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TRANSPORTATION RESEARCH BOARD
@NASEMTRB#TRBwebinar
Accelerating Automated Vehicle Acceptance
July 14, 2020
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•2.0 Professional Development Hours (PDH) – see follow-up email for instructions•You must attend the entire webinar to be eligible to receive PDH credits•Questions? Contact Reggie Gillum at RGillum@nas.edu
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Learning Objectives
#TRBwebinar
1.Identify current AV practices
2.Discuss how data, policies, and trust impact the pace of automated system technologies
Are We There Yet?Building on TRB Advancing Automated Vehicle Adoption Workshop
Valerie ShumanPrincipal, SCG, LLC
https://connectedautomateddriving.eu/event/computers-wheels-whos-going-keep-track-driverless-vehicles/
Overview
• Key Questions• Roundtable Insights
Are We There Yet?
• What is an AV anyway & who sets that definition?
• Who consistently captures & reports this data for this population (the same way that NHTSA does for the driving public as a whole)?
• How do they do this?
• Roundtable Question: What AV metrics can we implement within 12 months (or sooner)?
https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812826
Tracking AV Capability/Performance Trends
• What are the key tasks and metrics (top 10? Top 20)?• How do we regularly review these metrics as an industry to ensure we’re
trending in the right direction and report progress?
• Roundtable Question: Propose a set of key driving tasks and metrics that we should be monitoring, and a national level solution for monitoring them
What Did We Learn?
• What is an AV?• AV is L3 or 4 and above• Initial focus should be metrics for ADAS and HAV systems (L1/L2)
• What types of metrics?• Focus on efficiency, safety, equity metrics• Look at scenario-based data and outcome metrics to understand status of overall
fleet• Develop metrics for each mode• Consider regional requirements like different levels of AV functionality (e.g., rural)• Need to look at crashes and near misses; collect data on what’s working and what’s
failing
Specific Metrics (1)
• Performance along a “familiar” route• Route performance and adjustments• Takeover time controls in various road conditions and situations• Interaction with local traffic “culture” – is the AV a good citizen?
• Consider overtaking distance (especially for bikes)
• Organizational Design Domains (ODDs)• How much driving is done in and outside of the ODD?• At L4, testing on all intended ODDs for a given road must be “green” before
can use that road• Moving object detection (including speed at which detection is made)
• Develop third party testing standards
Specific Metrics (2)
• Signal detection analysis for certain crash types in various scenarios• Functional testing• Secondary crashes
• Consider contributing factors/context• Develop a list of factors, design scenarios and test to understand crashes/mile
• Environmental data• Takeover requests (planned/unplanned)• Post crash what L1/L2 features were turned on (or not)?
• Was the driver aware of the feature?• Biometrics of person in the car. Is the person in a good state and can reengage? • Vehicle kinematics
How Do We Implement?
• Partner with insurance industry, OEMs, hospitals and public & private sector tracking
• Carefully consider model to encourage private sector sharing – anything too “regulatory” will be a challenge
• Develop nationally consistent/standardized metrics to allow data-sharing and confidence
• Beware of unintended consequences from metrics (incentivize proper design targets)
In Summary
• There is a lot of nuance to consider
• Making choices is going to be tough
• Trust is less important than trustworthiness
https://www.automatedvehiclessymposium.org/register
Trusting Increasingly AutonomousVehicle Technology
John D. Lee
University of Wisconsin—Madison jdlee@engr.wisc.edu
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80.Lee, J. D., & Kolodge, K. (2019). Exploring trust in self-driving vehicles with text analysis. Human Factors.
doi:10.1177/0018720819872672Lee, J. D. (2020). Trust in automated, intelligent, and connected vehicles. In D. L. Fisher, W. J. Horrey, J. D. Lee, & M. A.
Regan (Eds.), Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles. CRC Press.Chiou, E. K. & Lee, J. D. (in review). Trusting automated agents: Designing for appropriate cooperation. Human Factors.
Transportation as multi-echelon network with many trust relationships
• Driver-Automated vehicle trust
• Person-Policymaker trust
Policy, Standards, andSocietal Infrastructure
Remote Infrastructureand Tra�c
Negotiated RoadSituations
Pedestrian trust in AV
Remote operator trust in AV
Driving functions andactivities
Vehicle Sensors andControls
Engineers trust in sensors
Driver trust in sensor system
NTSB Finds Overreliance in Tesla Crash
NTSB Highway Accident Report
7
trim from the car was found entangled within the forward-most area of contact damage on the semitrailer. Figure 6 shows a postcrash photograph of the semitrailer, and figure 7 focuses on the damage to the semitrailer.
Figure 6. Damaged right side of the Utility semitrailer.
Figure 7. Closeup view of impact damage to the right side of the Utility semitrailer. The arrow indicates a segment of front windshield trim from the Tesla entrapped in the forward-most area of damage.
NTSB Highway Accident Report
15
Figure 11. Chart showing how much time during the 41-minute crash trip that, while Autopilot was active, the driver had his hands on the steering wheel. Visual and auditory warnings are also indicated. (Timing provided is based on vehicle data and is approximate and relative.)
System Performance Data. The vehicle performance data revealed the following:
x The crash-involved Tesla’s last trip began at 3:55:23 p.m. The car was stopped ornearly stopped about 4:19 p.m. and again about 4:30 p.m. The collision with the truckoccurred at 4:36:12.7, as indicated by fault codes and system disruptions.
x The last driver input before the crash was to increase the TACC speed to 74 mph at4:34:21, which was 1 minute 51 seconds before the crash. After that input, there wasno driver interaction with Autopilot, no change in steering angle, and no brake lampswitch activation until the collision.
x During the last trip, TACC detected a vehicle ahead of the Tesla seven times. For thefinal 1 minute 35 seconds preceding the crash, TACC detected no lead vehicle in frontof the Tesla.
x About 9.7 seconds before the collision, the motor torque demand steadily decreased(indicating that the vehicle was on a descending grade). The reported torque demanddropped to zero at the time of the first fault report.
x No brakes were applied before or during the collision.
x Vehicle headlights were not on at the time of the collision.
x The driver was wearing his seat belt during the trip.
x Throughout the approach to the collision with the truck, the electronic power assiststeering exhibited no substantial changes in steering angle.
x There was no record indicating that the Tesla’s automation system identified the truckthat was crossing in the car’s path or that it recognized the impending crash. Becausethe system did not detect the combination vehicle—either as a moving hazard or as astationary object—Autopilot did not reduce the vehicle’s speed, the FCW did not
Trust Calibration is Critical
Trust
Trustworthiness
Overtrust
Undertrust
Topic modeling of ~10k responses to JDPower TechChoicesurvey
Technology improving
Tested for long time
Works good?
Selfdriving accidents
Trust when mature
Hacking & glitches
Errors & failures
Many things go wrongScary drivers and robots
Control until proven
Safer than human
Computers make mistakes
Feel uncomfortable
Lee, J. D., & Kolodge, K. (2019). Exploring trust in self-driving vehicles with text analysis. Human Factors, in review.
Psychophysics of Dread Risk Undermine Trust
• Dread risk perceived as 1000 times greater than controlled risk (Slovic, P. (1987). Perception of risk. Science, 236(4799), 280–285)
• Dread risk guides society to risky outcomes(Gigerenzer, G. (2004). Dread risk, September 11, and fatal traffic accidents. Psychological Science, 15(4))
• Trust declines with surprisingly poor behavior: Ease missesMadhavan, P., Weigmann, D. A., & Lacson, F. C. (2006). Automation failures on tasks easily performed by operators undermine trust in automated aids. Human Factors, 48(2), 241–256
Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Song, D. (2017). Robust physical-world attacks on machine learning models.
Small changes produced 100% misclassification
Help People See Benefits
Societal
Relational
Experiential
Basis of Trust Dimensions of Trust
Purpose–Betrayal
Process–Violation
Performance–Disappointment
Trust
Acceptance
Perceived Risk
Sense of Control
1
Trusting Vehicle Technology
Lee, J. D. (2020). Trust in automated, intelligent, and connected vehicles. In D. L. Fisher, W. J. Horrey, J. D. Lee, & M. A. Regan (Eds.), Handbook of Human Factors for Automated, Connected, and Intelligent Vehicles. CRC Press.
Trusting in Increasingly Autonomous Vehicles
• Trusted and trustworthy technology Calibrated with capability and aligned with goals
• Trust in multi-echelon networksTrusters beyond the direct users to include other road usersTrust basis beyond vehicle sensors to include policy makers
• Trust based on societal, relational, and experiential factors Avoid dread risk with transparency and control
John D. LeeUniversity of Wisconsinjdlee@engr.wisc.eduTwitter: jdlee888
Data for AV Integration
ARIEL GOLD
DATA PROGRAM MANAGER
U.S. DEPARTMENT OF TRANSPORTATION (USDOT)
INTELLIGENT TRANSPORTATION SYSTEMS (ITS) JOINT PROGRAM OFFICE (JPO)
JULY 14, 2020
Accelerating Automated Vehicle Acceptance
https://www.transportation.gov/av/data/wzdx
AV 4.0 & Data• Builds upon AV 3.0 by expanding
the scope to 38 relevant United States Government (USG) components
• Highlight crosscutting data-related items: • Privacy and data security• Consistent standards and policies• Multipronged approach to
advance AI• Connectivity and data exchanges
• Features efforts aimed at enabling voluntary data exchanges Source: USDOT https://www.transportation.gov/av/4
https://www.transportation.gov/av/data/wzdx
U.S. DOT’s Data for AV Integration (DAVI) Initiative
https://www.transportation.gov/av/data
Source: USDOT https://www.transportation.gov/av/data
https://www.transportation.gov/av/data/wzdx
DAVI Website
4
DAVI Overview Guiding Principles DAVI Framework
Source: USDOT https://www.transportation.gov/av/data
https://www.transportation.gov/av/data/wzdx
DAVI Framework
5Source: USDOT https://www.transportation.gov/av/data
https://www.transportation.gov/av/data/wzdxhttps://www.transportation.gov/av/data 6
The Work Zone Data eXchange (WZDx)
Source: Work Zone Data Working Group https://github.com/usdot-jpo-ode/jpo-wzdx
https://www.transportation.gov/av/data/wzdx
WZDx Demonstration Grants
• Total funding: $2.4M • Number of Awards: Up to 12 • Potential Award Amounts: Up to $200,000 each • Period of performance: 12 months• Cost Share: 20% Non-Federal Share • Federal involvement: Performance monitoring, technical guidance,
and participation in status meetings, workshops, and technical group discussions.
https://www.grants.gov/web/grants/view-opportunity.html?oppId=327731
Source: ITS JPO
https://www.transportation.gov/av/data/wzdx
Utilizing Common Work Zone Event Data for V2x and Cooperative ADS Applications
8
IOO Work Zone Field Data Collection
Types of content needs
Road Network
Road Furniture
Dynamic Environment Data
Driving Task Content Needs
Driving Task Questions
Where am I relative to my environment?
What are the rules of the road that affect path?
What’s changed from what I already know? Road Usage
Restrictions
Signal and VMS status/translation
Speed Limit Changes
Geometry Changes
Work Zones
Lane Closures
Signal LocationsData Fusion & Decision
Develop tools to collect spatial data from the field to support work zone data collection
Develop software translators for V2X and
cooperative ADS applications
Improved Data Specifications & Tools
WZDI Program
CARMA3
WZDx Specification
Identify improvements to spatial data
elements for work zone events
Source: FHWA
https://www.transportation.gov/av/data/wzdx
Cooperative ADS as a Component of Work Zone Event Data
9
• TMC Operator/IOOs enter basic information about work zone.
• Data is consistent with WZDx spec v2.0.
• Data collection automatically starts/ends when set starting/ending locations are reached.
• User interface to select current state of road/work zone.
Copyright: https://github.com/TonyEnglish/V2X-manual-data-collection
https://www.transportation.gov/av/data/wzdx
Cooperative ADS as a Component of Work Zone Event Data
10
• Received information is used to generate a work zone with new geospatial details in the back office (cloud) for validation.
• Overlay WZ Map information.
• TMC Operator verifies accuracy of recorded work zone.
• TMC Operator publish verified work zones available for 3rd parties, 511, etc.
• Repository available at https://github.com/TonyEnglish/V2X-manual-data-collection
Copyright https://github.com/TonyEnglish/V2X-manual-data-collection
https://www.transportation.gov/av/data/wzdx
• Many outside the federal government are contributing open training data sets that assist with computer vision and other core Automated Driving System (ADS) functions
• Open training data sets:• BDD100K from the University of California
at Berkeley• Waymo Open Dataset• Lyft Level 5 Dataset• Audi AEV Autonomous Driving Dataset• Ford Autonomous Vehicle Dataset
• Contact avdx@dot.gov to share other examples of open training data sets
11
Open Training Data Sets
Source: University of California at Berkeley (https://bdd-data.berkeley.edu/)
https://www.transportation.gov/av/data/wzdx
For More Information
Ariel GoldData Program Manager
U.S. Department of Transportation
Intelligent Transportation Systems Joint Program Office
Ariel.Gold@dot.gov
Twitter: @ITSJPODirector
Website: www.its.dot.gov
Facebook: www.facebook.com/DOTRITA
iihs.org
ADAS Ratings for Consumer Information ProgramAccelerating Automated Vehicle Acceptance
David HarkeyInsurance Institute for Highway Safety
TRB WebinarJuly 14, 2020
IIHS consumer ratings
4200+ 2019 models rated
410 evaluations per model
4460 new ratings in 2019
2017Small overlap front:
passenger-side
2012Small overlap front:
driver-side
Roof strength2009
2004Rear
(whiplash mitigation)
Side impact2003
1995Moderate overlapfront
IIHS crashtesting programs
Effect of crash avoidance systems on claim frequencyResults pooled across automakers
-40%
-20%
0%
20%
40%
forward collisionwarning
frontautobrake
curve-adaptiveheadlights
lane departurewarning
blind spotwarning
parkingsensors
rearcamera
rearautobrake
Collision Property damage liability Bodily injury liability MedPay PIP
Most crash avoidance technologies are living up to expectationsEffects on relevant police-reported crash types
-80%
-60%
-40%
-20%
0%
20%
forward collision warning low-speed autobrake FCW with autobrake lane departure warning side-view assist(blind spot)
all severities injury statistically significant
2020 TOP SAFETY PICK requirementsGood ratings in the driver-side small overlap front, passenger side small overlap front, moderate overlap front, side, roof strength and head restraint tests
Advanced or superior rating for pedestrian AEB as Optional equipment
Good or acceptable headlight as Standard equipment
Advance or superior rating for front crash prevention as Optional equipment
G A
G
Good or acceptable headlight as Optional equipment
Good ratings in the driver-side small overlap front, passenger side small overlap front, moderate overlap front, side, roof strength and head restraint tests
Advanced or superior rating for pedestrian AEB as Optional equipment
Advanced or superior rating for front crash prevention as Optional equipment
G
G A
Volvo S60(2 points advanced)
Dodge Durango(3 points advanced
Subaru Outback(6 points superior)
12 mph test(speed reduction)
12 mph 6 mph 12 mph
24 mph test(speed reduction)
2 mph 9 mph 12 mph
ü ü
ü
Speed reduction in 12 and 24 mph tests
Front crash prevention: vehicle-to-vehicle ratings2013 – 20 models, as of July 2020
0%
20%
40%
60%
80%
100%
2013 2014 2015 2016 2017 2018 2019 2020
Pedestrian test scenarios
Pedestrian crash prevention ratingsAs of November 2019
0
2
4
6
8
Superior Advanced Basic No credit
small SUVs
midsize sedans
Adult walking from the right side25 mph condition
Adult walking from the right side25 mph condition
Adult walking from the right side25 mph condition
Adult walking from the right side25 mph condition
Child running from the right side12 mph condition
Child running from the right side12 mph condition
Child running from the right side12 mph condition
Child running from the right side12 mph condition
Rear crash prevention ratings4 Rear parking sensors
4 Rear cross traffic alert
4 Rear autobrake
reversing car-to-car, 16” overlap reversing car-to-car, 45° angle
reversing car-to-car, 10° angle reversing toward fixed pole
Functional performance and user experience
2017 BMW 5 serieswith Driving
Assistant Plus
2017 Mercedes E-Classwith Drive Pilot
2016 Tesla Model Swith Autopilot
software ver. 7.1
2018 Volvo S90with Pilot Assist
2018 Tesla Model 3with Autopilot
software ver. 8.1
Lane keeping in curves
0%
20%
40%
60%
80%
100%
BMW 5 seriesn=16
Volvo S90n=17
Mercedes E-Classn=17
Tesla Model Sn=18
Tesla Model 3n=18
disengaged
crossed lane line
on lane line
remained in lane
Lane keeping on hills
0%
20%
40%
60%
80%
100%
BMW 5 seriesn=14
Tesla Model Sn=18
Volvo S90 n=17
Mercedes E-Classn=18
Tesla Model 3n=18
disengaged
crossed lane line
on lane line
remained in lane
Adaptive cruise control trusted more than active lane keepingPercentage of drivers who agreed or strongly agreed
0
20
40
60
80
100
Tesla Model SAutopilot
Volvo S90Pilot Assist
BMW 5 seriesDriving Assistant Plus
Infiniti QX50ProPilot Assist
Mercedes E-ClassDrive Pilot
I trust the automation to maintain speed and distance to vehicle ahead
I trust the automation to keep me in center of lane
IIHS consumer information does more…Since 1995
4Empower consumers
4Identify gaps in safety regulations and testing programs
4Encourage automakers
4Accelerate technology integration!
Thank you
Today’s Panelists• Moderator: Cynthia Jones, DriveOhio• John Lee, University of Wisconsin-
Madison• Valerie Shuman, Shuman Consulting
Group• Ariel Gold, US DOT• David Harkey, Insurance Institute for
Highway Safety
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