Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach

Sheehan, Barry; Murphy, Finbarr; Ryan, Cian; Mullins, Martin; Liu, Hai Yue

doi:10.1016/j.trc.2017.06.015

Cited by 44 publications

(37 citation statements)

References 35 publications

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“…(Lemaire, Park, & Wang, ). Ultimately, traditional actuarial approaches and risk factors would result in drivers of SAVs (lower risk exposure) subsidizing the insurance costs of manual vehicle drivers (higher risk drivers) (Sheehan et al., ).…”

Section: Emerging Technology Risksmentioning

confidence: 99%

“…(Bengler et al., ). In‐vehicle sensors govern the vehicle state and include accelerometer, gyroscope, odometer, steering, and more (Sheehan et al., ). Safe autonomous driving will undoubtedly rely heavily on the accuracy and reliability of vehicle sensors.…”

Section: Emerging Technology Risksmentioning

confidence: 99%

“…Future road accidents will hinge on technological vulnerabilities rather than human predispositions. In particular, the evolving risk landscape is expected to significantly disrupt the insurance industry as increased vehicle safety ultimately translates into lower claims losses, premiums, and a shrinking motor insurance marketplace (Sheehan, Murphy, Ryan, Mullins, & Liu, ). However, risk will not be eradicated and the onus of liability will shift toward original equipment manufacturers (OEMs) or affiliated suppliers (Gurney, ), particularly as drivers can justifiably disengage while an artificial agent takes control.…”

Section: Introductionmentioning

confidence: 99%

“…The ensuing risk and liability landscape will present an interdisciplinary challenge, demanding closer collaboration between OEMs and insurers in order to retain, mitigate, and/or transfer risks. Table demonstrates the levels of vehicle automation and the projected liability shift to OEMs (SAE International, ; Sheehan et al., ). Demand for product liability, product recall, and cyber insurance policies is expected to increase, corresponding to the progressive transfer of control from human to artificial agent (Murphy et al., ).…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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Section: Emerging Technology Risksmentioning

confidence: 99%

Section: Emerging Technology Risksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2018

Self Cite

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“…As such, semi-autonomous vehicles will become the dominating paradigm in the coming years, and traffic accidents from the manual driving of these vehicles will persist as a key insurance issue (Albright et al, 2016). As semi-autonomous vehicles becoming increasingly common, customers will expect a shift from the traditional model-based approaches of accident prediction and 'rough proxies' of exposure (Sheehan et al, 2017), to a crowd-sourcing approach using the real-time collection and analysis of data from these vehicles. Semi-autonomous vehicles are enabled by a wealth of technology capabilities, including network connectivity, high precision sensor data from the vehicle's Controller Area Network (CAN) Bus, GPS, high definition cameras, and LIDAR systems (Mannering and Washburn, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

Ryder

Dahlinger

Gahr

et al. 2019

Transportation Research Part A: Policy and Practice

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Despite the fact that semi-autonomous vehicles will become more and more prevalent in the coming decades, recent studies have highlighted that traffic accidents will persist as a core issue for road users, insurers, and policy makers alike. Researchers and industry players see potential in the technology embedded in semi-autonomous vehicles to combat this challenge by reliably predicting locations with a high likelihood of traffic accidents. This technology can be leveraged to detect accidents and 'near miss incidents', such as heavy braking and evasive manoeuvres, otherwise known as Critical Driving Events (CDEs). The locations of CDEs could identify areas of high accident exposure, offering automotive insurers a unique opportunity to reduce traffic accidents through the adoption of active loss prevention business models, such as providing saferouting services and in-vehicle warnings. To date, there is limited empirical evidence on whether the Crash Frequency and Crash Rate of locations can be accurately identified through CDEs. To address this research gap, an 18-week naturalistic driving field study of 72 vehicles was conducted in Switzerland, covering over 690,000 km. Data collected from the CAN Bus of these vehicles indicate that there is a proportional relationship between the CDEs of the fleet, and the Crash Frequency and Crash Rate of a location. Furthermore, a nationwide spatial regression analysis was applied to determine Crash Frequency across the majority of the Swiss road network. We identify the relationship between Crash Frequency, and the CDEs and Trip Frequency of the fleet, along with additional explanatory variables for urban and highway locations. These insights provide first evidence that insurance companies and other industry players with access to a nationwide semi-autonomous fleet can determine existing and emerging locations of high accident probability, enabling more proactive business models and safety focused services.

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The Contribution of Usage-Based Data Analytics to Benchmark Semi-autonomous Vehicle Insurance

Guillén

Pérez-Marín

2018

Mathematical and Statistical Methods for Actuarial Sciences and Finance

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Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach

Cited by 44 publications

References 35 publications

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

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

Spatial prediction of traffic accidents with critical driving events – Insights from a nationwide field study

The Contribution of Usage-Based Data Analytics to Benchmark Semi-autonomous Vehicle Insurance

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