I/UCRC Program Review XVII Driving Performance Data Verification Process at the NADS Judith Wightman Ginger S. Watson Judith Wightman Ginger S. Watson

I/UCRC Program Review XVIII/UCRC Program Review XVIII/UCRC Program Review XVIII/UCRC Program Review XVII

Driving Performance Data Verification Process at the NADS

Driving Performance Data Verification Process at the NADS

Judith WightmanGinger S. WatsonJudith WightmanGinger S. Watson


Data Verification at NADSData Verification at NADS

• Overview

• Raw Data

• Data Reduction

• Verification Tools

• Issues, Concerns, Future Research

• Overview

• Raw Data

• Data Reduction

• Verification Tools

• Issues, Concerns, Future Research


Data Verification at NADSData Verification at NADS

• The goal is to ensure that collected data are – Reliable

• Consistent or reproducible

– Valid• Meaningful and relevant

– Complete, yet concise

• The goal is to ensure that collected data are – Reliable

• Consistent or reproducible

– Valid• Meaningful and relevant

– Complete, yet concise


Raw DataRaw Data

• Master list of up to 400 internal variables available on NADS

• Subset chosen based on goals of experiment– Binary collected in the data acquisition system (DAQ)– Various frequencies available per variable (up to 240 Hz)– Data can be converted to other formats and used in

various ways• MATLAB• ASCII• Excel• ISAT (Interactive Scenario Authoring Tool; for playback)• Reduced by coded algorithms

• Master list of up to 400 internal variables available on NADS

• Subset chosen based on goals of experiment– Binary collected in the data acquisition system (DAQ)– Various frequencies available per variable (up to 240 Hz)– Data can be converted to other formats and used in

various ways• MATLAB• ASCII• Excel• ISAT (Interactive Scenario Authoring Tool; for playback)• Reduced by coded algorithms


Data ReductionData Reduction

• The process of transforming raw data into meaningful summary variables (called “reduced data”) that address experimental hypotheses

• Components– Identifying and defining summary variables of interest– Filtering the data– Applying various algorithms (automated or human

processed) to obtain the desired measures– Verifying that reduced data are valid (meaningful) and

reliable (consistent)

• The process of transforming raw data into meaningful summary variables (called “reduced data”) that address experimental hypotheses

• Components– Identifying and defining summary variables of interest– Filtering the data– Applying various algorithms (automated or human

processed) to obtain the desired measures– Verifying that reduced data are valid (meaningful) and

reliable (consistent)


Data Reduction MethodsData Reduction Methods

• Automated code– Used to transform many data points into a

concise measure such as reaction time– Examples

• Reaction time to 10% release in accelerator pedal from point of incurring vehicle creation

• Minimum time to collision with incurring vehicle from point of incurring vehicle creation

• Automated code– Used to transform many data points into a

concise measure such as reaction time– Examples

• Reaction time to 10% release in accelerator pedal from point of incurring vehicle creation

• Minimum time to collision with incurring vehicle from point of incurring vehicle creation


Data Reduction MethodsData Reduction Methods

• Human coding– Used when video analysis

or decomposition of non-driving behavior is needed, or when more efficient than automated coding

– Examples• Determination of number

of seconds taken to dial a wireless phone

• Loss of control in ESC studies (90 degrees non-zero yaw rate)

• Human coding– Used when video analysis

or decomposition of non-driving behavior is needed, or when more efficient than automated coding

– Examples• Determination of number

of seconds taken to dial a wireless phone

• Loss of control in ESC studies (90 degrees non-zero yaw rate)


Verification ToolsVerification Tools

• ISAT– Displays top-down view of logical database and

scenario saved in raw data stream– Allows for review of participant drives and

examination of various reduced measures– Examples

• Verification of reaction time• Verification of accelerator release

• ISAT– Displays top-down view of logical database and

scenario saved in raw data stream– Allows for review of participant drives and

examination of various reduced measures– Examples

• Verification of reaction time• Verification of accelerator release


Video ExampleVideo Example


ISAT Playback: Incurring Vehicle CreationISAT Playback: Incurring Vehicle Creation


ISAT Playback: Accelerator ReleaseISAT Playback: Accelerator Release


ISAT Playback: Brake OnsetISAT Playback: Brake Onset


ISAT Playback: End of EventISAT Playback: End of Event



• SAS/Excel– Used to calculate measures of central tendency

for group or treatment, plot, or statistically analyze measures

– Examples• Velocity variance as calculated in SAS/Excel

equivalent to the reduced variable velocity instability in certain situations

• Excel used for calculating length of various parameters of wireless call data (e.g., dial/answer, connect, converse, disconnect)

• SAS/Excel– Used to calculate measures of central tendency

for group or treatment, plot, or statistically analyze measures

– Examples• Velocity variance as calculated in SAS/Excel

equivalent to the reduced variable velocity instability in certain situations

• Excel used for calculating length of various parameters of wireless call data (e.g., dial/answer, connect, converse, disconnect)



• MATLAB– Used to plot performance over time– Example

• Sanity checks on phase (continuous measure of the delay in the participant’s reaction time to changes in lead vehicle’s speed)

• MATLAB– Used to plot performance over time– Example

• Sanity checks on phase (continuous measure of the delay in the participant’s reaction time to changes in lead vehicle’s speed)


MATLAB ExamplesMATLAB Examples


Issues, Concerns, Future ResearchIssues, Concerns, Future Research

• Missing Data

• Generalizability

• Questions

• Missing Data

• Generalizability

• Questions

Documents

I/UCRC Program Review XVII Driving Performance Data Verification Process at the NADS Judith Wightman Ginger S. Watson Judith Wightman Ginger S. Watson