The original use of the vehicle dashboard was to provide enough sensory information to inform the driver of the current engine and vehicle status and performance. Over time, it has evolved into an entertainment system that includes person-to-person communication, global positioning information, and the Internet, just to name a few. Each of these new features adds to the amount of information that drivers must absorb, leading to potential distraction and possible increases in the number and types of accidents. In order to provide an overview of these issues, this paper summarizes previous work on driver distraction and workload, demonstrating the importance of addressing those issues that compete for driver attention and action. In addition, a test platform vehicle is introduced which has the capability of assessing modified dashboards and consoles, as well as the ability to acquire relevant driving performance data. Future efforts with this test platform will be directed toward helping to resolve the critical tug-of-war between providing more information and entertainment while keeping drivers and their passengers safe. The long-term goal of this research is to evaluate the various technological innovations available for inclusion in the driving environment and determining how to optimize driver information delivery without excessive distraction and workload. The information presented herein is the first step in that effort of developing an adaptive distraction/workload management system that monitors performance metrics and provides selected feedback to drivers.The test platform (1973 VW Beetle converted to a plug-in series hybrid) can provide speed, location (GPS), 3-D acceleration, and rear proximity detection. The test drive route was a 2 km × 3 km city street circuit which took approximately 25 minutes to complete. Data is provided herein to demonstrate these capabilities. In addition, the platform has driver selectable layouts for the instrument cluster and console (LCD screens). The test platform is planned for use to determine driver preferences (e.g., dashboard/console configurations) and attention performance in addition to identifying optimal real-time feedback for drivers with different demographics.Copyright © 2013 by ASME
[1]
Paul Green,et al.
Driver Interface/HMI Standards to Minimize Driver Distraction/Overload
,
2008
.
[2]
Eun-Ha Choi,et al.
Crash Factors in Intersection-Related Crashes: An On-Scene Perspective
,
2010
.
[3]
Thomas A. Dingus,et al.
The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data
,
2006
.
[4]
Man Ho Kim,et al.
On-road assessment of in-vehicle driving workload for older drivers: Design guidelines for intelligent vehicles
,
2011
.
[5]
Paul Green,et al.
Driver Distraction, Telematics Design, and Workload Managers: Safety Issues and Solutions
,
2004
.
[6]
Christopher D. Wickens,et al.
Multiple Resources and Mental Workload
,
2008,
Hum. Factors.
[7]
Paul Atchley,et al.
Potential Benefits and Costs of Concurrent Task Engagement to Maintain Vigilance
,
2011,
Hum. Factors.
[8]
Paul Atchley,et al.
Conversation Limits the Functional Field of View
,
2004,
Hum. Factors.
[9]
Richard A. Young,et al.
Cognitive Distraction While Driving: A Critical Review of Definitions and Prevalence in Crashes
,
2012
.
[10]
Austin Joseph Hausmann,et al.
Advances in Electric Drive Vehicle Modeling with Subsequent Experimentation and Analysis
,
2012
.
[11]
Christopher Depcik,et al.
A Sustainable Approach to Advanced Energy and Vehicular Technologies at the University of Kansas
,
2009
.
[12]
Omer Tsimhoni,et al.
Address Entry While Driving: Speech Recognition Versus a Touch-Screen Keyboard
,
2004,
Hum. Factors.