# A Cognitive Environment Modeling Approach for Autonomous Vehicles: A Chinese Experience
## 1 Introduction
### Related Works  
Environment models are the basis for motion planning in autonomous driving systems. These models build digital environments that are responsible for environmental assessment and trajectory planning [3]. The most common environment modeling approaches are scenario-based, and the challenge of current research lies in the modeling of dynamic scenarios and the trajectory planning in various scenarios [4]. Environment models for dynamic scenarios: Two-dimensional occupancy grid maps are the most-used environment models [5]. The environment can be represented if all the objects in the environment are projected onto the map. Occupied grids represent areas that should not be moved into, and free grids represent passable areas [6]. Moreover, the occupancy grid maps can overlay multiple 2D occupancy grid maps to represent environmental information, such as passable areas, potential collision risks, potential safe fields, and driving preferences [7]. These occupancy grid maps are widely used in robot path planning [8], but it is challenging to be compatible with autonomous vehicle trajectory planning in dynamic scenarios. In these cases, dynamic occupancy grid maps are proposed to handle dynamic scenarios [9]. Basically, a typical dynamic occupancy grid map is an occupancy map with multiple overlays of different times, which can represent the movement of environmental objects. There are other types of environment models. The Voronoi environment model [10] is proposed for non-structured roads with multiple static obstacles. However, it is challenging to introduce traffic rules. The weighted directed graph is a new solution for modeling and evaluation of structured road scenarios, which introduces multiple traffic rule constraints [11]. Some researchers have proposed the concept of continuous environmental models [12]. The list of obstacle information is a typical continuous environment model [13], which uses a sequence of locations with timestamps to represent all obstacles. It adapts to multidomain scenarios (structured roads, half-structured roads, and off-road), but it is challenging to introduce additional safety costs and requires a lot of computing power to get results. Trajectory planning approaches in various scenarios: There are two architectures of trajectory approaches: hierarchical architecture and integrated architecture. (1) Most existing planning approaches are hierarchical architecture approaches. It is a brilliant idea to handle the task of autonomous vehicle motion planning as a classic hierarchical robot motion planning task [14]. The path and the speed profile are planned separately [15]. The speed profile is attached after the path is planned. This hierarchical architecture is functional in most scenarios, such as car following scenarios [16], lane changing scenarios [17], emergency stop scenarios, dynamic scenarios with few dynamic objects, and scenarios that can be converted into simple scenarios. The path can be generated through state-machine-based planners [15], lattice-based planners [18], RRT-based planners [19], A-star-based planners [20], Dijkstra-based planners [10], numerical-optimization-based planners [21], etc. Additionally, the speed profile can be generated by rule-based planners [15], S-T-graph-based planners [21], numerical-optimization-based planners [22], etc. (2) Integrated trajectory planners are functional in both common and \"corner case\" scenarios. Roundabout scenarios are typical \"corner case\" scenarios [23]. There are multiple cut-in and cut-out obstacles that affect the movement of the ego vehicle. Usually, reinforcement-learning-based approaches are applied to these scenarios instead of hierarchical approaches [24]. The unprotected left turn scenario is another typical \"corner case\" scenario [25], a multi-obstacle game scenario [26]. The multivehicle cooperative control scenario is also a corner case scenario that requires trajectory planning approaches to perform integrated planning of multivehicle motion trajectories [27].