# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
## 2 Method
### Decision Model Based on Reinforcement Learning
#### 2.3.1 State Space Design  
The factors affecting the decision-making of ego-vehicles mainly lie in the vehicles around the global path points of ego-vehicles, and the range of interest represents the vehicle's field of concern, thus it is crucial to first divide it reasonably for the decision-making of ego-vehicles. In this paper, we analyze the real dataset NGSIM [20] (Next Generation Simulation) under actual traffic conditions, which contains two arterial road segments (Lankershim and Peachtree Street) and two freeway segments (I-80 and US-101). The arterial roadway segments connect multiple congested intersections, and in this scenario, vehicles should pay more attention to the lateral view to prevent non-compliant oncoming traffic. In contrast, the freeway scenario travels in a regular and single direction, contains scenarios such as merging and following, and has a high average speed during normal driving; therefore, it may be difficult for the ego-vehicle to capture whether a vehicle exists in front of it if the longitudinal field of view is shorter, and the longitudinal distance is too small for the vehicle to avoid in time to collide when the vehicle is traveling at a higher speed, whereas the transverse distance is only needed to consider the vehicles in the single lane on both sides of the road when impact is sufficient. The comparison of the two road conditions and the range of interest settings are shown in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Typical Road Conditions** & **The Number of Lanes** & **Average Lane Width (m)** & **Average Speed (m/s)** & **Range of Interest Setting (m)** \\ \hline Straight sections of highways & Vertical 5 & 3.6 & 13.4 m/s & Horizontal \(\pm\)4 m, vertical \(\pm\)70 m \\ Arterial Road Intersection & Vertical 8, horizontal 6 & 3 & 7.23 m/s & Horizontal \(\pm\)15 m, vertical \(\pm\)10 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Delineation of areas of interest for different road conditions. **Current state space:** The design of the current state space often contains only the ego-vehicle information and the surrounding vehicle information at the current moment, and the ego-vehicle information includes the relationship between the vehicle and the waypoints as well as the vehicle's own state quantities, which are used to ensure the stability of the vehicle traveling under the target path point. The absolute coordinates of the global waypoints are converted to the coordinates of the vehicle coordinate system, and thus the ego-vehicle information includes the longitudinal distance \(\Delta x_{\text{target}}\) and the lateral distance \(\Delta y_{\text{target}}\) between the vehicle and the next target path point, the longitudinal velocity \(u\) of the vehicle itself, and the heading angle \(\theta\). The surrounding vehicle information includes the longitudinal distance difference \(\Delta x\), the lateral distance difference \(\Delta y\) and the speed difference \(\Delta u\) between the vehicle and the surrounding vehicles, and the trajectory points of the vehicles in the interested range are acquired by the sensors. The surrounding vehicle information can observe the state difference between the surrounding vehicles and itself to realize effective obstacle avoidance. **State space with added trajectory prediction** Considering that the current state space has limitations and the vehicle cannot effectively predict the uncertainty of the surrounding environment, this paper introduces the future trajectory information output from the trajectory prediction model by combining the trained prediction model with the trajectory data acquired by the sensors in real time, the model is capable of predicting the trajectory distribution of the next 10 frames when the environmental input satisfies 10 frames of data, and removes if the input data is more than 10 frames from the earliest frame of data.