# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
## 4 Conclusions  
In order to overcome the influence of dynamic traffic participants on the decision-making of the ego-vehicle in the traffic environment, this paper proposes an autopilot learning strategy based on spatial-temporal feature prediction. The strategy establishes spatial relationships between the vehicle and the surrounding traffic participants, and establishes temporal relationships with the input of historical trajectory points, so as to predict the trajectories of the vehicle and the surrounding participants, and uses reinforcement learning as the automatic driving strategy, and the predicted trajectory points will expand the state space of the reinforcement learning, so as to enable the vehicle to have the ability to \"foresee\" the behavior of future traffic participants, and then realize effective obstacle avoidance in dynamic environments. The predicted trajectory points will expand the state space of reinforcement learning, so that the vehicle has the ability to \"anticipate\" the behavior of future traffic participants, and then realize effective obstacle avoidance in dynamic environments. Simulation results show that the spatial-temporal feature-based prediction model proposed in this paper has a high prediction accuracy. Compared to the two temporal feature predictions, Bilstm and transformer encoder, in the high-speed scenario, the prediction model ADE of this paper decreased by 55% and 80%, and the FDE decreased by 34% and 75%, respectively, and in the dry-road scenario, the prediction model ADE of this paper decreased by 54% and 84%, and the FDE decreased by 54% and 80%, respectively. Compared with the traditional spatial-temporal fusion-based prediction algorithm EGCN, the ADE of this paper's prediction model decreased by 45% and FDE decreased by 43% in the high-speed scenario, and the ADE of this paper's prediction model decreased by 33% and FDE decreased by 28% in the arterial road scenario. In the prediction-based decision-making task, this paper's algorithm shows the highest success rate, the lowest success rate of the proposed algorithm. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Obstacle** & **Model** & \begin{tabular}{c} **Success Rate** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Crash Rate** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Overtime Rate** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Test Rewards** \\ \end{tabular} &
\begin{tabular}{c} **Average Test Time** \\ **(s)** \\ \end{tabular} \\ \hline \multirow{3}{*}{A, C} & Ours & 99 & 1 & 0 & 419.71 & 6.37 \\  & EGCN-SAC & 100 & 0 & 0 & 409.75 & 7.00 \\  & Bi-SAC & 97 & 3 & 0 & 228.68 & 8.56 \\ \hline \multirow{3}{*}{A, C, D} & Ours & 95 & 5 & 0 & 382.67 & 5.28 \\  & EGCN-SAC & 90 & 10 & 0 & 251.11 & 2.93 \\  & Bi-SAC & 75 & 25 & 0 & 205.11 & 4.94 \\ \hline \multirow{3}{*}{A, B, C, D} & Ours & 76 & 24 & 0 & 286.36 & 7.00 \\  & EGCN-SAC & 65 & 35 & 0 & 175.31 & 2.99 \\ \cline{1-1}  & Bi-SAC & 54 & 30 & 16 & 122.62 & 3.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Test result statistics in an unprotected five-way intersection. timeout and collision rates, and the highest average test rewards compared to the Bi-SAC and EGCN-SAC algorithms in the three dynamic scenarios. This shows that the decision-making algorithm based on the prediction algorithm in this paper is able to use safer and more efficient strategies to complete the driving task. Future research will be extended to model other dynamic participants with specific behavioral patterns such as pedestrians and non-motorized vehicles to further improve trajectory prediction for other traffic participants with dynamic, uncertain behavior. In addition, future work will also aim to apply these algorithms to real-vehicle validation in practical road scenarios to further evaluate the performance and feasibility of the models in different driving conditions. All authors contributed to the study's conception and design.