## 1 Introduction
In contrast, vehicles in the leftmost (edge) lane will move smoothly without interference from increased traffic from other lanes. In this case, only vehicles initially traveling in the right and center lanes will experience delays in overtaking the obstacle. As the result, traffic distribution among lanes will be unfair. Therefore, the proposed extended strategy aims to control lane changes of vehicles in all lanes to achieve traffic fairness among all lanes without sacrificing safety or ride comfort. In this strategy, each vehicle approaching the obstacle selects a suitable lane in accordance with the obstacle's location and the vehicle density in each lane estimated from the beacon messages broadcast by each CV. The main contributions of this paper are listed below:  
* We propose a method to realize safe, smooth, and fair wide-range cooperative lane changing, reacting to a suddenly appearing obstacle on the road. In this strategy, a CV that detects the obstacle immediately notifies the following vehicles of the obstacle by using V2V communication. In turn, the following vehicles can take action to avoid the obstacle smoothly using a wide range behind the obstacle without sacrificing safety and ride comfort. To the authors' knowledge, existing studies focus on microscopic control around obstacles and do not discuss the control of traffic in a wide range, a few kilometers, as addressed in this study. The microscopic operation for lane-changing trajectory planning is out of this paper's scope. * The proposed method works when one lane is obstructed on a three- or more lane road. On three- or more lane roads, vehicles can have multiple choices to change lanes. Thus, we develop a method to select a suitable lane that will not cause unfairness among lanes and deteriorate ride comfort. * We confirm the effectiveness of the proposed method in realizing fairness among lanes, safety, ride comfort, and traffic throughput through simulations of a two-lane road and a three-lane road with various traffic scenarios, assuming that the vehicles' microscopic operation depends on the lane change model of SUMO. This paper is an extended version of a conference paper presented at VTC2022-Spring [9]. The main differences between this paper and the conference paper are in the simulation model, performance metrics, and results. The details are as follows. * The road length in the simulation environment is increased from 1 km to 2 km in order to provide sufficient free space before vehicles encounter an obstacle. * The simulations consider occlusion in a vehicle detection model. * Two vehicle behavior models (_Proposed_NoPrelim_ and _Proposed_NoAdaptive_) are introduced to confirm the effectiveness of the proposed scheme. * A fairness equation is introduced to quantitatively evaluate the fairness level in the simulation evaluation. * A safety metric is introduced in the simulation evaluation. * Cumulative distribution function (CDF) graphs are created for each performance metric to analyze the simulation results in more detail. * Simulations are conducted with various traffic loads to evaluate the performance of the proposed method comprehensively and with various zone sizes of the proposed scheme to investigate the impact of the zone sizes. The remainder of the paper is structured as follows. Section 2 summarizes the related work. Section 3 describes the proposed scheme, Section 4 explains the simulation model, and Section 5 presents the simulation results and a discussion from the viewpoints of traffic throughput, fairness, safety, and ride comfort. Section 6 concludes the paper with suggestions for future work.