01623737

Component

https://doi.org/10.3141/2663-07

Driver distraction represents a major safety problem in the United States. Naturalistic driving data, such as SHRP 2 Naturalistic Driving Study (NDS) data, provide a new window into driver behavior that promises a deeper understanding than was previously possible. Unfortunately, the current practice of manual coding is infeasible for large data sets such as SHRP 2 NDS, which contains millions of hours of video. Computer vision algorithms have the potential to automatically code SHRP 2 NDS videos. However, existing algorithms are brittle in the presence of challenges such as low video quality, underexposure and overexposure, driver occlusion, nonfrontal faces, and unpredictable and significant illumination changes, which are all substantially present in SHRP 2 NDS videos. This paper presents and evaluates algorithms developed to quantify high-level features pertinent to driver distraction and engagement in challenging videos like those in SHRP 2 NDS. Specifically, a novel three-stage video analysis system is presented for tracking head position and estimating head pose and eye and mouth states. The accuracy of the new head pose estimation module is competitive with the state of the art on publicly available data sets and produces good qualitative results on SHRP 2 NDS videos.

01653375

17-01419

English

pp 48–56

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309442060

Digital/other

Figures (6) ; Photos; References (38) ; Tables (2)

Algorithms; Automatic data collection systems; Data quality; Drivers; Head motion range; Image analysis; Image coding; Posture; Video

Data and Information Technology; Highways; Safety and Human Factors

TRIS, TRB, ATRI

Dec 8 2016 10:27AM

• Age-Based Simulation of Merging Behavior at Freeway Merging Ramps
• Analyzing and Modeling Drivers’ Deceleration Behavior from Normal Driving
• Comparative Analysis of Toll Plaza Safety Features in Puerto Rico and Massachusetts with a Driving Simulator
• Crash Heterogeneity: Implications for Naturalistic Driving Studies and for Understanding Crash Risks
• Drivers’ Lane-Keeping Ability in Heavy Rain: Preliminary Investigation Using SHRP 2 Naturalistic Driving Study Data
• Effects of a Change in Environment on the Minimum Time to Situation Awareness in Transfer of Control Scenarios
• Effects of Alert Cue Specificity on Situation Awareness in Transfer of Control in Level 3 Automation
• Examining Potential Confounds in Use of the Detection Response Task for In-Vehicle System Evaluation
• How to Drive Drivers Nuts: Effect of Auditory, Vibrotactile, and Multimodal Warnings on Perceived Urgency, Annoyance, and Acceptability
• Investigating Driver Behavior at Minor-Street Stop-Controlled Intersections in Qatar
• Linking the Detection Response Task and the AttenD Algorithm Through Assessment of Human–Machine Interface Workload
• Modeling Effects of Forward Glance Durations on Latent Hazard Detection
• Modeling of Vehicles Merging Movement at Unsignalized Intersections Considering Drivers’ Risk Perception
• On-Road Visual Sign Salience, Driver Attention Allocation, and Target Detection Accuracy
• Simulator Study of Involuntary Driver Distraction Under Different Perceptual Loads
• Strategies to Assist Drivers in Remaining Attentive While Under Partially Automated Driving: Verification of Human–Machine Interface Concepts