Benchtesting Driver Support and Collision Avoidance Systems using Naturalistic Driving Data Shane McLaughlin March 17, 2011

New technology or application Crash statistics Carry-over systems Observation of drivers System Development Cycle Prototype System Concept Engineer On-road Test track Simulator Participants Friends and colleagues

Timeline as Testing Accrues Participants Driving stylesRoad types Vehicle types Time Driving Time and Mileage Cost Rare Events Ecological Validity Weather conditions Prototype version n

Benchtesting Strategy Many vehicle subsystems process inputs from the vehicle, machine vision, driver controls, etc. Prototype Wheel speed Turn signal state Pedal position Range to forward objects Vehicle yaw Vehicle Control Display Many of these are available directly in naturalistic data sets. Others can be created in post-processing (e.g., a time- to-intersection “sensor”)

Example from Forward Collision Warning (FCW) Development We want to look at the performance of different warning algorithms. We want to evaluate false alert rates. Three public FCW algorithms modeled in MATLAB. Naturalistic data collected during real crashes (100Car study) were used as inputs. Algorithm output was evaluated.

Reaction Start Time Crash / Near Crash Risk Present Perception Time Movement Time Timeline of Input Data and Algorithm Time Available for Driver Response (3) VehicleResponseTime We want to be able to generalize beyond the involved driver’s response capabilities… Real data input into Algorithm Models (1) Alert Occurs Kinematic Estimate based on observed values and equations of motion (2) Response Necessary

Time Available for Driver Response Kinematic Estimate based on observed values and equations of motion Real data input into Algorithm Models Time Crash / Near Crash Risk Present Perception Time Movement Time VehicleResponseTime Cumulative Distribution of Reaction Time from Driving Literature (for now). Reaction Time (s) % of Population Estimate of Percentage of Population Able to Respond in Time Available Reaction Start

One Event 0.675g

Controlled and Stratified Inputs (Age X Vehicle Type) SedanPick-upSUV Age Prototype Young Sedan Drivers Older Sedan Drivers Benefits Estimation Performance measures

Controlled and Stratified Inputs (Age X Weather) SnowClearRain Age Prototype Young Drivers in Snow Older Drivers in Snow Benefits Estimation Performance measures

Controlled and Stratified Inputs (Age X Speed) mph55-70 mph25-55 mph Age Prototype Older Drivers in 0-25 mph Benefits Estimation Performance measures 70+ mph

Types of Uses Anticipating the range of conditions in which a system will be operating Tuning prototype logic Evaluating value of additional sensors or different sensor sample rates Exploring prototype behavior in the “corners” of the design space, including rare events Estimating frequency of applicable driving conditions Estimating false alert rates

Summary Design iterations can be performed quickly before physical prototypes are constructed. Some testing that would require time, vehicles, drivers, recruiting, weather, etc, can be done “on the bench”. Benefits estimates can be made with real exposure information.