Driving automation at IIHS

Consensus Safety Measurement Methodologies for ADSEquipped Vehicles Workshop

June 25, 2019

Jessica Cicchino
IIHSis an independent, nonprofit scientific and 

educational organization dedicated to reducing the losses 

— deaths, injuries and property damage — from crashes 

on the nation’s roads.

HLDI shares this mission by analyzing insurance 

data representing human and economic losses from 

crashes and other events related to vehicle ownership.

Both organizations are wholly supported by auto insurers.
Ratings to promote ADAS that’s proven to work

153

14

34

17

superior

advanced

basic

not available

8 7

superior

advanced

basic
Data are key for understanding real-world effects

Summary of HLDI findings of technology effects on insurance claim frequency

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

forward

collision

warning

fcw with

autobrake

adaptive

headlights

lane

departure

warning

side-view

assist

(blind spot)

parking

sensors

rear camera rear AEB

Collision Property Damage Liability Bodily Injury Liability MedPay PIP
Data are key

Independent and objective research is needed to

foster public confidence in automated driving 

Deployment for public use of automated driving systems

– Publicly available VIN-searchable database for all vehicles with level 2 automation and above

• Listing of all driver assistance and crash avoidance features; level of automation (2+), operational design domains, 

etc. for each applicable feature

• All FMVSS exemptions granted by DOT

– Automatically recorded data in the event of a crash (black box)

• Retrievable with publicly available tool for use by researchers, insurers, law enforcement

• Status of each automated system, last actions including take over request by system, speed, location, etc.

 Testing of automated driving on public roads

– Data on crashes, disengagements and mileage
Focus on safety

Necessary conditions for automation to be safer than human drivers

The critical reason for the critical pre-crash event was attributed to drivers in 94 percent of 

crashes in NHTSA’s NMVCCS database

41% were recognition errors (inadequate surveillance, distraction, inattention)

Automated driving systems need to reliably “recognize” and avoid critical situations better than humans

33% percent were decision errors (speed, wrong assumptions about other road users, 

illegal maneuver, aggressive driving)

ADS need to make better decisions, obey traffic laws and predict the future better than humans

11% performance errors (poor control, freezing); 7% percent non-performance

ADS need to reliably control the vehicle better than humans
IIHS developing evaluation of L2 systems to promote safetyfocused partial automation

Considerations

Safe lane following and speed control within 

designated ODD

Effectiveness of engagement of human co-driver

Enforcement of use within designated ODD

Adherence to/enforcement of traffic laws

Interlocks requiring the use of crash avoidance 

functions including warnings

Interlocks requiring the use of seatbelts

Crash avoidance system ratings

– Maybe set a minimum requirement for AEB etc.
iihs.org

Jessica Cicchino

VP Research, IIHS

jcicchino@iihs.org