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Actuarial Insights on the Risks of Tomorrow – Autonomous Vehicles

Actuarial Insights on the Risks of Tomorrow – Autonomous Vehicles. Actuarial Science Club University of Texas March 7, 2017 Rick Gorvett, FCAS, CERA, MAAA, ARM, FRM, PhD Staff Actuary Casualty Actuarial Society. Agenda. Background Issues Opportunities. 1964 Worlds Fair

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Actuarial Insights on the Risks of Tomorrow – Autonomous Vehicles

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  1. Actuarial Insights on the Risks of Tomorrow – Autonomous Vehicles Actuarial Science Club University of Texas March 7, 2017 Rick Gorvett, FCAS, CERA, MAAA, ARM, FRM, PhD Staff Actuary Casualty Actuarial Society

  2. Agenda • Background • Issues • Opportunities

  3. 1964 Worlds Fair General Motors Futurama 2015 Mercedes Concept Car Google Self-Driving Car/Taxi

  4. Historic Development • 2014 • MI passes law • NHTSA passes V2V • Google developing driverless car • without steering wheel or brakes • 2013 • Google surpasses 500K miles • Oxford creates a $7,750 self-driving car • Britain tests on public roads • Mercedes tests on public roads • CMU tests on public roads • Audi receives autonomous car license • NHTSA issues policy on automated vehicles • DC passes autonomous car law • 2012 • Google surpasses 300K accident free miles • Nissan opens research facility in Silicon Valley • Google & Continental receive autonomous car licenses • FL & CA pass autonomous car laws • 2011 • Google surpasses 150K miles • BMW begins testing self driving car on public roads • NV passes autonomous car law • 2009 • Google begins testing on • public roads 2010 Volvo CitySafe standard 2007 CMU wins DARPA Urban Challenge 2005 Stanford wins DARPA Grand Challenge

  5. Even More Recently • The Good • Self-Driving Buses • Japan • Helsinki • Self-Driving Taxis • The Bad • Accidents • Google – caused accident • Tesla – fatal crash • The Ugly • Some AVs are not exactly stylish…

  6. Level of Vehicle Automation • Autonomous Vehicles (AV): Vehicles that are able to guide themselves from an origin point to a destination point desired by the individual • Varying levels of Automation (by NHTSA): Full Self-Driving Automation Limited Self-Driving Automation (e.g. drivers can cede safety-critical functions) Combined Function Automation (e.g. adaptive cruise control with lane centering) Function-Specific Automation (e.g. cruise control)

  7. Enabled by Connected Vehicles LIDAR: combination of light and radar, and uses laser light to create 3D images of the surrounding environment. Video Camera V2V/V2I uses Dedicated Short Range Communications (DSRC), similar to wifi Ultrasonic Sensor Computer RADAR

  8. Future development may create two models for AVs All driving, limited location Some driving, all locations • End to end service • Only operates in specified area • “Taxi” service • Google, Uber • Takes over some of the driving • E.g. Supercruise, parallel parking • Only operates in specified area • Driver owns and operates • Mercedes, BMW, Volvo, Cadillac, Telsa

  9. Societal Benefits of AV • Reduce accidents • Reduce transportation costs • Support demographic change • Promote the economy

  10. Agenda • Background • Issues • Opportunities

  11. CAS AVTF: Overview Goal The CAS AVTF is researching the technology’s risks to provide policymakers with the information needed to ensure the product is brought to market as safely and efficiently as possible. Focus Pre market Post market Post claim identify & quantify risks accurately price the technology compensate claimants fairly & efficiently Taskforce is actively pursing relevant studies and other opportunities

  12. Issues • Safety • Are these vehicles safe? • What should the safety standard be? • Liability • Who is liable in the event of an accident? • Regulation • What regulations should govern the testing and driving of an AV? • Privacy and Cyber Security • Who owns and is responsible for the data collected by AVs

  13. “93% of accidents are caused by human error.” NHTSA’s 2008 National Motor Vehicle Crash Causation Survey ≠ “Automated vehicles will reduce accidents by 93%”

  14. NMVCCS – Limiting Factors Behavioral (Driver) Issues Technology Issues 48.9% 32.4% 21.3% 16.7% 12.2% 11.6% 11.0% 3.1% 2.3% 2.9% 0.4% Driver Asleep Drugs Total Technology Issues Driver Disables Total Behavioral Issues Vehicle Issue Physical Impairment Total Inclement Weather Inoperable TCD Distraction

  15. Some Automated Vehicle Caveats

  16. NMVCCS – Implications of the CAS Study

  17. Actuarial Pricing of Auto Insurance • Cost-Based pricing approach • As auto insurance losses decrease, premiums eventually decrease • As opposed to a • Market-Based pricing approach • Charge what the market allows Rating Characteristic Examples • Law of large numbers • Risks grouped by characteristics • Rates charged based on group rating • Actual discount determined by vehicle rating • Driver age • Location • Driving history • Mileage • Vehicle

  18. Types of Auto Coverage ? Coverage not as affected in a world of AVs

  19. Possible Insurance Frameworks for AVs • Product Liability • Attach liability to sellers and manufacturers of the vehicle • Tends to be complex and expensive – as the standard to establish a defect is vague/unpredictable • Strict liability when an AV is at fault • Making the owner of the vehicle responsible when the owner’s automobile is at fault • First party insurance • Similar to UM coverage, injured parties would look to their own insurers • A combination of above?

  20. Current U.S. regulatory approach varies by state http://cyberlaw.stanford.edu/wiki/index.php/Automated_Driving:_Legislative_and_Regulatory_Action

  21. Agenda • Background • Issues • Opportunities

  22. Insurance Industry Will Add Value • More detailed accident data & models • Risk management expertise • Best understanding of 51 different state driving regulations • Best understanding of products liability & general liability • Financial incentive to decrease losses • A commitment to charge rates that are not excessive, inadequate or unfairly discriminatory

  23. Actuarial Opportunities • Responsible for matching price to risk • Past Future: Represents a fundamental change in relationship between driver & vehicle • Heterogeneous: Different products perform differently • Black Box: Cannot readily discern differences • Outside influence: Outside interests may put pressure on rates • Big Data • 1 GB/s data generated • Machine/Deep Learning

  24. Other Considerations • Adoption of AVs • New type of transportation? • Replacement car? • Infrastructure Planning • Car Ownership Pattern • Traveler Behavior Pattern • Impact on public transportation?

  25. You’re Invited… join online today at www.CASstudentcentral.org

  26. CAS Student Central • Membership program for university students: • No Membership fee • Access to resources including P&C internship listings, CAS Curriculum Guide, Case Studies, Online Community • Free webinars created specifically for students • Invitations to free networking events including student programs at CAS meetings and Seminars • Thousands of student members from hundreds of schools

  27. Questions and Discussion rgorvett@casact.org

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