# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 3 Objectives and Assumptions  
The proposed method aims to obtain lane-level driving tasks within the outlook region, which will be used to guide the vehicle to follow the lane or change lanes several times to enter the desired lane. The outlook region in this paper is defined as a spatial range covering potential multiple lane-change maneuvers of the ego vehicle which is much larger than the vehicle's surrounding region. Decision-making within the outlook region should not involve vehicle collision issues. Lane-level driving tasks are spatially represented by several consecutive lane segments, as shown in Figure 3. In scenarios where the vehicle is traveling on a road segment, when there is only one lane segment laterally, the vehicle is expected to be in a lane-following state. When a second lane segment appears laterally, it represents the need for a lane-change maneuver. In the method proposed in this paper, collision safety issues are not considered in the decision of lane-level driving tasks. The lane segment specified in a lane-level driving task is the region where the vehicle is expected to drive. Subsequent modules which execute the driving tasks ensure safe operation based on the local traffic situations. For situations where it is unsafe to change lanes in a timely manner, the proposed method includes an alternative path in the lane-level driving task. The alternative path only involves lane-following maneuvers. This allows the vehicle to slow down or even stop if necessary to execute the required lane change. The handling of collision safety is separated from the decision of lane-level driving task primarily due to the following two reasons:  
1. The time required for a complete lane-changing maneuver is significantly longer than the reaction time needed to avoid a collision. Additionally, the decision-making interval for lane-level driving tasks is much larger than the computation interval for vehicle motion intention or trajectories;
2. The decisions of lane-level driving tasks focus on the overall performance of the vehicle's operation within the outlook region. Under normal circumstances, interactions with individual surrounding vehicles have a relatively minor impact on this. In the lane-change decision process of automated vehicles, several evaluation indicators are usually established, such as driving efficiency, collision risk, energy consumption, driving comfort, etc. However, in order to introduce the proposed method more clearly and concisely, only the aspects of driving efficiency and legality are considered here. The goals of the proposed method in making decisions on lane-level driving tasks are as follows:  
1. To minimize the travel time (or maximize the average speed) that the ego vehicle takes to pass through the predefined road segment;
2. To ensure that the lane-change behaviors of the ego vehicle comply with traffic regulations, specifically meaning, during a single lane change, not crossing multiple lanes; activating turn signals before changing lanes; and adhering to road or lane speed limits at all times. When establishing a driving environment model for the outlook region, the proposed method uses high-resolution traffic flow information to describe the traffic situations rather than the motion states of individual vehicles. This approach considers the following factors:  
1. In real-world road environments, whether using on-board sensors-based or V2X (Vehicle-to-Everything)-based perception methods, it is impossible to obtain infor  
Figure 3: Spatial composition of the lane-level driving task in the proposed method. mation about all vehicles on the road. Extracting traffic flow information from the perceivable traffic vehicles can effectively avoid this disadvantage. 2. The states of traffic flow, which represent the motion of a group of vehicles, tend to change more smoothly compared to the motion states of individual vehicles. This allows us to avoid predicting the trajectories of a large number of vehicles over an extended period.