Automatic lane change is one of the most important highway operations. It seriously affects traffic efficiency and safety. It is also an important driving technology for automatic driving. To achieve the best automatic lane-change control, it is necessary to achieve the control from the perspective of multi-objective evaluation. In this paper, to make it applicable for a hybrid condition of car following and lane change, the traditional car-following model is modified by regarding the longitudinal motion during the lane-changing process as a transition of the car-following behavior in the two lanes before and after a certain lane-change behavior. A hyperbolic tangent transition function is introduced to connect the model to achieve a smooth transition of the model output. Then, the discretionary lane-change decision process of highway autonomous vehicles is modeled into a two-vehicle game model, and a comprehensive loss function concerning safety, efficiency, and ride comfort is proposed for the evaluation of the strategies. The optimal strategy is obtained by minimizing the expectation of losses. Finally, to verify the performance of the proposed new model, simulations of different car-following and lane-changing models are carried out, which is for multi-target simulation conditions. The results of the simulation show that the new model exhibits higher traffic efficiency, better homogeneity, and stability.

自动变道是最重要的高速公路运营之一。它严重影响交通效率和安全性。它也是自动驾驶的重要驱动技术。为了实现最佳的自动换道控制，有必要从多目标评估的角度来实现控制。在本文中，为了使其适用于跟随和变道的混合条件，通过将变道过程中的纵向运动视为两个跟随行为的过渡，对传统的跟随模型进行了修改。特定车道变更行为前后的车道。引入了双曲正切过渡函数以连接模型以实现模型输出的平滑过渡。然后，将高速公路自动驾驶汽车的任意车道变更决策过程建模为两车博弈模型，并提出了涉及安全性，效率和乘坐舒适性的综合损失函数，以评估该策略。通过使损失的期望最小化来获得最佳策略。最后，为了验证所提出的新模型的性能，针对多目标仿真条件，对不同的跟车和换道模型进行了仿真。仿真结果表明，新模型具有更高的流量效率，更好的同质性和稳定性。通过使损失的期望最小化来获得最佳策略。最后，为了验证所提出的新模型的性能，针对多目标仿真条件，对不同的跟车和换道模型进行了仿真。仿真结果表明，新模型具有更高的流量效率，更好的同质性和稳定性。通过使损失的期望最小化来获得最佳策略。最后，为了验证所提出的新模型的性能，针对多目标仿真条件，对不同的跟车和换道模型进行了仿真。仿真结果表明，新模型具有更高的流量效率，更好的同质性和稳定性。