A Cognitive Computational Model of Driver Warning Response Performance in Connected Vehicle Systems

Yiqi Zhang, Changxu Wu, Chunming Qiao, Adel Sadek, Kevin F. Hulme

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Most existing driver models focus on predicting driving performance in normal and near-collision situations without considering the impact of collision warning parameters on driver behavior. This study develops a cognitive computational driver model based on the Queueing Network-Model Human Processor (QN-MHP) to quantify the effects of key warning parameters (i.e., warning lead time, warning reliability, and speech warning style) on driver performance in warning responses, in connected vehicle systems (CVSs). The model was validated by comparing its predictions of driver response time, response type, and braking and steering performance with data from thirty-two drivers collected in an experimental study. Once the route choice mechanism had been implemented, the driver model was found to explain the cognitive mechanism underlying how drivers process warnings in CVSs. Indeed, the validation results showed that the model was able to capture major changes in patterns of the experimental data, with R-squared values of 0.88 for warning response time, 0.69 and 0.65 for decision making in response type for the initial trial and across trials, 0.85 for braking performance, and 0.83 for steering performance. The model can be applied to optimize the interface design of CVSs based on driver needs.

Original languageEnglish (US)
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
StatePublished - Sep 1 2022

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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