Human Factors and VII Enabled Applications A Role for Naturalistic Data Jim Sayer University of Michigan Transportation Research Institute

UMTRI n Established in 1966 n Founding sponsors: vehicle manufacturers n Research oriented toward highway safety n $17 million per year research budget n About 120 staff members n Report to the Vice President for Research

Combining Human Factors and Engineering Domains of Research HFERD AAD SBA TSABiosci UMTRI Dynamics and control of the motor vehicle, modeling Capabilities, limitations, driver behavior, test methodologies Program on the Driving Control Process KNOWLEDGE The normal driving process, as controlled by people - manually and with driver assistance systems.

Driver Assistance Systems Research n Much of this work is field operational tests (FOTs) n Fleets of instrumented vehicles (cameras, radars, DGPS, accelerometers, etc.) n Data acquisition systems n Data archiving o Includes a wealth naturalistic baseline data

Today’s Discussion n What naturalistic driving data currently exist that could help guide/support VII weather related applications? o Driving behavior in inclement weather o What is the behavioral baseline? n A need to better understand driver visibility and the use of windshield wipers o I need historical precipitation data n Sharing of previously unknown resources

Origins of the Naturalistic Data

UMTRI Naturalistic Data with Driver Assistance Systems 1996 – subjects, 2-5 weeks each, passenger cars, manual, adaptive cruise control (ACC) Naturalistic114K mi 2000 – heavy truck drivers, 12 months, manual, roll stability advisory Naturalistic478K mi subjects, 4 weeks each, passenger cars, manual, ACC, forward collision warning Naturalistic140K mi subjects, 4 weeks each, passenger cars, manual, curve and lane departure warnings Naturalistic83K mi drivers, 16 passenger cars and 10 heavy trucks Naturalistic250K mi 500K+ mi  All studies include baseline periods  Almost 800K miles of naturalistic data collection with passenger cars and heavy trucks  With additional experience on test tracks, on-road and pilot testing

Data Acquisition & Remote Monitoring End-of-trip data upload cellular modem: Webpage tracking vehicle and system 750K miles of naturalistic data with 350 drivers

Recent FOT Data Scope n Approx hrs (312 weeks, 1250 trips) n >400 signals n About 700 GB including video/audio n 10 Hz, 1Hz, event- triggered, histograms n Cameras (forward and face), in-cabin microphone n Objective data: vehicle motion and state, environment, driver activities, sensing and processing (vision, GPS/map, radar, constructed maps) n Remote system monitoring n Linked objective data to subjective responses/ demographic data

Integrated Data Collection ACAS Sensor Fusion Threat Radar... ACAS Data DB Main Cpu CAN Visualization/Analysis Tools ACAS Project database Video Cpu Forward camera Face camera DAS... Audio Video/Audio files Phone DB Analyst’s DBs Participant DB subjective questionnaires C e l l p h o n e  SQL analyzer  DB manager  Data browser  Video viewer  Desktop database  Spreadsheet DAS files to tables Test Vehicle Data Archive/Server

Data Analysis and Warehousing Relational database to analyze and mine FOT data

Video and Visualization Tools

Geographic Reference Map of ADAS warnings Selected HPMS data spatially “joined” to road segments Functional Class AADT Urban/Rural Selected crash data spatially “joined” to road segments Date of Crash Gender of Driver Weather condition Functional Class Overlaying Vehicle Data with Crash and Roadway Data

A Naturalistic Examination of Windshield Wiper Usage

The Data Set n 96 drivers from Michigan, USA o Urban, suburban and rural residents o Ranged in age (20-70) n Drove 4 weeks each, instrumented vehicle replaced their personal cars n 137,000 miles (220,000 km), 13,600 trips n 325,000 wiper cycles on 1,700 trips o 170 windshield cleaning events

Wiper Utilization by Month Spray? Rain?

Wiper Speed Selection: Ambient Light Level

Summary n No relationship between wiper speed selected and road class or vehicle speed n Average wiper usage is 8.6% of the time the car is running, or 3.9 events/100 miles n Neither wiper use nor speed selected is readily predicted by precipitation o Attempts to relate wiper use with rain rates was very poor using hourly historic data

Naturalistic Use of High-Beam Headlamps

High-Beam Usage n Drivers vastly under use high-beam headlamps o Even in conditions when glare is not an issue n Data from 87 drivers o ~ 100k miles, of which ~ 21k miles were driven at night o Night defined by a solar zenith angle ≥ 96°

Headlamp Usage

Results n Best case scenario for using high beams: Rural roads, no opposing traffic, not following a vehicle

Summary n Collapsed across road types, high beams are used 3.1% of the mileage driven at night n Even under ideal conditions, high-beam usage averages ~ 25% n Drivers continue to under use high- beam headlamps n Automatic high/low switching could improve this situation

Naturalistic ABS Events

The Data Set n 96 drivers from Michigan, USA o Urban, suburban and rural residents n 137K miles, 13,600 trips, ~ 10 driving yrs n 851 ABS events o ~ 1 every 161 miles o ~ 1 every 16 trips o ~ 85 per year

ABS and Precipitation? 81% of ABS events are without active precipitation

ABS and Temperature 70% of ABS events occur above freezing

ABS and Road Class ABS events over rep. on dirt roads

ABS and Speed 50% of ABS events are initiated under 25 mph 90% of ABS events are initiated under 40 mph

Video Samples

What This Means for VII Weather n There is a wealth of naturalistic data to be mined relative to baseline driving behavior o Data can aid in assessing the probability of a weather related event o Data can aid in determining timing required before issuing an alert o Data can be used for assessing the relative value of providing VII in certain locations

Questions?