# Biform Game Approach to Strategy Optimization of Autonomous Vehicle Lane Changes on Highway Ramps
## 1 Introduction
Therefore, in order to improve road traffic safety and achieve the smooth operation of the entire highway traffic flow, the effective identification of abnormal lane-changing behaviors of vehicles is of great practical significance [6]. Therefore, research on lane-changing at highway entrance ramps is especially important. Research on lane-changing decision-making of autonomous vehicles in a mixed-traffic-flow environment can be classified into three main types: 1 rule-based lane-changing models; 2 neural-network-based lane-changing models; and 3 game-theory-based lane-changing models. Rule-based lane-changing models mainly rely on pre-set rules and logic to make lane-changing decisions. However, this approach often ignores real-time traffic conditions and the behaviors of other vehicles, and thus cannot flexibly handle complex lane-changing situations. Neural-network-based lane-changing models use neural networks to learn and predict vehicle lane-changing behaviors. Nevertheless, neural-network-based models may suffer from problems such as insufficient training data or a poor generalization ability, resulting in poor performance in complex traffic environments. Both rule-based and neural-network-based lane-changing models mainly focus on processingparameters such as position and speed, which have certain limitations and cannot fully balance the safety and fairness of vehicles. In contrast, game-theory-based lane-changing models better address these issues. By considering the mutual influence and strategic choices among vehicles, they provide a more comprehensive approach to lane-changing decision-making. Here, we review the research of the influence of game theory on driving patterns using connected vehicle technology. Connected vehicle technology brings new opportunities for traffic development through the information exchange between vehicles and the outside world, and game theory can effectively analyze the driving-behavior decisions of drivers. In terms of driving-behavior decision-making modeling, [PERSON] et al. [7] proposed a non-cooperative game-theoretic driving decision-making model in their published study, starting from the perspective of individual driver decisions and laying the foundation for subsequent research. [PERSON] et al. [8] introduced a dynamic game model to simulate the process of drivers adjusting their decisions in real time, which is more in line with the dynamic changes in actual driving scenarios. [PERSON] et al. [9] constructed a game model from the perspective of driver risk preferences and analyzed the decision-making differences between drivers with different risk preferences in a connected vehicle environment, providing a new perspective for the research on personalized driving patterns. In terms of resource allocation and cooperation mechanisms, [PERSON] et al. [10] used game theory to design a vehicle communication resource allocation model to solve the problem of communication resource allocation in connected vehicles. [PERSON] et al. [11] focused on the vehicle energy cooperation problem and proposed a strategy based on cooperative game theory. [PERSON] et al. [12] explored the computational resource allocation strategy in connected vehicles and used game theory to solve the problem of insufficient computational resources when vehicles are performing complex data processing, which is of great significance for improving the overall performance of the connected vehicle system. Safety and conflict avoidance have also been research focuses. [PERSON] et al. [13] analyzed vehicle avoidance decisions through game theory, while [PERSON] et al. [14] explored multi-vehicle conflict avoidance. [PERSON] et al. [15] studied the safe game strategies of vehicles in a connected vehicle environment under adverse weather conditions. Considering the special impact of adverse weather on vehicle driving safety, they proposed more targeted safe driving strategies. In addition, [PERSON] et al. [16] enhanced the game model with reinforcement learning to optimize the driving pattern. [PERSON] et al.