# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 8 Results and Discussion  
In the test of scenario 1, the lane-change histories of the proposed model and the baseline model are shown in Figure 10(a) and 10(b), respectively, and their driving trajectories on the road segment are shown in Figure 10(a),b. Meanwhile, Figures 13 and 14 compare the results of one test for both models. From Figure 10(a),b, it can be observed that both the proposed model and the baseline model are capable of guiding the ego vehicle to change lanes step-by-step into the innermost lane with the highest passing efficiency. Additionally, combined with Figure 10(a),b, it can be found that although there is some variation in the timing of lane changes for the same model across different tests, the overall driving trajectories within the road segment differ only slightly. The comparison in Figure 13 reveals that the proposed model initiates lane changes earlier than the baseline model, and as shown in Figure 10(a), the intervals between consecutive lane changes are also shorter for the proposed model. Figure 14 indicates that these executed lane changes result in improvements to driving efficiency. These phenomena occur because the proposed model makes lane-change decisions within the outlook region, comprehensively considering traffic situations farther ahead. This allows the vehicle to react more \"proactively\" to upcoming traffic situations, thereby improving driving efficiency. baseline model makes lane-change decisions based solely on the surrounding vehicles in current and adjacent lanes. Therefore, under such traffic situations, it keeps the vehicle in its original lane to maintain a higher speed. The proposed model, however, decides to change lanes based on the overall driving efficiency within the outlook area, enabling the vehicle to move through three lane changes to reach the innermost lane. Figure 11: The lane change histories of the proposed model (**a**) and the baseline model (**b**) in scenario 1. Figure 12: The driving trajectories of the proposed model (**a**) and the baseline model (**b**) on the road segment in scenario 1. Figure 13: Comparison of lane change histories of the proposed model and the baseline model in scenario 1. The phenomenon that the proposed model guides the vehicle to change lanes only after a longer period can be explained by the traffic situations described in Figures 19 and 20. Since the controlled vehicles in the innermost lane have the highest speed, which is used to generated the highest flow speed, a large gap will be formed between these vehicles after a long period of driving, as shown in Figure 19. This makes the speed advantage of the innermost lane grow more significant over time, as illustrated in Figure 20. When this advantage becomes sufficient to compensate for the speed loss caused by the first two lane changes, the proposed model generates a lane-level driving  
Figure 16: Driving trajectories of the proposed model (**a**) and the baseline model (**b**) on the road segment in scenario 2. Figure 14: Comparison of driving trajectories of the proposed model and the baseline model in scenario 1. Figure 15: Lane change histories of the proposed model (**a**) and the baseline model (**b**) in scenario 2.  
task containing three lane changes. A comparison through Figure 18 reveals that, after briefly falling behind the baseline model, the proposed model manages to overtake it. Figure 19: The large gap between controlled vehicles after a long simulation time. The blue boxes in the figure mark the controlled vehicles used to generate expected traffic flow and the yellow box marks the traffic vehicle that is changing lanes. Because the controlled vehicle in the top lane (innermost lane) is set to a faster speed, after a long simulation time, it forms a larger gap with the controlled vehicles in other lanes. This will allow other traffic vehicles to change lanes and overtake, resulting in an increase in the traffic flow speed in the top lane (innermost lane).