Drivers are more accepting of on-road dynamic messages/warnings than in-vehicle message/warnings.

In Beijing a driving simulator study assesses driver acceptance of warning messages from competing interfaces.

Beijing; China

Summary Information

Connected vehicle and infrastructure technology can now provide drivers with Basic Safety Messages (BSMs) either via in-vehicles cars or on-road signs. These BSMs can warn drivers of things like inclement weather conditions, work zone alerts, speed limit changes etc. However, whether on-road or vehicle BSMs are more effective debated.


To help answer this question researchers at Beijing Key Laboratory of Traffic Engineering tested user acceptance of on-road signs and in-vehicle warnings.
Researchers setup a driving simulator and had 43 different participants (27 males and 16 females with a mean age of 35 years) drive the simulator under light and heavy fog conditions. Drivers were shown both on-road and in-vehicle fog warnings during different simulator runs. After the simulations researchers surveyed the drivers about the "perceived usefulness" and "perceived ease of use" of the different types of warning systems. Finally, researchers used established heuristic methods to calculate an index score of "acceptance" for each of the different technologies.

  • Average acceptance of on-road warning systems (73 percent) is higher than average acceptance of in-vehicle warning system (64 percent)
  • Drivers likely prefer on-road warning because they "[like] to get [messages] head up rather than [look] at [a display screen] in the cab"
  • Engineers should pay attention to the "simplicity and directness" of messaging systems.

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Technology Acceptance Comparison between on-Road Dynamic Message Sign and on-Board Human Machine Interface for Connected Vehicle-Based Variable Speed Limit in Fog Area

Author: Li, Jia; Wenxiang Xu; and Xiaohua Zhao

Published By: Journal of Intelligent and Connected Vehicles

Prepared by the Beijing University of Technology

Source Date: 09/18/2019

Other Reference Number: ISSN: 1947-1017



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Benefit ID: 2019-01409