# Biform Game Approach to Strategy Optimization of Autonomous Vehicle Lane Changes on Highway Ramps
## 5 Conclusions  
This study constructed a lane-change strategy model for autonomous vehicles on high-speed ramp sections based on biform game theory, focusing on the effects of the relationship between the lane-changing vehicle and the affected vehicles in the target lane under the premise of forced lane changes, which involves both competition and cooperation. The following conclusions were drawn:  
1. Based on lane-changing using V2V communication in the Internet of Vehicles with multi-vehicle interactions, a speed estimation model for adjacent vehicles during the lane-changing process was established. Firstly, a lane-changing model was designed. The impacts of the head-distance and speed change of the lane-changing vehicle on the lane-changing time, as well as the relationship between the number of lane-changes and the difference in lane density, were analyzed. Then, the relationship between the  
Figure 4: Trends in optimal stable speed changes of three vehicles when the speed probability of the following vehicle remains unchanged (Red is the ahead car speed, green is the lane-changing car speed, and blue is the rear car speed). vehicle speed change and the inter-vehicle distance was quantitatively described, and the data caliber for the interactive vehicle speed change was given. Subsequently, a simulation program was designed. The speed changes of adjacent vehicles at three different positions around the lane-changing vehicle were calibrated, and a uniform probability distribution function was given. The lane-changing behaviors under different initial speeds and numbers of vehicles indicate that the speed change of vehicles at different positions has different sensitivities. Finally, an update criterion for the speed of autonomous vehicles entering the ramp section was given. Under the premise of ensuring safe passage through the ramp entrance, the comfort of autonomous vehicles was adjusted, and the driving efficiency was effectively improved. 2. In this study, this biform game model was applied to the analysis of traffic problems for the first time. An autonomous driving lane-changing model based on the Internet of Vehicles was constructed. The speed gain of each vehicle was taken as the revenue value. Under the condition of ensuring the safe driving of vehicles and enabling them to obtain their maximum benefits, a pure-strategy Nash equilibrium solution that satisfies the necessary and sufficient conditions was finally obtained, enabling the lane-changing vehicle and the affected vehicles in the target lane to adjust to the optimal equilibrium speed in a timely manner. 3. Through the research and analysis of numerical examples, it was verified that the biform game model can be effectively applied to the autonomous vehicle lane-changing model. It makes up for the fact that cooperative games only consider the formation of alliances and revenue distribution during vehicle lane-changing while ignoring the distribution and selection of lane-changing strategies. At the same time, it also makes up for the fact that non-cooperative games only analyze the layout and selection of lane-changing strategies while ignoring the alliance cooperation and revenue distribution of affected autonomous vehicles. 4. In this study, the biform game model was used to adjust the probability of a speed change of the vehicles participating in lane-changing, and the impacts of the lane-changing probability and speed change on the optimal stable speed were analyzed. The results are in line with the actual situation of mutual influence among lane-changing vehicles in the Internet of Vehicles, indicating that the model has good applicability. However, this study has limitations. It only considered the benefit values of the lane-changing vehicle and the vehicles in front and behind in the target lane, and did investigate the timeliness, hysteresis, heterogeneity, and stability of the overall traffic flow caused by the lane-changing.