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Analysis of Driver Behavioral Adaptation
to the
Lateral Drift Warning System
Adam Greenstein, M.S.C.E. Candidate
Graduate Research Assistant, Larson Transportation Institute
Department of Civil and Environmental Engineering
Pennsylvania State University
Transportation Engineering and Safety Conference
December 9, 2009
Co-conspirators:
Dr. Paul Jovanis
Dr. Venky Shankar
Kun-Feng Wu, Ph.D. Candidate
Definitions and
Preliminary Discussion
• Lateral Drift Warning (LDW)
– Exceed threshold of distance b/w vehicle and lane
centerlines
• Adaptation
– Changes in alert frequency over time
• Important Findings
– Drivers adapt to LDW alerts by decreasing alert
frequency over time
– Males have more substantial decreases than females
– Drivers who need more excitement while driving have
more overall alerts and cannot maintain a decrease in
frequency over time
Outline
• Introduction
• Data Description
• Hypotheses
• Analyses
• Major Findings, Conclusions
• General ITS and Safety Implications
• Data Limitations, Future Research
Introduction
• Technology – make tasks easier
– Adapt behavior
• Implement ITS technology in vehicles – improve
driving experience
– Often for safety
• Limited research with in-vehicle ITS devices
• Continued need to understand adaptation to
technology
• New device → must be tested
Data Description
• UMTRI RDCW-FOT (2004)
• 87 drivers
• 4 weeks each
– 1st week – system disabled (pseudo-alerts)
– Weeks 2-4 – LDW alerts provided
• Visual, auditory, haptic
• Looking for changes in alert freq. between weeks
• DAS – roadway and environment information,
vehicle kinematics and status (10Hz)
Driver Information and
Test-Related Questionnaires
• Gender
• Smoking Habits
• Questions related to crash predisposition
– Sensation-seeking desires
– Risk perception
Modeling Approach - Hypotheses
• 3 possible responses
– 1 - Rely on system – more risky behavior
– 2 – Learn from dangerous situations – drive more carefully
– 3 - Annoyed by alerts – try to reduce alert likelihood
– (2) and (3) should have same outcome – reduce freq.
• Hypotheses
– Alert frequency increases with distance traveled
– Alert frequency decreases by week in study
– Driver descriptors influence adaptation
Count Models
(Count Model)
(Negative Binomial Distribution)
(Washington et al., 2003)
• Aggregate count models
– Counts of alerts per week as a function of distance
traveled per week, week in the study, driver attributes
• Segmented count models
– By attributes and/or predispositions
– Examine trend differences between driver groups
Count of Alerts Based on
Distance Traveled (by week) – All Drivers
Aggregate Model – All Drivers
Variable Coef. SE z P>z
Weekly distance in miles 0.0026 0.0002 14.6 <0.001
Week 2 -0.2703 0.0833 -3.24 0.001
Week 3 -0.3561 0.0872 -4.08 <0.001
Week 4 -0.3869 0.1014 -3.82 <0.001
Constant 0.6963 0.1419 4.91 <0.001
Number of drivers = 71
Count of Alerts Based on
Distance Traveled (by week) - Gender
Segmented Model Pair – Gender
Males Females
Variable Coef. SE z P>z Coef. SE z P>z
Weekly dist. in mi. 0.0023 0.0003 8.92 <0.001 0.0030 0.0003 11.39 <0.001
Week 2 -0.3569 0.1239 -2.88 0.004 -0.1087 0.1129 -0.96 0.336
Week 3 -0.4551 0.1323 -3.44 0.001 -0.2143 0.1130 -1.9 0.058
Week 4 -0.5655 0.1605 -3.52 <0.001 -0.1955 0.1274 -1.53 0.125
Constant 0.6392 0.1944 3.29 0.001 0.7736 0.2120 3.65 <0.001
Number of males = 34 Number of females = 37
Count of Alerts Based on Distance Traveled
(by week) - Sensation-Seeking Desires
Segmented Model Pair –
Sensation-Seeking Desires
Low Sensation-Seeking Desires High Sensation-Seeking Desires
Variable Coef. SE z P>z Coef. SE z P>z
Weekly dist.
in mi.
0.0026 0.0002 11.77 <0.001 0.0025 0.0003 8.72 <0.001
Week 2 -0.2036 0.1167 -1.74 0.081 -0.3563 0.1183 -3.01 0.003
Week 3 -0.2857 0.1240 -2.3 0.021 -0.4403 0.1204 -3.66 <0.001
Week 4 -0.3485 0.1429 -2.44 0.015 -0.4317 0.1429 -3.02 0.003
Constant 0.5022 0.1878 2.67 0.007 0.9701 0.2204 4.4 <0.001
Drivers in “low” group = 40 Divers in “high” group = 31
Major Findings and Conclusions
• General result: decrease alert freq. over time
when controlling for distance traveled
– Supports 1st hypothesis
• Trends vary between driver groups (attributes
and predispositions)
– Supports 2nd hypothesis
• System does improve safety related to ROR crash
risk
ITS and Safety Implications
• In-vehicle vs. infrastructure-based ITS technologies
– Both can improve safety but in different ways
• Implement based on driver differences
– Distributions within population
• Issues with Data
– Accounting for technological skill
– Sample size issues
– Random sampling of drivers
Acknowledgements
• Strategic Highway Research Program
– SHRP2 S-01: Development of Analysis Methods Using
Recent Data
• UMTRI staff supplied data
www.trb.org/SHRP2 www.umtri.umich.edu
QUESTIONS?
www.mtc.ca.gov

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2009 TESC presentation

  • 1. Analysis of Driver Behavioral Adaptation to the Lateral Drift Warning System Adam Greenstein, M.S.C.E. Candidate Graduate Research Assistant, Larson Transportation Institute Department of Civil and Environmental Engineering Pennsylvania State University Transportation Engineering and Safety Conference December 9, 2009 Co-conspirators: Dr. Paul Jovanis Dr. Venky Shankar Kun-Feng Wu, Ph.D. Candidate
  • 2. Definitions and Preliminary Discussion • Lateral Drift Warning (LDW) – Exceed threshold of distance b/w vehicle and lane centerlines • Adaptation – Changes in alert frequency over time • Important Findings – Drivers adapt to LDW alerts by decreasing alert frequency over time – Males have more substantial decreases than females – Drivers who need more excitement while driving have more overall alerts and cannot maintain a decrease in frequency over time
  • 3. Outline • Introduction • Data Description • Hypotheses • Analyses • Major Findings, Conclusions • General ITS and Safety Implications • Data Limitations, Future Research
  • 4. Introduction • Technology – make tasks easier – Adapt behavior • Implement ITS technology in vehicles – improve driving experience – Often for safety • Limited research with in-vehicle ITS devices • Continued need to understand adaptation to technology • New device → must be tested
  • 5. Data Description • UMTRI RDCW-FOT (2004) • 87 drivers • 4 weeks each – 1st week – system disabled (pseudo-alerts) – Weeks 2-4 – LDW alerts provided • Visual, auditory, haptic • Looking for changes in alert freq. between weeks • DAS – roadway and environment information, vehicle kinematics and status (10Hz)
  • 6. Driver Information and Test-Related Questionnaires • Gender • Smoking Habits • Questions related to crash predisposition – Sensation-seeking desires – Risk perception
  • 7. Modeling Approach - Hypotheses • 3 possible responses – 1 - Rely on system – more risky behavior – 2 – Learn from dangerous situations – drive more carefully – 3 - Annoyed by alerts – try to reduce alert likelihood – (2) and (3) should have same outcome – reduce freq. • Hypotheses – Alert frequency increases with distance traveled – Alert frequency decreases by week in study – Driver descriptors influence adaptation
  • 8. Count Models (Count Model) (Negative Binomial Distribution) (Washington et al., 2003) • Aggregate count models – Counts of alerts per week as a function of distance traveled per week, week in the study, driver attributes • Segmented count models – By attributes and/or predispositions – Examine trend differences between driver groups
  • 9. Count of Alerts Based on Distance Traveled (by week) – All Drivers
  • 10. Aggregate Model – All Drivers Variable Coef. SE z P>z Weekly distance in miles 0.0026 0.0002 14.6 <0.001 Week 2 -0.2703 0.0833 -3.24 0.001 Week 3 -0.3561 0.0872 -4.08 <0.001 Week 4 -0.3869 0.1014 -3.82 <0.001 Constant 0.6963 0.1419 4.91 <0.001 Number of drivers = 71
  • 11. Count of Alerts Based on Distance Traveled (by week) - Gender
  • 12. Segmented Model Pair – Gender Males Females Variable Coef. SE z P>z Coef. SE z P>z Weekly dist. in mi. 0.0023 0.0003 8.92 <0.001 0.0030 0.0003 11.39 <0.001 Week 2 -0.3569 0.1239 -2.88 0.004 -0.1087 0.1129 -0.96 0.336 Week 3 -0.4551 0.1323 -3.44 0.001 -0.2143 0.1130 -1.9 0.058 Week 4 -0.5655 0.1605 -3.52 <0.001 -0.1955 0.1274 -1.53 0.125 Constant 0.6392 0.1944 3.29 0.001 0.7736 0.2120 3.65 <0.001 Number of males = 34 Number of females = 37
  • 13. Count of Alerts Based on Distance Traveled (by week) - Sensation-Seeking Desires
  • 14. Segmented Model Pair – Sensation-Seeking Desires Low Sensation-Seeking Desires High Sensation-Seeking Desires Variable Coef. SE z P>z Coef. SE z P>z Weekly dist. in mi. 0.0026 0.0002 11.77 <0.001 0.0025 0.0003 8.72 <0.001 Week 2 -0.2036 0.1167 -1.74 0.081 -0.3563 0.1183 -3.01 0.003 Week 3 -0.2857 0.1240 -2.3 0.021 -0.4403 0.1204 -3.66 <0.001 Week 4 -0.3485 0.1429 -2.44 0.015 -0.4317 0.1429 -3.02 0.003 Constant 0.5022 0.1878 2.67 0.007 0.9701 0.2204 4.4 <0.001 Drivers in “low” group = 40 Divers in “high” group = 31
  • 15. Major Findings and Conclusions • General result: decrease alert freq. over time when controlling for distance traveled – Supports 1st hypothesis • Trends vary between driver groups (attributes and predispositions) – Supports 2nd hypothesis • System does improve safety related to ROR crash risk
  • 16. ITS and Safety Implications • In-vehicle vs. infrastructure-based ITS technologies – Both can improve safety but in different ways • Implement based on driver differences – Distributions within population • Issues with Data – Accounting for technological skill – Sample size issues – Random sampling of drivers
  • 17. Acknowledgements • Strategic Highway Research Program – SHRP2 S-01: Development of Analysis Methods Using Recent Data • UMTRI staff supplied data www.trb.org/SHRP2 www.umtri.umich.edu