Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach. Issue 5 (25th October 2018)

• Record Type: Journal Article
• Authors: Ryan, Cian; Murphy, Finbarr; Mullins, Martin
• Is Part Of: Risk analysis. Volume 39:Issue 5(2019)
• Page Start: 1125
• Page End: 1140
• Publication Date: 2018-10-25
• Subjects: Anomaly detection -- autonomous vehicles -- emerging technology risks -- insurance; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.13217
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: 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.