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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
4
1964 Worlds Fair
General Motors Futurama
Self-Driving Car/Taxi
2015 Mercedes Concept Car
Historic Development
5
2005
Stanford wins DARPA
Grand Challenge
2010
Volvo CitySafe standard
2009
- Google begins testing on
public roads
2011
- Google surpasses 150K miles
- BMW begins testing self
driving car on public roads
- NV passes autonomous car
law
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
2007
CMU wins DARPA
Urban Challenge
2014
- MI passes law
- NHTSA passes V2V
- Google developing driverless car
without steering wheel or brakes
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
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):
Level of Vehicle Automation
7
Level 0
No Automation
Level 1Level 2
Level 3Level 4
Combined Function
Automation (e.g. adaptive cruise control
with lane centering)
Limited Self-Driving Automation(e.g. drivers can cede safety-critical functions)
Full Self-Driving
Automation
Function-Specific
Automation(e.g. cruise
control)
Enabled by Connected Vehicles
8
LIDAR: combination of light and
radar, and uses laser light to create 3D
images of the surrounding
environment.
V2V/V2I uses Dedicated
Short Range Communications
(DSRC), similar to wifi
RADAR
Ultrasonic
SensorComputer
Video
Camera
Future development may create two
models for AVs
All driving, limited location Some driving, all locations
• 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
• End to end service• Only operates in specified area• “Taxi” service• Google, Uber
Societal Benefits of AV
10
Reduce accidents
Reduce transportation costs
Support demographic change
Promote the economy
Agenda
Background
Issues
Opportunities
11
CAS AVTF: Overview
12
Pre market Post market Post claim
identify & quantify risks
accurately price the technology
compensate claimants fairly & efficiently
Focus
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.
Goal
Taskforce is actively pursing relevant studies and other opportunities
Issues
13
1. Safety
Are these vehicles safe?
What should the safety standard be?
2. Liability
Who is liable in the event of an accident?
3. Regulation
What regulations should govern the testing and
driving of an AV?
4. Privacy and Cyber Security
Who owns and is responsible for the data
collected by AVs
“93% of accidents are
caused by human error.”
14
NHTSA’s 2008 National Motor Vehicle Crash Causation Survey
“Automated vehicles will
reduce accidents by 93%”
≠
NMVCCS –
Limiting Factors
15
0%
10%
20%
30%
40%
50%
1 2 3 4 1 2 3 4 5 6
12.2% 11.6%
0.4%
21.3%
3.1%
11.0%
2.3% 2.9%
16.7%
32.4%
48.9%Technology IssuesBehavioral
(Driver) Issues
Some Automated Vehicle Caveats
16
NMVCCS –
Implications of the CAS Study
17
New benchmark should be calculated
• Data is old and unrepresentative
• Human driving risks automated vehicle risks
Appropriate test for each risk
• Computer simulations for technology’s error rate
• Simulations provide little insight into driver’s actual use of technology.
Policy changes can increase AV’s safety
• 1% reduction in accidents is ~55k fewer accidents and $1.4 billion of economic value per year
• Policy cost benefit analysis
• E.g. driver training program, automated vehicle only lanes, allowing the AVs to speed
Cost-Based pricing approach
• As auto insurance losses decrease, premiums
eventually decrease
• Driver age
• Location
• Driving history
• Mileage
• Vehicle
Rating Characteristic
Examples
Law of large numbers
• Risks grouped by characteristics
• Rates charged based on group rating
• Actual discount determined by vehicle rating
Actuarial Pricing of Auto Insurance
As opposed to a
Market-Based pricing approach
• Charge what the market allows
Types of Auto Coverage
19
First-Party
• Comprehensive:
• Expenses due to theft, vandalism, glass breakage, and related matters to your car that weren't caused by an auto accident.
• Collision:
• Damages incurred by your vehicle in an auto accident.
• Medical payment coverage:
• Cover medical expenses you incur up to a limit
• Uninsured/underinsured motorist: Cover
• Others: Towing/Rental
Liability
• Bodily Injury:
• Medical-related expenses you caused to others.
• Physical damage:
• Cost to repair or replace other's property (such as a car)
Coverage not as affected in a world of AVs
?
Possible Insurance Frameworks for
AVs
20
1. 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
2. Strict liability when an AV is at fault
Making the owner of the vehicle responsible when the owner’s
automobile is at fault
3. First party insurance
Similar to UM coverage, injured parties would look to their own
insurers
4. A combination of above?
21
Current U.S. regulatory approach
varies by state
http://cyberlaw.stanford.edu/wiki/index.php/Automated_Driving:_Legislative_and_Regulatory_Action
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
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
28
Questions and Discussion