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

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Agenda

Background

Issues

Opportunities

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4

1964 Worlds Fair

General Motors Futurama

Google

Self-Driving Car/Taxi

2015 Mercedes Concept Car

Historic Development

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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

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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

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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

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Reduce accidents

Reduce transportation costs

Support demographic change

Promote the economy

Agenda

Background

Issues

Opportunities

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CAS AVTF: Overview

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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

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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.”

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NHTSA’s 2008 National Motor Vehicle Crash Causation Survey

“Automated vehicles will

reduce accidents by 93%”

≠

NMVCCS –

Limiting Factors

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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

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NMVCCS –

Implications of the CAS Study

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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

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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

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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?

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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

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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

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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

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Other Considerations

Adoption of AVs

– New type of transportation?

– Replacement car?

Infrastructure Planning

Car Ownership Pattern

Traveler Behavior Pattern

– Impact on public transportation?

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Questions and Discussion

rgorvett@casact.org