# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
## 3 Results and Analysis
### Evaluation of Decision Model
#### 3.2.2 Experimental Results of Decision Model  
**Training results** In order to verify the effectiveness of introducing vehicle trajectory prediction to the SAC decision model in this paper, controlled experiments were designed, which compared the reward curves of the following algorithms under 500 rounds of training: the algorithm in this paper (Ours), the SAC algorithm based on the prediction of the Bilstm model (Bi-SAC), the SAC algorithm based on the prediction of the EGCN model (EGCN-SAC), the and a SAC algorithm that does not incorporate any prediction trajectories (ONLY-SAC). (Since the poor prediction ability of the transformer encoder model itself was mentioned in Section 3.1.2, it will not be considered in this section.) The results of the reward curve comparison are shown in Figure 4.  
Comparing the four curves, firstly, it can be judged that the inclusion of the predicted number of future trajectories of the vehicle in the SAC model can bring positive benefits to the decision-making ability of the autonomous vehicle. Secondly, compared with the other three trajectory prediction models, the algorithm in this paper obtains faster reward growth and significantly higher reward values. Although the reward value of the EGCN-SAC algorithm, which is in second place, is significantly higher than that of Bi-SAC and  
Figure 4: Comparison curve of SAC algorithm training rewards under different prediction models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameterization** & **Parameter Value** \\ \hline Discount factor & \(9.8\times 10^{-1}\) \\ Number of neurons in the hidden layer of the Actor network & 256 \\ Number of neurons in the hidden layer of the Critic network & 256 \\ Actor Network Learning Rate & \(1\times 10^{-4}\) \\ Critic Network Learning Rate & \(3\times 10^{-4}\) \\ batch volume & 256 \\ Experience pool capacity & \(1\times 10^{5}\) \\ Initial temperature coefficient value & \(-3\) \\ Temperature coefficient learning rate & \(3\times 10^{-4}\) \\ optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SAC algorithm hyperparameter settings. ONLY-SAC, the overall amplitude of the curves is too large, and the algorithm stability is not strong. From the above analysis, it shows that the present model is more helpful for the ego-vehicle to learn better driving performance in a shorter period of time. In order to verify the effectiveness of the SAC decision-making algorithm designed in this paper, the present prediction model is applied to DDPG and TD3, two algorithms also based on the Actor-Critic framework, respectively, and the experimental results are shown in Figure 5 after 500 rounds of training:  
Based on the reward curves, it can be seen that the SAC algorithm incorporating the trajectory prediction model significantly outperforms the other two algorithms both in terms of convergence speed and overall reward value. The possible reason for this is that both the DDPG and TD3 algorithms are based on deterministic strategies, whereas in this paper, 100 NPC vehicles are added to the training map, and the resulting environment is highly dynamic, leading to poorer robustness of their decisions. In contrast, the algorithm in this paper benefits from the entropy regularization mechanism of the SAC algorithm, which can effectively encourage the exploratory behavior of the ego-vehicles, and improve the overall decision-making effect and the stability of decision-making.