In order to realize ecological driving behavior decision-making of autonomous vehicle at intersections, the driving behavior of the vehicle without the influence of the vehicle in front was determined through empirical fuel-saving strategies. Using NGSIM dataset, combined with Gaussian Mixture Model (GMM) cluster analysis method and Support Vector Machine (SVM) theory, the driving style of the driver in front of the vehicle was identified, and a binary logistic regression-based intersection driving behavior decision model for self-driving cars was established. According to the different behaviors of decision making and considering vehicle fuel consumption, the multi-stage ecological driving optimal control issue was transformed into a full-stage optimal control issue. The paper proposed an eco-driving strategy for self-driving cars at intersections, and the effectiveness of the strategy was verified by simulation.

Eco-driving  /  Autonomous vehicles  /  Intersection  /  Behavior decision-making  /  Fuel consumption