# A Graph-Based Method for Tactical Planning of Lane-Level Driving Tasks in the Outlook Region
## 2 Literature Review  
Currently, there is lots of research on lane-changing decisions and lane selection. Based on how these methods consider traffic vehicles in the driving environment, they can be broadly categorized into the following classes. The first class of methods considers only the nearest neighbor vehicles in the current lane and adjacent lanes. These methods ignore the influence of vehicles farther away on decision-making and are unable to respond in advance to upcoming traffic situations. Many existing methods adopt this approach. The earliest lane-changing model, proposed by [PERSON] [18] for micro-traffic simulation, uses the following speed of the ego vehicle to determine the speed advantage between the current lane and adjacent lanes. Ref. [19] calculates indicators such as traffic efficiency and energy consumption based on the state of leading vehicles in the current and adjacent lanes to select the optimal lane. Refs. [20; 21] generate lane-changing intentions, considering factors such as whether the ego vehicle cannot reach its desired speed or maintain a minimum safety distance, due to the leading vehicle in the current lane. Refs. [22; 23] establish an artificial potential field for leading vehicles, using lateral forces from leading vehicles in the left and right lanes to decide lane-changing behavior. Since this approach makes it easy to represent the driving environment and build datasets, numerous machine learning methods also fall into this category. Ref. [24] uses a support vector machine to train a lane-change decision model, where the training data are extracted from the NGSIM dataset based on a predefined lane-change model. This model considers the three nearest vehicles in the current and target lanes. Refs. [25; 26] employ reinforcement learning to train a lane-change decision model. In [25], the state vector includes the closest leading vehicle in all lanes, while [26] considers the eight nearest neighbor vehicles in the current and adjacent lanes. The second class of methods considers all vehicles within a certain range in the current lane and adjacent lanes. Refs. [27; 28] use reinforcement learning to train lane-change models, with state representations that include all vehicles within 50-60 m ahead. Ref. [29] develops a two-dimensional IDM algorithm to calculate lane-change intentions based on the relative distances to surrounding vehicles. Refs. [30; 31] propose an MPC-based framework that uses the motion states of all nearby vehicles in the current and adjacent lanes to make lane-change decisions, enabling the vehicle to react proactively to upcoming traffic disturbances. Ref. [32] decides lane changes based on the average speed of vehicles in different lanes within a certain distance ahead. Among these, refs. [30; 31; 32] assume that vehicles can obtain the motion states of traffic vehicles through V2X communication, and their driving environments cover a broader range of traffic vehicles than other models. Refs. [4; 33] introduce large language models into automated driving systems. These models comprehensively understand the ego vehicle's near-field driving environment and handle complex and extreme scenarios. However, these methods still focus on single lane-change behaviors for decision-making and do not address the issue of multiple lane changes in multi-lane scenarios. The third class of methods does not consider traffic vehicles. Such methods typically focus on planning strategic driving tasks, i.e., lane-level navigation or route planning, such as in [34; 35; 36; 37]. These methods usually plan lane-level routes in the entire road network based on the lane structure and traffic rules such as speed limits and prohibitions on changing lanes. They can effectively handle the problems of lane selection and multiple lane changes at intersections, but cannot respond to dynamic traffic situations on road segments.