## 5 Simulation Results and Discussion
### Effect of Cooperative Lane Change  
Figure 8 graphs the cumulative distribution function (CDF) for each metric when the traffic load is low (2880 vehicles/h for Scenario 20 and 4320 vehicles/h for Scenarios 30 and 31) or high (1440 vehicles/h for Scenario 20 and 2160 vehicles/h for Scenarios 30 and 31). Figure 9 graphs the comprehensive evaluation values for each metric under different traffic loads, from low traffic loads to the maximum traffic loads that can work in simulation in the SUMO simulator. _NoObstacle_ represents the case when there are no obstacles, i.e., the ideal road condition. Thus, the closer the metric value of the vehicle behavior model is to _NoObstacle_, the better the model becomes. Figure 7: Snapshots of simulations. The green box indicates an obstacle. Red boxes are cars that have detected the obstacle directly. (**a**) Simulations of obstacle avoidance in Scenario 20. (**b**) Simulations of obstacle avoidance in Scenario 30. Figure 8: Simulation results for each vehicle behavior model under fixed traffic loads, \(d_{\text{avoid}}=300\,\text{m}\), \(d_{\text{prelim}}=100\,\text{m}\), \(d_{\text{decel}}=500\,\text{m}\), CV penetration ratio \(=1.0\). obstacle, as each vehicle can start changing lanes only after it directly detects the obstacle and thus it has to finish lane changes within a short distance. That rapid deceleration to avoid a collision can be seen in the results of the CDF of the minimum TTC and the CDF of the maximum jerk in Figure 8. On the other hand, the models based on the proposed scheme are better than _Manual_ in terms of traffic throughput, safety, and comfort level. This is because sharing information about the obstacle through V2V communication allows them to adjust their speed and change lanes in advance, preventing rapid deceleration. None of the models caused traffic jams; thus, they all have similar fairness. Next, we focus on the case of the high traffic load. From Figure 9, we can see that there is a large difference among the models in each metric at the high traffic load. Therefore, we discuss each vehicle behavior model below. _Manual_: In Scenarios 20 and 31, _Manual_ has lower traffic throughput and lower comfort level than the other models. This is because all vehicles in the free lanes are heavily affected by the traffic that moves from the closed lane, resulting in traffic jams in all lanes. On the other hand, in Scenario 30, _Manual_ has similar traffic throughput to the  
Figure 9: Simulation results for each vehicle behavior model under different traffic loads, \(d_{\text{avoid}}=300\,\text{m}\), \(d_{\text{prelim}}=100\,\text{m}\), \(d_{\text{decel}}=500\,\text{m}\), CV penetration ratio \(=1.0\). other models and achieves the highest comfort level. However, it has the worst fairness of traffic throughput among lanes. This is because chronic traffic jams occur in lanes 0 and 1 in Scenario 30. Here, lane 2 is the farthest from the closed lane (lane 0), and vehicles in lane 2 are mostly unaffected by lane changes from the closed lane and hence, they keep moving quickly. On the other hand, vehicles in lane 1 need to decelerate to accommodate vehicles changing lanes from the closed lane. The difference in speed between vehicles in lanes 2 and 1 increases; thus, vehicles in lane 1 find it hard to move to lane 2. Therefore, the number of acceleration changes is reduced by the chronic traffic jams in lanes 0 and 1. That is, vehicles in lane 2 keep moving quickly, and vehicles in lanes 0 and 2 move slowly; thus, the discomfort level is very low. Additionally, in Scenario 30, lane 2 has much higher traffic throughput than lanes 0 and 1; thus, the total traffic throughput improves but fairness deteriorates compared with the other scenarios. This can be seen in Figure 8, where there are large differences in the minimum speed and in the travel times. _Proposed_NoGapOpen:_ In Scenarios 30 and 31, _Proposed_NoGapOpen_ has the highest traffic throughput.