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- W2608822015 abstract "The development of autonomous vehicles provides effective solutions and opportunities for reducing the probability of traffic accidents. However, because of technical limitations and economic and social challenges, achieving fully autonomous driving is a long-term endeavor. One principal research question is how to choose the suitable driving mode of an intelligent vehicle during stressful traffic events. For this purpose, an on-road experiment with 22 drivers was conducted in Wuhan, China; multisensor data were collected from the driver, the vehicle, the road, and the environment. Driving modes were classified into three categories on the basis of the driver’s self-reported records, and two physiological indexes that use the k-means cluster method were adopted to calibrate the self-reported driving modes. A feature-ranking algorithm based on the information gained was adopted to identify significant factors, and a driving mode decision-making model was established with the multiclass support vector machi..." @default.
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- W2608822015 date "2017-01-01" @default.
- W2608822015 modified "2023-09-23" @default.
- W2608822015 title "Driving Mode Decision Making for Intelligent Vehicles in Stressful Traffic Events" @default.
- W2608822015 hasPublicationYear "2017" @default.
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