## 2 Related Work  
Lane changing is one of the common and essential operations of vehicles and has a significant influence on traffic safety and efficiency [10]. With the rapid development of V2V communications, most vehicles will have the ability to exchange information with each other and perform cooperative lane changes, improving the efficiency and safety of the entire traffic. Automated lane changing by connected and autonomous vehicles (CAVs) has been studied actively. [PERSON] et al. propose a cooperative lane change strategy based on model predictive control (MPC), which realizes a safe and effective lane change through the cooperation between a lane-changing vehicle and vehicles on the target lane [11]. Their simulation results show that the deceleration of the follower vehicles can be mitigated. Further, the shock wave propagated in traffic flow can be alleviated compared with a traditional lane change. [PERSON] et al. propose a multi-vehicle cooperative automated lane-change maneuver for scenarios with three lanes and two lane-change vehicles [12]. They classify cooperative automated multi-lane-change scenarios into two types: same-direction lane change and intersectant-direction lane change, which facilitates the construction of a mathematical model. Lane-change vehicles determine the lane-change type based on information from all vehicles involved and plan safe trajectories. [PERSON] et al. propose a dynamic cooperative trajectory planning model for lane changes of autonomous driving on the traffic scene with multiple mandatory lane change demands [13]. This model divides the traffic flow into groups, which simplifies the problem. Vehicles in the same group cooperatively plan their trajectory so that they can drive safely with each other. These above-mentioned research works mainly focus on the trajectory planning of lane change in a limited area; that is, a few vehicles that want to change lanes and their neighboring vehicles are considered. Lane change maneuvers discussed in the literature are obtained mainly by solving an optimization model, such as MPC. Since merging from an on-ramp lane to the highway is one of the most challenging driving scenarios, there has been much research on cooperative lane change for on-ramp flows. In the merging scenario, vehicles in the lane that vanishes (i.e., an on-ramp lane or a merging lane) are forced to change lanes, and they know the position of the merging point beforehand. The Car 2 Car Communication Consortium (C2C-CC) [14] aims to improve safety in merging support control by recognizing the existence of NCVs by using collective perception [15]. The Federal Highway Administration [16] developed the Cooperative Automation Research Mobility Application (CARMA) as an open-source software platform to promote the verification and evaluation of automated cooperative driving technologies. In the merging support of CARMA, the mainline vehicle and the on-ramp vehicle exchange information (e.g., position and speed) by using vehicle-to-infrastructure (V2I) and V2V communication and control their speed for smooth merging [17]. [PERSON] et al. propose a rule-based adjusting algorithm to achieve a near-optimal merging sequence for on-ramp vehicles and mainline vehicles [18]. [PERSON] et al. propose a cooperative multiplayer game-based optimization framework to provide optimal trajectories for the CAVs in a merging zone [19]. These studies assume that all vehicles are CAVs and focus on microscopic operations, i.e., they treat the mobility of vehicles in a narrow area. Drivers and autonomous vehicles cannot know the existence of a sudden obstacle on the road beforehand. Thus, avoiding such an obstacle by lane changing is more difficult than lane changing at a fixed merge point. The Multi-Car Collision Avoidance (MuCCA) project has been developing a next-generation driver aid system that uses V2V communication to avoid multi-car collisions on highways [20]. [PERSON] et al.