# Biform Game Approach to Strategy Optimization of Autonomous Vehicle Lane Changes on Highway Ramps
## 1 Introduction  
In recent years, the rapid increase in the number of vehicles on urban roads has made issues such as traffic congestion and traffic accidents a primary focus of research in the field of transportation. The continuous advancement of intelligent transportation technology is a major strategy for addressing these traffic problems. The technological development of autonomous vehicles has become a significant goal of intelligent transportation technology, as traditional manually driven vehicles can no longer meet the current demands of transportation development and are also a primary medium for traffic accidents. Currently, China is in the initial stage of \"vehicle-road-cloud integration\". The development of the connected vehicle industry will be advanced in accordance with short-term and mediumto long-term goals, achieving an upgrade from single-vehicle intelligence to connected intelligence, and ultimately enabling broader applications of autonomous driving. For example, Baidu Apollo's \"Luo bo Kuai pao\" project deeply integrates cloud computing platforms and edge computing technologies to support driverless cars in achieving real-time perception, path planning, and dynamic decision-making in complex traffic environments, thereby enhancing traffic efficiency and safety. Therefore, the development of autonomous driving technology is particularly important. In the driving process based on the Internet of Vehicles environment, the prediction of lane-changing trajectories for autonomous vehicles is crucial for traffic safety, traffic efficiency, and the traffic environment [1]. It requires the ability to predict the trajectories of surrounding vehicles in order to make reasonable decision-making plans, thereby enhancing safety and comfort during driving [2]. Through vehicle-to-vehicle (V2V) communication in the Internet of Vehicles, cars can exchange position, speed, and route information in real time, thereby predicting and avoiding potential collision risks. V2V communication enables vehicles to receive information from other vehicles as well as traffic lights and roadside sensors to optimize driving strategies. For example, adjusting the vehicle speed to adapt to changes in traffic flow, reducing the number of stops and decelerations, reducing fuel consumption and exhaust emissions, and reducing the incidence of safety accidents. According to statistics published by the U.S. National Highway Traffic Safety Administration, traffic safety accidents caused by improper lane-changing behavior account for as much as 27% of traffic safety incidents [3]. In particular, in highway scenarios, poor control of vehicle speed at merging points on high-speed ramps is a significant cause of traffic accidents, which affects road operational safety. In addition to dangerous driving behavior, the driver's misjudgment of driving behavior is also an important cause of traffic accidents [4]. Among them, lane-changing behavior needs to consider not only the current driving state of the vehicle, but also other vehicles and the current driving environment. It is a complex dynamic decision-making process [5]. Lane changes are one of the actions that lead to traffic accidents, highlighting the complexity and uncertainty of lane-change behavior. In addition to the influence of the road environment, there is also the influence of drivers and vehicles, and abnormal behaviors under the coupling of various factors are highly likely to cause instability, and uncontrollable or potentially risky behaviors of the vehicles themselves, which will greatly affect the highway traffic capacity and controllability. It may even affect the steady state of traffic flow upstream, downstream, and beyond, or even cause the paralysis of a large area of the traffic network or traffic congestion, preventing the entire expressway trunk line from running in an orderly manner.