# Presentation: 183542

## SAfety VEhicles using adaptive Interface Technology Phase 1 Research ProgramQuarterly Program Review

- Task 1: Scenario Identification
- Task Leader: David Eby

## 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)**

## 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).**

## 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.**

## 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.**

## 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.**

## 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.**

## 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.**

## 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).**

## 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**

## 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**

## 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. **

## 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.**