematics-based anomaly detection model to assess split risk profiles. An unsupervised multivariate Gaussian (MVG) based anomaly detection method is used to identify abnormal driving patterns based on accelerometer and GPS sensors of manually driven vehicles. Parameters are inferred for vehicles equipped with semiautonomous capabilities and the resulting split risk profile is determined. The MVG approach allows for the quantification of vehicle risks by the relative frequency and severity of observed anomalies and a location-based risk analysis is performed for a more comprehensive assessment. This approach contributes to the challenge of quantifying SAV risks and the methods employed here can be applied to evolving data sources pertinent to SAVs. Utilizing the vast amounts of sensor-generated data will enable insurers to proactively reassess the collective performances of both the artificial driving agent and human driver.
机译:随着人为错误逐渐从驾驶任务中消除,向半自动驾驶的过渡将大大降低道路事故发生率。同时,自主能力将改变运输风险格局,并极大地破坏保险业。半自动驾驶汽车(SAV)的风险将开始在人为错误和技术敏感性之间交替。不断变化的风险形势将迫使传统的风险评估方法脱离历史,而传统的风险评估方法依靠历史数据来量化可保风险。本文研究了SAV的风险结构,并采用了基于远程信息处理的异常检测模型来评估拆分的风险状况。一种基于无监督多元高斯(MVG)的异常检测方法,用于基于手动驾驶车辆的加速度计和GPS传感器来识别异常驾驶模式。推断配备半自动功能的车辆的参数,并确定由此产生的分裂风险状况。 MVG方法允许通过观察到的异常的相对频率和严重性来量化车辆风险,并执行基于位置的风险分析以进行更全面的评估。这种方法加剧了量化SAV风险的挑战,此处采用的方法可以应用于与SAV相关的不断发展的数据源。利用大量传感器生成的数据将使保险公司能够主动重新评估人工驾驶代理和驾驶员的集体绩效。

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