Improving driver recognition of in-vehicle icons
Read Online
Share

Improving driver recognition of in-vehicle icons

  • 805 Want to read
  • ·
  • 50 Currently reading

Published by U.S. Dept. of Transportation, Federal Highway Administration, Research, Development, and Technology, Turner-Fairbank Highway Research Center in [McLean, Va .
Written in English

Subjects:

  • Traffic signs and signals -- United States.,
  • Traffic engineering -- United States.,
  • Design -- Human factors -- United States.

Book details:

Edition Notes

Other titlesImproving driver recognition of in vehicle icons.
ContributionsTurner-Fairbank Highway Research Center.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL15569170M

Download Improving driver recognition of in-vehicle icons

PDF EPUB FB2 MOBI RTF

Get this from a library! Improving driver recognition of in-vehicle icons.. [Turner-Fairbank Highway Research Center.;]. shown to be important in determining driver attention and cognitive state (e.g. fa-tigue) [3, 13–15, 20]. Some example studies in this category are approaches for monitoring and prediction of driver fatigue, driver head pose tracking for monitor-ing driver awareness, eye tracking and blink recognition. Affectiva Automotive AI takes driver state monitoring to the next level, analyzing both face and voice for levels of driver impairment caused by physical distraction, mental distraction, drowsiness and more. Monitor levels of driver fatigue. This enables appropriate alerts and driver assistance that help correct dangerous driving. Recognizing truck drivers with a crystal 3D truck award or a personalized truck driving recognition plaque for example will encourage better safety on the road. There’s no way to completely prevent % of accidents from happening, but using a recognition .

Driver Emotion Recognition and Real Time Facial Analysis for the Automotive Industry. They were looking for ways that emotion recognition could help improve car safety, leverage emotional data to better understand the user experience of the car driver and its passengers, and deepen understanding of the consumer for car OEM’S and suppliers.   Keep track of personal interests that drivers have, family details and ask about unique experiences on the road. Drivers will always fight to be “more than just a number’. Start a Truck Driver Recognition Program – Many carriers already do this, but everyone can always improve. Carriers recognize driver of the month, celebrating million. “Driver retention is a major issue with any company, and I truly believe the little things you do for a driver will pay off in the long run,” said Safety Officer Kevin Bergman. “Drivers love recognition and the more you recognize them, the higher they (will) think of the company.”.   In an effort to get a bit more mileage out of the recent “Truck Driver Appreciation Week” — which should ideally be more of a year-round kind of .

In Fig. 1(a), s 1 is the driver's original voice signal and s 2 is the passenger's original voice signal. The signals x 1 and x 2 are mixed signals of s 1 and s 2 through the microphones Mic1 and Mic2. To increase the voice recognition rate and recognize only the driver's voice, it is necessary to separate the desired voice signal alone from the mixed voice signal. to be provided to, comprehended, and rapidly acted upon by the driver to avoid a collision. Thus, ensuring that the DVI enables drivers to quickly and easily access needed information is of great importance with respect to driver performance. The purpose of this document is to provide Human Factors design guidance, based on the best-. The objective of this project was to develop and test a system using in-vehicle three-dimensional (3D) sound as a technique for augmenting the truck driver’s situational awareness (SA). In the driving task, vision, hearing, and the haptic senses are all used by the driver .   Abstract: This paper presents a novel approach to automated recognition of the driver's activity, which is a crucial factor for determining the take-over readiness in conditionally autonomous driving scenarios. Therefore, an architecture based on head-and eye-tracking data is introduced in this study and several features are analyzed. The proposed approach is evaluated on data recorded .