# Biform Game Approach to Strategy Optimization of Autonomous Vehicle Lane Changes on Highway Ramps
## 2 Lane-Change Model for Autonomous Vehicles on Highways
### Analysis of Lane-Change Behavior  
This paper mainly focuses on lane-changing behaviors at the entrance ramps of highways. By establishing a biform game model for conflicting or collaborating vehicles in this area, and calculating the payoffs, an optimal lane-changing speed adjustment strategy can be selected. The driving entities in this model are intelligent connected vehicles. Lane-changing vehicles perceive the surrounding environment through advanced devices such as vehicle-mounted sensors and GPS positioning devices. Vehicles interact with each other by exchanging important information such as their positions, speeds, and accelerations through intelligent vehicle networking technology. Lane-changing vehicles make lane-changing decisions based on the driving states of the surrounding vehicles, ensuring safe lane-changing. This study primarily studied the lane-changing behavior of autonomous vehicles in high-speed ramp areas, establishing a biform game model for conflicting or cooperating vehicles in these areas. By calculating the payoff, it ultimately selects the optimal lane-changing speed adjustment strategy. The driving entities involved in this study are all autonomous vehicles, which have two major characteristics: first, they can obtain real-time information about the surrounding vehicles, such as relative position, speed, and acceleration; second, they can quickly and accurately understand their current motion state and predict their motion trends in a short period. Lane-changing vehicles can interact with all vehicles entering the lane-changing area, using the dynamic information of vehicles in front of and behind in target lane as input variables for the model. The model integrates predictions to derive the motion data for the next moment. When a vehicle is approaching a certain point before the ramp, to avoid missing the ramp and diminishing the traffic capacity, it must perform a forced lane change. Therefore, this study focused on lane-changing behavior under the premise of forced lane changes. This model assumes that lane-changing behaviors only occur in safe scenarios, meaning that lane-change operations will only be conducted when there is sufficient space in the target lane, and speed adjustment decisions are influenced by the distance between vehicles, resulting in instantaneous changes.  
The typical scenario discussed in this paper involves a highway segment approaching an exit ramp. It is assumed that the longitudinal speed of the lane-changing vehicle is v\({}_{0}\), and at the initial time t\({}_{0}=0\), during the lane change process, the vehicle can adjust to a higher speed to enhance the road-throughput capacity.  
Therefore, in this study, the research location was set as near the ramp of a two-lane highway, and the lane-changing vehicle is located in the outer lane. To eliminate the influence of other factors, the following assumptions were made when studying the lane-changing scenario at the highway ramp section:  
1. The research scenario is limited to the highway ramp section (not involving other scenarios), and the road environment remains unchanged during the research period.
2. The research scenario is not affected by the density of the macroscopic traffic flow. The traffic conditions and facilities of each lane are the same, and the traffic flow remains relatively stable.
3. Vehicles perform two driving behaviors: lane-changing and car-following. The vehicle performance remains stable during the lane-changing process, and the final lane-changing speed and the lane-changing avoidance speed of the vehicle are not affected by the lane.
4. All vehicles are Internet-connected vehicles, and the influence of non-motor vehicles and pedestrians is not considered.  
All vehicles can determine their own positions, receive information transmitted by other vehicles, and the network communication remains unobstructed.  
Abnormal traffic conditions are not considered in the research scenario.