@inproceedings{ea8b21d2c9f04d1f8044ceaeb8a91fd8,
title = "Application Evaluation of Self-explaining Intersections Based on Visual Information",
abstract = "More than 80% of the traffic information acquired by drivers comes from the visual channel. Therefore, visual information provided by traffic environment has great effects on driving safety. Data of drivers' driving trajectories, driving velocities, and eye movement characteristics in experimental scenes with different traffic environmental visual information and levels of self-explaining characteristic were collected via a driving simulator and eye tracker experiment. This paper analyzed the effects of visual information on drivers' behaviors, evaluated the safety of self-explaining roads, and designed the methods to improve the capacity of fault tolerance and safety of the self-explaining intersection.",
keywords = "Driving Safety, Self-explaining Feature, Visual Information",
author = "Wuhong Wang and Shanyi Hou and Xiaobei Jiang and Leyi Wang and Qian Cheng",
note = "Publisher Copyright: {\textcopyright} 2020 American Society of Civil Engineers.; International Conference on Transportation and Development 2020: Transportation Safety, ICTD 2020 ; Conference date: 26-05-2020 Through 29-05-2020",
year = "2020",
language = "English",
series = "International Conference on Transportation and Development 2020: Transportation Safety - Selected Papers from the International Conference on Transportation and Development 2020",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "83--94",
editor = "Guohui Zhang",
booktitle = "International Conference on Transportation and Development 2020",
address = "United States",
}