# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
## 3 Results and Analysis
### Linear Obstacle Avoidance  
As shown in Figure 6a, in which A, B, C three obstacle vehicles are to be entered into the range of interest of the ego-vehicle to start the movement, in the ego-vehicle driving process, C obstacle car suddenly changed to the left to the ego-vehicle lane, placing it directly on the ego-vehicle's originally traveled route of impact, and if the vehicle chooses to change lanes to overtake the vehicle in front of it, it will be possible to produce a potential collision with the A, B cars.  
The test results are shown in Table 7, where both this paper's algorithm and the EGCN-SAC algorithm obtained a 100% success rate and high average test rewards, while the Bi-SAC algorithm experienced multiple timeouts. From Figures 7 and 8, it can be seen that the reward value and the time required during the testing of this paper's algorithm and the EGCN-SAC algorithm are smoother, while the Bi-SAC algorithm has a significant decrease in the reward value due to the occurrence of multiple timeouts and collisions. Additionally compared to the EGCN-SAC algorithm, the algorithm in this paper has a higher reward value and requires less test time under the same driving conditions, giving it higher passing efficiency.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Explanation** \\ \hline Success rate & the percentage of autos successfully completing a prescribed route over multiple tests. \\ Crash rate & the percentage of crashes that occurred in multiple tests of the autocar. \\ Overtime rate & the percentage of autos exceeding the maximum time limit over multiple tests. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Test performance metrics.  
Figure 6: Three simulation test scenarios built in CARLA.  
Further analyzing the test situation of a single round, when the obstacle vehicle C urgently inserts into the lane of the ego-vehicle, it can be seen from Figures 9 and 10 that the three algorithms adopt different obstacle avoidance strategies: the vehicle of in the EGCN-SAC algorithm fails to return to the original lane after switching to the neighboring lanes, and instead, it travels in the direction of the obstacle vehicle A, failing to reach its own target speed. In this paper, after adding the predicted number of each obstacle vehicle, we find that the future speed trend of obstacle vehicle A is always smaller than that of its own vehicle, so it completes the lane change to overtake vehicle C with a more stable speed and returns to the original lane quickly, reflecting a higher passing efficiency. The Bi-SAC algorithm's vehicle chooses a more conservative route, always follows vehicle C in this lane, and completes the whole journey through frequent acceleration and deceleration after the vehicle C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Success Rate** & **Crash Rate** & \begin{tabular}{c} **Overtime** \\ **Rate** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Rate** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Test Rewards** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Time** \\ **(s)** \\ \end{tabular} \\ \hline Ours & 100 & 0 & 0 & 654.29 & 5.54 \\ EGCN-SAC & 100 & 0 & 0 & 354.96 & 7.52 \\ Bi-SAC & 69 & 1 & 30 & 336.78 & 10.84 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Test result statistics in linear obstacle avoidance.  
Figure 8: Test time change curves.  
Figure 7: Test reward change curves.  
unsuccessful attempts to overtake, and because of the moderate speed maintained at a low level, the vehicle is not able to reach its own target speed. The overall reward value is lower because the speed is moderately maintained at a lower level.