Shai Fine, Yishay Mansour
Machine Learning
XVC (eXtensible Viewer Composition) is an in-vehicle user interface framework for telematics applications. It provides a document-oriented application model, which enables drivers to simultaneously make use of multiple information services, while maintaining satisfactory control of their vehicles. XVC is a new client model that makes use of the beneficial functions of in-vehicle navigation devices. This paper presents the results from usability tests performed on the XVC framework in order to evaluate how the XVC client affects drivers' navigation while using its functions. The evaluations are performed using the Advanced Automotive Simulator System located at KATECH (Korea Automobile Technology Institute). The advantages of the XVC framework are evaluated and compared to a non-XVC framework. The test results show that the XVC framework navigation device significantly reduces the scanning time needed while a driver obtains information from the navigation device. Copyright© 2010.
Shai Fine, Yishay Mansour
Machine Learning
Ran Iwamoto, Kyoko Ohara
ICLC 2023
P.C. Yue, C.K. Wong
Journal of the ACM
Gaku Yamamoto, Hideki Tai, et al.
AAMAS 2008