Using Perceptual Evaluation to Quantify Cognitive and Visual Driver Distractions

Chapter

Abstract

Developing feedback systems that can detect the attention level of the driver can play a key role in preventing accidents by alerting the driver about possible hazardous situations. Monitoring drivers’ distraction is an important research problem, especially with new forms of technology that are made available to drivers. An important question is how to define reference labels that can be used as ground truth to train machine-learning algorithms to detect distracted drivers. The answer to this question is not simple since drivers are affected by visual, cognitive, auditory, psychological, and physical distractions. This chapter proposes to define reference labels with perceptual evaluations from external evaluators. We describe the consistency and effectiveness of using a visual-cognitive space for subjective evaluations. The analysis shows that this approach captures the multidimensional nature of distractions. The representation also defines natural modes to characterize driving behaviors.

Keywords

Driver distraction Active safety Driver perception Subjective evaluation Driving performance 

Notes

Acknowledgment

The authors would like to thank Dr. John Hansen for his support with the UTDrive Platform. We want to thank the Machine Perception Lab (MPLab) at The University of California, San Diego, for providing the CERT software. The authors are also thankful to Ms. Rosarita Khadij M Lubag for her support and efforts with the data collection.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.The University of Texas at DallasRichardsonUSA

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