# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 1 Introduction  
Automated driving is regarded as one of the main development directions of the automotive industry due to its great potential in aspects of safety and efficiency [1, 2, 3, 4]. Automated lane-selection and lane-change functions are key components of automated driving systems. For a long time in the future, automated vehicles will coexist with human-driven vehicles on the road. Compliance with road traffic rules is crucial for automated vehicles, especially when it comes to lane changes. Traffic regulations often stipulate that a vehicle can only move one lane at a time during a lane change and must turn on the turn signal several seconds before initiating the lane-change action. Under such regulatory constraints, if a vehicle wishes to move to a lane farther away from its current lane, it would need to perform multiple lane changes (more than two). For instance, as shown in Figure 1, if a vehicle wants to enter a faster-moving lane, it would require at least two lane changes. In this scenario, the first lane change may even have a negative impact on travel efficiency. Many current studies on automated lane selection or lane changes typically only consider the surrounding region within a limited range, which is usually used to consider interactions with other vehicles, and these methods usually search for optimal strategies within the scope of a single lane-change maneuver. This can easily cause the vehicle to fall into in local optimum situations in multi-lane scenarios and fail to find potentially better strategies. Furthermore, the aforementioned limitations also reveal the parts of the driving task hierarchical architecture commonly reflected in many research papers which can be further improved. Referring to the earliest driving task model proposed by [PERSON] [5; 6], the entire driving task can be divided into three levels: strategic, tactical, and operational. For automated driving, the strategic-level tasks mainly refer to navigation, i.e., planning the route for vehicle travel in the road network, including the lane-level route when entering and exiting intersections, while the tactical-level tasks include more detailed and specific driving behaviors such as deciding whether to overtake, change lanes, or adjust speed [7]. However, when a vehicle is traveling on a road segment, a series of related lane-change maneuvers caused by local traffic represent the local lane-level path expected by the driver or the automated driving system. This corresponds to the lane-level driving tasks located between the existing strategic- and tactical-level tasks, as shown in Figure 2. This issue is rarely mentioned in current research. This paper attempts to propose a tactical planning method for lane-level driving tasks. According to research [8; 9; 10; 11; 12], the duration of a single lane change by human drivers generally falls within the range of 1 s to 16 s, with an average of around 5 s to 7 s. Adding the time required to signal with the turn indicator, the spatial span of the driving tasks, which can contain multiple lane changes, far exceeds the surrounding region of the ego vehicle. In this spatial span, the impact of collision safety on the decision-making outcome is small. Therefore, this paper attempts to construct a standalone lane-level driving task planning module whose considered driving environment encompasses a larger road region. Figure 1: Multiple lane changes required to enter the target lane. Figure 2: Hierarchical framework of whole driving task. The right part indicated by the orange arrow represents the result of further decomposition of the existing tactical functions. The surrounding region primarily involves a series of functions that focus on rapid response, such as obstacle avoidance, while the outlook region focuses on the overall driving strategies the vehicle adopts to address upcoming traffic situations.