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Casualty Actuarial Society
Automated Vehicle Task Force (CAS AVTF)
Wenwen Salerno, ACAS, MAAA
August, 2015
Vehicle operates as Windows System
• Automobiles would frequently crash for no apparent reason. This would be so common that motorists would simply accept it, restart their car and continue driving.
• Airbags would ask, 'Are you sure?' before deployment. • Vehicles would occasionally shut down completely and
refuse to restart, requiring motorists to reinstall their engine.
2 www.vtpi.org/avip.pdf
3
Example
Video
CAS AVTF: Overview
4
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
Agenda
Automated Vehicles – Background
Insurance Industry Impact
Automated Vehicle Risk Profile
Vehicle Symbol Analysis
Regulatory Overview
5
Agenda
Automated Vehicles – Background
Insurance Industry Impact
Automated Vehicle Risk Profile
Vehicle Symbol Analysis
Regulatory Overview
6
Automated Vehicle Components
Automatic transmissions Diverse sensors (cameras, radars, etc.) Wireless network Navigation system including GPS Automated control (Steering, braking, signals, etc.)
Servers, software and power supply
7 www.vtpi.org/avip.pdf
Driver Assistance Features
8
Three key technologies for AVs
9
V2V/V2I
LiDAR
Inertial Navigation
Systems & GPS
• Vehicle to Vehicle or Vehicle to Infrastructure • Dedicated Short Range Communications (DSRC), similar to wifi • Vehicle to communication to other vehicles or infrastructure (traffic
signals, toll booths, etc
• Light Detection And Ranging • combination of light and radar, and uses laser light to create 3D images
of the surrounding environment. • Remote sensing technology to measure distances
• INS uses computers, accelerometers (motion), and gyroscopes (rotation) • Calculates position, orientation, and velocity
10
Cameras and Deep Learning may be another technology NVIDIA @ CES 2015
NVIDIA video https://www.youtube.com/watch?v=o29TBy2a0ek
Historic Developments
11
2005 – 2010
• 05: Stanford wins DARPA Grand Challenge
• 07: CMU wins DARPA Urban Challenge
• 09: Google begins tesCng on public roads
• 09: EU launches Project SARTRE
• 10: Volvo City Safe standard
2011
• Google surpasses 150K miles
• BMW begins tesCng self driving car on public roads
• NV passes autonomous car law
2012
• Google @ 300K accident free miles
• Nissan opens research facility in Silicon Valley
• Google & ConCnental receive autonomous car licenses
• FL & CA pass autonomous car laws
2013
• Google @ 500K miles
• Oxford creates a $7,750 self-‐driving system
• Public road tesCng • Britain • Mercedes • CMU
• Audi receives AV car license
• NHTSA issues policy on AVs
• DC passes AV car law
2014
• MI passes AV law
• NHTSA passes V2V
• Google @ 700k miles
• Volvo ‘Drive Me’ tests in Gothenburg
• Google developing driverless car without steering wheel or brakes
2015 Developments
12
January • Audi partner with Nvidea to use Tegra X1 chips • Nissan & NASA announce automated research project partnership
February • Uber and Carnegie Mellon University announce strategic partnership • Apple reported to be working on automated vehicles Sony’s image sensors and ZMP’s
robotics
April • Delphi 3,400 mile cross country trip with 99% of miles in autonomous mode • Nevada licenses Mercedes’ Freightliner truck • Nokia’s HERE: 2 billion offer from auto manufacture and Uber reported to offer $3 billion
May • Tesla adjusts adaptive cruise control to limit liability • Daimler’s self-driving truck got license and hit the road July • M City in University of Michigan
London’s AV testing regulations may increase investment
13
Goal • Light-touch non-regulatory approach • provides clarity for industry to invest in further
in research
No special licenses or
permits requires
No geographical limits
No additional insurance
requirements
London has 3 trials underway
14
Milton Keyes and Coventry Greenwich
• Lutz Pods that drive in pedestrian zones
• Max speed 15 mph • Electronic AV
UK Autodrive Programme: 3 years to pave way for introduc?on of AVs Dept. of Transporta.on put ~$29M USD for trials
Explore both legal and technical changes required for Autonomous Vehicles
Bristol
• GATEway shucles • Electronic AV • Local tour with drop
off points: input desCnaCon on CPU
• Venturer consorCum will invesCgate congesCon and safety
• BAE Wildcat
Future development may create three models for AVs
Private: all driving/limited loca?on Private: Some driving/all loca?ons
• 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
16
Commercial: Some driving/Special Vehicle
http://www.bloomberg.com/news/articles/2015-05-14/daimler-s-freightliner-tests-self-driving-truck-in-nevada
Daimler’s video
17
Commercial: Some driving/Special Vehicle
Agenda
Automated Vehicles – Background
Insurance Industry Impact
Automated Vehicle Risk Profile
Vehicle Symbol Analysis
Regulatory Overview
18
Private Passenger Auto Insurance Coverage Components
19
Liability -‐ Bodily Injury Liability, $131
Liability -‐ Property Damage Liability, $78
Liability -‐ Medical Payment, $7
Liability -‐ Personal Injury Protec?on, $104
Liability -‐ UM/UIM Bodily Injury, $85
Physical Damage -‐ Comprehensive, $50
Physical Damage -‐ Collision, $105
Other -‐ Addi?onal Service, $17
PRIVATE PASSENGER AUTO INSURANCE EXAMPLE
From presenter’s example quotes
Physical Damage/Liability Coverages
20
Physical Damage/Liability Coverages
21
Liability Coverages
22
Private Passenger Auto Liability vs. Products Liability
23
ALAE Factor
Permissible Loss Ratio
Accident classification
Liability limits
Settlement lag
Unnecessary coverages
Insurers’ responses
24
Pricing Adjustments Proprietary coverage level vehicle symbols
Paten?ng Pricing Approaches State Farm: Trip-‐Based Insurance Pricing Plan (2015) Travelers: “Risk-‐Zone” Pricing (2014) Progressive: Vehicle sensor approach (2012)
Forming Partnerships Ford, State Farm & U of Michigan – Ford Hybrid automated research vehicle (Dec 2013) Honda & major insurance company sign agreement to use self-‐driving automobile test track
at former Concord Naval Weapons StaCon (March 2015)
Tes?fying at hearings CA DOI: State Farm & NaConwide & CAS AVTF NJ Senate: Munich Re America
Industry Groups are performing research CAS; HLDI-‐IIHS; RAND Corp; Brookings InsCtute
Agenda
Automated Vehicles – Background
Insurance Industry Impact
Automated Vehicle Risk Profile
Vehicle Symbol Analysis
Regulatory Overview
25
“93% of accidents are caused by human error.”
26
National Motor Vehicle Crash Causation Survey (NMVCCS) – Limiting Factors
27
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 Issues
Behavioral (Driver) Issues
NMVCCS – Implications of the CAS Study
28
New benchmark should be calculated
• Data is old and unrepresentaCve • Human driving risks <> automated vehicle risks
Appropriate test for each risk • Computer simulaCons for technology’s error rate • SimulaCons provide licle insight into driver’s actual use of technology.
Policy changes can increase AV’s safety • 1% reducCon 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
Agenda
Automated Vehicles – Background
Insurance Industry Impact
Automated Vehicle Risk Profile
Vehicle Symbol Analysis
Regulatory Overview
29
Vehicle symbol analysis approach
30
Vehicle experience groups
Complements to credibility
Automated vehicle symbol:
op?on 1
• Each group’s experience is weighted and combined with similar vehicles
• Vehicle’s body style factor • Prior year factor
• Assume a brand new vehicle • e.g. Mercedes introduces a new fully automated vehicle • No iniCal prior year factor, growth trend impacts credibility
Automated vehicle symbol:
op?on 2
• Assume update to a current vehicle • e.g. all new Honda Civics sold with AV equipment
Vehicle symbol analysis approach
31
Vehicle experience groups
Complements to credibility
Automated vehicle symbol:
op?on 1
• Each group’s experience is weighted and combined with similar vehicles
• Vehicle’s body style factor • Prior year factor
• Assume a brand new vehicle • e.g. Mercedes introduces a new fully automated vehicle • No iniCal prior year factor, growth trend impacts credibility
Automated vehicle symbol:
op?on 2
• Assume update to a current vehicle • e.g. all new Honda Civics sold with AV equipment
Vehicle Symbol Calculation
32
Automated vehicle symbol:
op?on 1
• Assume a brand new vehicle • e.g. Mercedes introduces a new fully automated vehicle • No iniCal prior year factor, growth trend impacts
credibility
Vehicle Symbol Discount
Number of Exposures
Loss Aeenua?on
Year 0% 25% 50% 75% 100%
2,500 1 0.0% 0.5% 0.9% 1.3% 1.8%
5,000 2 0.0% 1.4% 2.6% 3.9% 5.1%
7,500 3 0.0% 2.8% 5.1% 7.4% 9.7%
10,000 4 0.0% 4.4% 8.0% 11.6% 15.2%
Vehicle Symbol Calculation
33
Vehicle Symbol Discount Loss Aeenua?on
Year 0% 25% 50% 75% 100%
1 0.0% 4.3% 7.4% 10.5% 13.6%
2 0.0% 7.1% 13.7% 20.0% 26.3%
3 0.0% 9.7% 18.2% 25.7% 35.4%
4 0.0% 11.1% 21.0% 31.0% 41.2%
Automated vehicle symbol:
op?on 2
• Assume update to a current vehicle • e.g. all new Honda Civics sold with AV equipment
Upcoming Projects: Average Accident Rate
Comparable Driver Match location & type Match driver characteristics
Issues with NHTSA Data Only include police reported accidents Cannot segment by driver type
Insurance Data Calculate frequencies for different driving segments Can more accurately define “good driver” GLM’s lead to a more stable & accurate calculations
34
Upcoming Projects: Auto Liability vs. Products Liability
Quantify the change in costs from liability systems
Scenario #1: Assume no change in accidents
Scenario #2: Accident frequency is reduced by X% – Determine what X needs to equal for Scenario 1 = Scenario 2
Scenario #3: Cap liability to $Z – Determine what Z needs to be for Scenario 1 = Scenario 3
Scenario #4: Combination of Scenario #2 & #3
35
Agenda
Automated Vehicles – Background
Insurance Industry Impact
Automated Vehicle Risk Profile
Vehicle Symbol Analysis
Regulatory Overview
36
37
Current U.S. regulatory approach varies by state
Comments § CA, DC, FL, MI, NV have
regulations that permit operation/testing of AVs
§ May 2013 NHTSA publication
§ Statement with guidance to states on AV regulations
§ Statement outlined NHTSA plans for testing AV technology
hcp://cyberlaw.stanford.edu/wiki/index.php/Automated_Driving:_LegislaCve_and_Regulatory_AcCon
Consumer Protection
38
NHTSA
States
Individuals
• establishes regulaCons that manufacturers must self-‐cerCfy with
• State regulators can impose addiConal requirements
• Individuals can sue manufacturers if an error occurs
Manufactures • An alliance of twelve automakers will
create a center for sharing informaCon and analysis.
Regulatory approach needs to be updated
39
Insufficient protec?on for consumers and manufacturers • AlliedSignal, Inc v. Moran
NHTSA does not have the scale: annual budget 60,000
82822
$M IIHS-HLDI
Auto Insurance NHTSA
• NHTSA lacks financial incenCve of auto insurance industry • Accurate evaluaCon of risk
Claims handling incen?ves • CompensaCng claimants fairly and efficiently is a core
competence of the insurance industry • Auto industry’s competence is not insurance
Questions and Discussion