Task 1: Scenario Identification Task Leader: David Eby

1. SAfety VEhicles using adaptive Interface Technology Phase 1 Research ProgramQuarterly Program Review Task 1: Scenario Identification Task Leader: David Eby

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

3. Task 1: Scenario Identification • 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).

4. Task 1: Scenario Identification • 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. • 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.

5. Task 1: Scenario Identification • 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.

6. Task 1: Scenario Identification • 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: • Inattention codes are used frequently (nearly 10 percent of cases); • 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.

7. Task 1: Scenario Identification • 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: • In nearly one-half of the crashes the distraction/inattention variables were not reported or unknown; • 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.

8. Task 1: Scenario Identification • 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: • About one-half of crashes were coded unknown for distraction/inattention. • 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.

9. Task 1: Scenario Identification • 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).

10. Task 1: Scenario Identification • 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

11. Task 1: Scenario Identification • 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

12. Task 1: Scenario Identification • 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.

13. Task 1: Scenario Identification • 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: • Background will be crash analyses and literature review • Five outside experts, SAVE-IT team members • 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.