Research on Drivers’ Cognitive Level at Different Self-explaining Intersections

Wuhong Wang*, Shanyi Hou, Xiaobei Jiang, Qian Cheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

One demand for road is the ensurance of self-explaining, under which means road users can make correct subjective classifications and expectations of road environment. Based on quantification of driver’s driving cognitive behavior and the self- explaining road theory, this paper designs road environments with different self-interpretation levels as experimental scenes. Through a driving simulation experiment, the changing process of driver’s cognitive workload level is simulated based on Hidden Markov Model. The Hidden Markov Model identifies the driving intention under the combined working conditions, thereby judging driving awareness of the road environment, and evaluating the self-interpretation level of each experimental scene.

Original languageEnglish
Title of host publicationGreen, Smart and Connected Transportation Systems - Proceedings of the 9th International Conference on Green Intelligent Transportation Systems and Safety, 2018
EditorsWuhong Wang, Xiaobei Jiang, Xiaobei Jiang, Martin Baumann
PublisherSpringer
Pages827-835
Number of pages9
ISBN (Print)9789811506437
DOIs
Publication statusPublished - 2020
Event9th International Conference on Green Intelligent Transportation Systems and Safety, 2018 - Guilin, China
Duration: 1 Jul 20183 Jul 2018

Publication series

NameLecture Notes in Electrical Engineering
Volume617
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference9th International Conference on Green Intelligent Transportation Systems and Safety, 2018
Country/TerritoryChina
CityGuilin
Period1/07/183/07/18

Keywords

  • Double HMM
  • Drivers’ cognitive level
  • Self-interpretation feature
  • Urban road intersection

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