Driving Mode Decision Making for Intelligent Vehicles in Stressful Traffic Events

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 machine algorithm. The results indicated that the SD of the front wheel angle, driver experience, vehicle speed, headway time, and acceleration had significant effects on the driving mode decision making. The driving mode decision-making model demonstrated a high predictive power with a prediction accuracy of 0.888 and area under the curve values of 0.918, 0.91, and 0.929 for the receiver operating characteristic curves. The conclusions provide theoretical support for decision making by the controller of a semiautomated vehicle.