A new risk estimation model of bayesian network for adapting to driving environment changing

Zhong Zhang, Taira Furuichi, Takuma Ueda, Takuma Akiduki, Tomoaki Mashimo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In recent years, research on automated driving of automobiles is being promoted, and accidents caused by human error by driving support systems are also expected to decrease. However, most of the accidents occur because the risk that the driver feels subjectively is too small. Therefore, to reduce the number of traffic accidents, it is necessary to raise danger perception while driving. There are two kinds of risk in the driving environment: the subjective risk felt by the driver and the objective risk existing in the driving environment. In this research, we construct a model to estimate each risk value by using two pieces of information: traffic environment information obtained from the front image of the vehicle and driving operation information of the driver. Furthermore, by combining them the risk of adapting to the driving environment is determined, and acts to raise drivers’ perception of danger.

Original languageEnglish
Pages (from-to)515-521
Number of pages7
JournalICIC Express Letters, Part B: Applications
Volume10
Issue number6
DOIs
Publication statusPublished - Jun 2019
Externally publishedYes

Keywords

  • Bayesian network
  • Driving support
  • Hazard estimation
  • Objective risk
  • Subjective risk

ASJC Scopus subject areas

  • General Computer Science

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