Research On Lane-changing Model Of Intelligent Connected Vehicles Combined With Urgency Degree And Game Theory

Lane changing is one of the most common traffic behaviors,and intelligent connected vehicles are considered to be an effective means to solve traffic congestion and reduce safety accidents in the future.The lane changing model is a key component of the intelligent connected vehicles behavior decision-making system,and it plays a vital role in the safe driving of vehicles.In the process of lane changing,vehicles will face different degree of lane changing urgency according to the traffic conditions on the road,and the urgency degree is an important factor affecting the vehicle lane changing.Therefore,the research on the lane changing model of intelligent connected vehicles plays an important role in the traffic safety,highway included.Lane changing is a process of competition and cooperation between vehicles,which can be regarded as a game.Therefore,it is feasible to study the lane changing model of intelligent connected vehicles based on game theory.The lane changing model based on game theory has gradually become a hotspot of scholars in further research.However,at this stage,the lane changing model based on game theory pay less attention to the urgency degree of lane changing,which makes intelligent connected vehicles less efficient in making lane changing decisions when facing an emergent situations.This thesis studies the lane changing model of intelligent connected vehicles combined with urgency degree and game theory,and mainly does the following work:(1)The paper studies the lane changing model based on game theory.Starting from the influencing factors of vehicle lane changing on highway,the paper analyzes the lane changing behavior of vehicles and introduces the theoretical basis of game theory.Combined with the vehicle lane changing scene,a vehicle lane changing model based on game theory is constructed and solved.(2)The paper proposes a method for predicting the urgency degree of changing lanes.In order to complete the data preparation work required by this method,the lane changing data in the highway trajectory data set was extracted in this paper,and the validity of lane changing data is verified by the normality test.The paper uses programming to mine the feature information of lane changing data,and on this basis,proposes a method for predicting the urgency degree of lane changing based on machine learning.(3)The paper constructs a lane changing model of intelligent connected vehicles combined with urgency degree and game theory.In order to achieve this goal,the paper uses the feature information of lane changing data,K-Means clustering algorithm and radial basis function neural networks to realize the prediction method of lane changing urgency degree.The paper determined the safety lane changing restrictions for intelligent connected vehicles,and based on the lane changing model based on game theory combines with urgency degree,and construct a lane changing model of intelligent connected vehicles combined with urgency degree and game theory.(4)The paper builds a carla-based intelligent connected vehicles lane changing simulation system.The system is developed by using Carla’s Python API,which achieved the lane changing function combined with urgency degree and game theory.Three different simulation scenes were designed in this system.The paper uses this system to simulate and analyze the lane changing model of intelligent connected vehicles combined with urgency degree and game theory.The analysis results show that the lane changing model of intelligent connected vehicles combined with urgency degree and game theory is more adaptable to the lane changing scene than the lane changing model based on game theory when making lane changing decisions,and it also has higher lane change efficiency and safety of lane change decision.