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Proceedings Paper

On an efficient and effective intelligent transportation system (ITS) safety and traffic efficiency application with corresponding driver behavior
Author(s): Nnanna Ekedebe; Wei Yu; Chao Lu
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Paper Abstract

Driver distraction could result in safety compromises attributable to distractions from in-vehicle equipment usage [1]. The effective design of driver-vehicle interfaces (DVIs) and other human-machine interfaces (HMIs) together with their usability, and accessibility while driving become important [2]. Driving distractions can be classified as: visual distractions (any activity that takes your eyes away from the road), cognitive distraction (any activity that takes your mind away from the course of driving), and manual distractions (any activity that takes your hands away from the steering wheel [2]). Besides, multitasking during driving is a distractive activity that can increase the risks of vehicular accidents. To study the driver’s behaviors on the safety of transportation system, using an in-vehicle driver notification application, we examined the effects of increasing driver distraction levels on the evaluation metrics of traffic efficiency and safety by using two types of driver models: young drivers (ages 16-25 years) and middle-age drivers (ages 30-45 years). Our evaluation data demonstrates that as a drivers distraction level is increased, less heed is given to change route directives from the in-vehicle on-board unit (OBU) using textual, visual, audio, and haptic notifications. Interestingly, middle-age drivers proved more effective/resilient in mitigating the negative effects of driver distraction over young drivers [2].

Paper Details

Date Published: 5 June 2015
PDF: 15 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94741F (5 June 2015); doi: 10.1117/12.2177505
Show Author Affiliations
Nnanna Ekedebe, Towson Univ. (United States)
Wei Yu, Towson Univ. (United States)
Chao Lu, Towson Univ. (United States)


Published in SPIE Proceedings Vol. 9474:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV
Ivan Kadar, Editor(s)

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