# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 9 Conclusions  
In this study, we propose a novel tactical driving task planning method for automated vehicles. The distinctive feature of the proposed method is that the output is a lane-level driving task which can contain multiple lane-change maneuvers. The planning and decision-making of the lane-level driving task take the overall driving performance in the outlook region as the goal and do not pursue the optimal individual lane-change maneuver. This tactical perspective enables automated vehicles to overcome local adversities by performing successive lane changes over time, aiming to reach a more desirable lane for enhanced overall performance. In proposed method, the driving environment within the outlook region is characterized via high-resolution traffic flow data and the planning problem of the lane-level driving task is transformed into a classic directed graph minimum-cost path problem. The implemented method was tested in the simulation environment of VTD and benchmarked against an existing typical lane-change decision method. The results show that the proposed method improves the overall driving efficiency within the road segment and enables the ego vehicle to react earlier to upcoming traffic situations. Furthermore, the simulation results also indirectly point out that the slight difference in the timing of lane changes has little effect on the overall driving efficiency within the road segment, and the well-planned lane-level driving task is more conducive to improving the overall driving efficiency. Currently, there still remain several unresolved issues. First, the proposed model does not consider situations where the ego vehicle is blocked by other traffic vehicles and thus unable to follow the lane-level driving task. If the proposed model is combined with a lane-change model that incorporates cooperative and game-theoretic functions, the vehicles in the target lane or the ego vehicle will be able to actively accelerate or decelerate to establish conditions for executing lane changes, which should effectively address this issue. Another issue is that this paper aims to demonstrate the beneficial effects of planning vehicle driving schemes from the perspective of lane-level driving tasks. However, in this research, decision-making was only based on minimizing travel time under normal driving speeds. In future studies, more comprehensive factors should be incorporated into the decision-making process. For instance, when the traffic density in a specific lane is high, it may reduce the likelihood of successfully executing lane changes in subsequent modules. In such cases, the \"weight\" assigned to related driving schemes should be decreased. Additionally, different types of drivers may have their own preferences for various driving schemes. For example, if a section of a lane contains heavy trucks, cautious and prudent drivers would naturally prefer to avoid the nearby area to minimize driving risks, even if the corresponding lane-level driving scheme might allow for faster travel speeds. Another example is that if the expected speed of a lane-level driving scheme aligns more closely with the vehicle's optimal economic speed or exhibits smaller speed variations, it may better suit drivers who prioritize economy or comfort. Therefore, determining the best strategy remains a complex issue, as it involves understanding the preferences of different types of occupants regarding the driving style of automated vehicles. At the same time, the construction of relevant evaluation metrics for the decision-making of lane-level driving tasks differs from previous methods based on surrounding driving environment. Finally, the proposed planning method is based on high-resolution traffic flow data, and there are still a few problems to be addressed. In fully congested areas or at the entrance of intersections with red lights, the local traffic flow speed will fluctuate within a range close to zero.