1. The Logic for the Distracted Driver David Strayer

2. Perspectives on Driving Simulation  Behavioral equation  Study design  Scenario development  Behavioral measures  Statistical analyses

3. Driver Distraction  An activity that competes or interferes with processing information critical to the safe operation of a motor vehicle (i.e., some aspect of driving performance is impaired by the concurrent performance of this activity).  Distracted by something  Not all sources of distraction are equal  Sensory/motor (e.g., lighting a cigarette, looking at a map)  Attentional/cognitive (e.g., talking on a cell phone)  What is the duration of the activity?

4. I-SIM Driving Simulator

5. Customized Data Collection Software  5 networked microprocessors  Multicast data sampled 60 Hz  Real-time stream  Driving parameters (speed, lane position, etc.)  Parameters of all other objects in scenario  Trigger real-time “outside” events (e.g., ERP recording)  Off-line analyses extract meaningful data  C++ code freely available

6. Study Design  Car-following paradigm  Follow periodically braking pace car (32 trials)  Required timely and appropriate reactions  Single- and Dual-task conditions (counterbalanced)  Dual-task: Hands-free cell phone (positioned in advance)  Naturalistic conversations with friends  Performance Measures  Driving speed  Reaction time  Recovery time  Following distance  Lane keeping  Time to collision

7. Car Following Details  24 mile multi-lane beltway (straight + gradual turns)  Daytime, dry pavement  Pace car programmed to travel in right lane and brake at 32 randomly distributed locations (unpredictable)  Distractor vehicles programmed to travel in left lane between 5 and 10% faster than pace car, providing impression of steady flow of traffic (manipulation of perceptual load)  Scenario duration ~ 10 minutes

8. Car Following Paradigm

9. Building Driving Profiles

10. Driving Profile Data Matrix (Method borrowed from ERP literature) 123456.. 300 2 3 4.. 32 Time Trials Average across trials to create a driving profile for each subject/condition (Noise decreases as a function of the sqrt of N)

11. Driving Speed Profile

12. Reaction Time

13. ½ Recovery Time

14. Following Distance Profile

15. Effects of Cell Phone Conversations on Driving (Meta-analysis using car following paradigm)  Drivers conversing on a cell phone were 5 times more likely to be involved in a traffic accident χ 2 (1)=6.1, p=0.013 Traffic Accident No Accident Single-Task 2118120 Dual-Task 10110120 12228240

16. Discriminating Distractions  Not all sources of distraction are equal  Multivariate Analysis of Variance  Discriminant function analysis

17. Latest Developments  ASL 501 mobile eye-tracker  Eye-scanning patterns  Fix probability  Fix duration  Conditional recognition memory analyses  Video tape sessions  Detailed coding of behavior  Audio tape sessions  Conversation analysis  Turn taking  Content analysis