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

All developed economies mandate at least third party auto insurance resulting inW a vast global liability industry. The evolution towards semi-autonomous and eventually driverless vehicles will progressively remove the leading cause of vehicle accidents, human error, and significantly lower vehicle accident rates. However, this transition will force a departure from existing actuarial methods requires careful management to ensure risks are correctly assigned. Personal motor insurance lines are anticipated to diminish as liability shifts towards original equipment manufacturers (OEMs), tier 1 and 2 suppliers and software developers. Vehicle accident risks will hinge on vehicular characteristics in addition to driver related risks as drivers alternate between autonomous and manual driving modes. This paper proposes a Bayesian Network statistical risk estimation approach that can accommodate changing risk levels and the emergence of new risk structures. The authors demonstrate the use of this method for a Level 3 semi-autonomous vehicle for two scenarios, one where the driver is in control and one where the vehicle is in control. This approach is especially suited to use telematics data generated from the vehicle inherent technologies. The authors validate the efficacy of this approach from the perspective of the insurer and discuss how vehicle technology development will require a greater degree of collaboration between the insurance company and the manufacturers in order to develop a greater understanding of the risks semi-autonomous and fully autonomous vehicles.

• Record URL:
  https://doi.org/10.1016/j.trc.2017.06.015
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X17301687
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Sheehan, Barry
  • Murphy, Finbarr
  • Ryan, Cian
  • Mullins, Martin
  • Liu, Hai Yue
• Publication Date: 2017-9

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 124-137
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 82
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Automobile insurance; Bayes' theorem; Cooperation; Industries; Insurance industry; Intelligent vehicles; Liability insurance; Risk; Telematics
• Uncontrolled Terms: Risk transfer
• Subject Areas: Administration and Management; Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01644494
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 28 2017 3:57PM