Intelligent networked vehicle lane changing method combining emergency degree and game theory

Abstract

The invention discloses an intelligent networked vehicle lane changing method combining the emergency degree and the game theory. Basedon a machine learning algorithm principle, clustering analysis is adopted to carry out emergency degree definition on a vehicle lane changing data set. The method innovatively predicts the emergency degree of the intelligent networked vehicle at the lane changing moment through an RBF radial basis function neural network, obtains an emergency degree factor, introduces the factor into a game matrix, calculates a profit value, and finally obtains a lane changing decision result. Compared with an existing intelligent networked vehicle lane changing method only depending on the game theory, the method is high in real-time decision-making performance and convenient to achieve through a computer, the emergency degree of the vehicle lane changing moment can be quantified, the execution efficiency is higher and the decision-making accuracy is better during lane changing decision making, the defect that a traditional game theory lane changing model is not high in adaptability is overcome, theoretical and technical support can be provided for an intelligent networked vehicle lane change collision early warning system and an autonomous lane change decision system.

Description

technical field [0001] The invention relates to a lane-changing method for an intelligent networked vehicle combining urgency and game theory, and belongs to the technical field of intelligent driving. Background technique [0002] In order to reduce traffic accidents caused by human factors and improve road traffic efficiency, intelligent networked vehicles have emerged as the times require. While freeing drivers from heavy driving tasks, they also reduce the traffic safety accident rate caused by improper human operation. Among the various behavioral decisions of intelligent networked vehicles including acceleration, deceleration, U-turn, lane change, car following, and parking, lane-changing decision-making has always attracted the attention and research of scholars because of the complexity of the process and the criticality of the decision-making. At this stage, some researchers regard the lane-changing behavior of vehicles as a game between participants, use the inform...

AI Technical Summary

Problems solved by technology

However, most existing lane-changing models mainly rely on vehicle kinematics to construct equations when making decisions, and make judgments based on the relative distance and relative speed between the vehicle and the surrounding environment vehicles, and seldom consider the urgency of lane-changing. The impact on safety makes the vehicle's decision-making efficiency low in the face of an emergency, and it is prone to "dare not" to change lanes and miss the best time to change lanes

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