United States Department of Transportation
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Effectiveness of Various Information Channels on User Training and Learning in Automobiles
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2016-04-07
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By Lee, Chaiwoo
[PDF-769.20 KB]
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TRIS Online Accession Number:01701452
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Edition:Year 25 Final Report
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Abstract:A number of advanced driver assistance systems are increasingly being implemented into the automobile, and many are now standard in cars that are newly manufactured. These in-vehicle technologies are designed to make driving safer and easier for those who operate the vehicle, as well as making the road safer for other users. In addition to these existing technologies, many automobile manufacturers and technology companies are also working to develop fully automated vehicles, or self-driving cars, which have the potential to relieve the person from manual operation of the vehicle and make the car and its software responsible for sensing, navigation and control. Technological advancements in the automobile are continued to be tested for performance. However, less is known about how people understand, perceive and accept various features and their potential effects. Also, it is unclear how people currently learn about the advanced features in their cars, if at all, and what methods are more effective for learning and training. In Abraham et al. (2017) it was stated that many drivers have only limited, and often inaccurate, understanding of the advanced systems in their cars, which can reduce the potential benefits of such systems. Furthermore, they found that many automakers don’t place a high priority on consumer education and that their dealerships are ill-equipped to offer information about their safety features. The objective of this study is to investigate how different methods of information presentation can be used to enhance drivers’ understanding and acceptance of advanced driver assistance systems in the automobile. An online experiment explored differences between three information media – video-based, text-based and text with accompanying images. This study also employed factors identified by the Technology Acceptance Model (Davis et al., 1989), shown in Figure 1, to understand how the information presented to drivers affect their perceptions, attitudes and behavioral intentions to accept and adopt in-vehicle technologies and the self-driving car.
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