SAVE-IT SAfety VEhicles using adaptive Interface Technology Phase 1 Research Program Quarterly Program Review Task 1: Scenario Identification Task Leader: David Eby

SAVE-IT Task 1: Scenario Identification u Objective: Identify distraction-related scenarios that the SAVE-IT technology should be designed to prevent. u Staffing: Eby, Kostyniuk, research assistant u Deliverables –Literature Review (completed) –Final Report u Schedule –Conduct literature search (Mar, 2003) –Literature review (April, 2003) –Data analysis (Nov, 2003) –Expert panel meeting (early Nov, 2003) –Final report (Dec, 2003)

SAVE-IT Task 1: Scenario Identification u Background –Safe operation of a motor vehicle requires that a driver focus a substantial portion of his or her attention on driving related tasks. –A driver may also engage in non-driving tasks that compete for attention. –As non-driving activities increase, the driver allocates more attention to them, and/or the driver’s attentional capacity is reduced, there are fewer attentional resources available for safe driving (inattention). –20-50% of crashes involve some form of inattention. –Distraction is one form of inattention »Results from a triggering event leading to delayed recognition of important information (Stutts, et al., 2001).

SAVE-IT Task 1: Scenario Identification u Background, continued –Driver distraction a contributing factor in 8-13% of tow-away crashes (Stutts, et al., 2001; Wang, et al., 1996). –Determining the effect of distraction on crash risk is quite challenging: »Databases lack good information about distraction-related events leading up to crashes; »Interpretation is difficult because of a lack of exposure data. –Our approach is to assess and synthesize available information that may be indicative of distraction-related crash scenarios to determine which may be preventable by the SAVE-IT system. u Activities –Literature Review »Purpose: Review and assess available crash databases to determine which variables are available, feasible, and appropriate; »Purpose: Investigate a variety of of other distraction-related driving- scenarios that may not appear in crash databases but are, nevertheless, important for this project.

SAVE-IT Task 1: Scenario Identification u Assess Crash Databases –Three important areas of information related to distraction-related crashes: »Distraction variables (sources of distraction) »Inattention variables (e.g., driver’s physical state) »Driver demand variables (roadway, traffic, environment; Task 2a). »The ideal crash database would contain detailed and accurate information for these variables.

SAVE-IT Task 1: Scenario Identification u Assess Crash Databases –Fatality Analysis Reporting System (FARS) »Census of all US vehicle crashes with at least one fatality »Information comes from police-reports and detailed field investigation. »Has numerous distraction/inattention codes. »Our analysis of 2000 FARS data showed: u Inattention codes are used frequently (nearly 10 percent of cases); u Distraction codes are rarely used and 31 states do not report distraction; »FARS is not useful for this project. –Highway Safety Information System (HSIS) »Database designed for studying the relationship between road features and crashes. »Data come from 8 states and are different for each state. »Information is police-reported crashes on state trunklines. »Poor distraction/inattention codes for all states, but good driving demand information. »HSIS not useful for this task, but useful for Task 2a.

SAVE-IT Task 1: Scenario Identification u Assess Crash Databases, continued –Regional databases »Many states are developing regional Geographic Information System (GIS) databases. »Link crashes with road network, traffic volumes, land use, etc. »Not generally focused on distraction/inattention –National Automotive Sampling System: General Estimates System »Nationally representative probability sample of police-reported crashes in the US (all crash and vehicle types). »Information comes from police crash-reports. »As of 1999, several variables on both distraction and inattention were added. »Our analysis of 2000 GES showed: u In nearly one-half of the crashes the distraction/inattention variables were not reported or unknown; u When coded, generally only three categories were used: Inattentive, looked-but-did- not-see, and sleepy/asleep; »GES will have great value in the future, but not for this project.

SAVE-IT Task 1: Scenario Identification u Assess Crash Databases, continued –National Automotive Sampling System: Crashworthiness Data System »Representative, random sample of about 5,000 police-reported crashes (passenger cars, tow-away damage). »Data come from detailed field investigation. »In 1995, detailed coding of distraction/inattention variables was included. »Our analysis of 2000 CDS showed: u About one-half of crashes were coded unknown for distraction/inattention. u When distraction/inattention was indicated, a wide range of distraction/inattention codes were used. »We conclude that this is the best database for this task.

SAVE-IT Task 1: Scenario Identification u Distracted Driving Crash Scenarios –We reviewed literature that analyzed crash databases (GES, CDS) for distraction-related crashes. –From these studies, five scenarios emerged: single-vehicle-run-off-the- road (SVROR); rear-end (RE); intersection/crossing path (I/CP); lane change/merge (LC/M); and head-on (HO).

SAVE-IT Task 1: Scenario Identification u Distracted-Driving Scenarios –We reviewed literature related to various scenarios (events) that can trigger driver distraction. –Omitted from this part of the review were factors related to other forms of inattention (e.g., fatigue, alcohol, medical condition), recognizing that these factors can influence distraction and crash outcomes. –Scenarios were divided into whether they occurred outside or inside the vehicle: –Outside the vehicle: »Exterior incident »Looking at scenery/landmark

SAVE-IT Task 1: Scenario Identification u Distracted-Driving Scenarios, continued –Inside the vehicle: »Passengers »Adjusting entertainment system »Listening to music »Cellular phone use »Route-guidance systems »Eating or drinking »Adjusting vehicle controls »Objects moving in the vehicle »Smoking »Reading »Use of wireless technology (PDAs) »Night vision systems »Personal grooming

SAVE-IT Task 1: Scenario Identification u Distracted-Driving Scenarios, continued –Little direct data available to help us determine the relative contribution of these scenarios to distraction-related crash risk. –First-pass method is to rank scenarios on measures believed to be related to the likelihood of a distraction-related crash: »Exposure »Volition »Linkage with driving demand »Level of distraction –These ranking are a possible activity for an expert panel.

SAVE-IT Task 1: Scenario Identification u Activities, continued –Crash database analysis »CDS data will be utilized. »Access to CDS is available. »We are formulating the plan. –Expert panel »Originally, this panel was scheduled for early in Task 1 so that the group could comment on the analysis plan and scenarios. »Now it is scheduled for the end of the task: u Background will be crash analyses and literature review u Five outside experts, SAVE-IT team members u Main purpose will be to reach consensus on which scenarios SAVE-IT should be designed to prevent. –Final report that documents crash database analysis results and expert panel activities.