# Biform Game Approach to Strategy Optimization of Autonomous Vehicle Lane Changes on Highway Ramps
## 1 Introduction
[17] incorporated pedestrian behavior into the game decision-making. [PERSON] et al. [18] analyzed the traffic control strategies of connected and automated vehicles at intersections. [PERSON] et al. [19] studied the game-theoretic model of driver fatigue detection and intervention in a connected vehicle environment. [PERSON] et al. [20] proposed an efficient cooperative lane-changing strategy for intelligent connected vehicles using game theory, effectively improving road traffic efficiency and driving safety. [PERSON] et al. [21] established a vehicle routing model in a connected vehicle environment using game theory, comprehensively considering traffic congestion, road conditions, and the mutual influence between vehicles to provide a more reasonable vehicle routing plan. These research results are remarkable, and a variety of models have been established, providing support for traffic management and automotive manufacturing. However, they also faced challenges such as the difficulty in balancing model complexity and computational efficiency, and the difficulty in fully incorporating complex actual scenarios into the model. Future research can be carried out in directions such as multi-agent collaborative optimization, integration with emerging technologies, and strengthening the empirical research and verification to give full play to the potential of game theory and connected vehicle technology in the transportation field and promote its intelligent and efficient development. Many scholars both domestically and internationally have conducted research on the lane-changing problem as autonomous vehicles are about to enter highway ramp entrances. [PERSON] et al., macroscopically characterized the behavior of connected autonomous vehicles (CAVs) on dedicated autonomous driving lanes when exiting ramps, proposing a safety risk-based CAV exit ramp lane-changing decision model under mixed traffic flow [22]. [PERSON] et al. [23] proposed a CAV autonomous lane-changing method based on the deep reinforcement learning DDQN algorithm to address traffic conflict issues of mainline connected autonomous vehicles in the merging area. These algorithms and models study the lane-changing problem from a macroscopic perspective, requiring substantial data support, which may lead to insufficient training data or inadequate generalization capabilities. In contrast, a lane-changing model at highway ramp entrances based on game theory can make microscopic lane-changing decisions by considering the mutual influence between and strategic choices of vehicles. Therefore, this study constructed a lane-changing decision model for autonomous vehicles at highway ramp entrances based on game theory. Many scholars have conducted in-depth research using non-cooperative game theory to improve the traffic flow efficiency and economic aspects of lane changes of autonomous vehicles. [PERSON] et al. [24] proposed a coordinated lane-changing model based on the Stackelberg game, using non-cooperative game theory to describe the interactions between human-driven and autonomous vehicles. [PERSON] et al. [25] discussed a non-cooperative mixed strategy game between two adjacent lanes, deriving the interests of the participants and subsequently establishing a lane-changing decision model based on this game theory. [PERSON] et al. [26] employed a mixed fuzzy multi-player multi-objective non-cooperative game method to establish a lane selection model for urban expressways, analyzing the payoff values of individuals using different lane selection strategies to derive the optimal lane selection strategy. [PERSON] et al. [4] established a non-cooperative game model for lane-changing behavior among autonomous vehicles, using the vehicles' own driving states as game payoffs, ultimately seeking lanes with better driving conditions. [PERSON] et al. [27] established a vehicle lane-changing cooperation game model based on capturing and analyzing the characteristics of drivers' different lane-changing behaviors and driving expectations.