## 3 Simulation
### Simulation
#### 3.2.2 Obstacle Environment Scenarios  
In an obstacle-free environment, the APF-D3 QNPER algorithm demonstrates a superior performance compared to other DQN and its derived algorithms. It achieves higher reward values in shorter training times and exhibits relatively low total loss during the training process. To further evaluate the obstacle avoidance ability of the APF-D3 QNPER algorithm, a comparison is made with Algorithm 4. By comparing the performance of the APF-D3 QNPER algorithm with Algorithm 4, we can verify the effectiveness of the APF-D3 QNPER algorithm in terms of obstacle avoidance. In this simulation, the static obstacle is placed at a fixed position, as indicated in Table 8. The dynamic obstacle also has a fixed starting position but follows a random driving path. The minimum distance between obstacles is maintained at 5 m apart. To assess the obstacle avoidance ability of the algorithm, five rounds of tests are conducted. Table 9 shows dynamic obstacle points information. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Static Obstacle Number**} \\ \hline  & 1 & 2 & 3 & 4 & 5 \\ Coordinate (m) & (4.0, 1.0) & (3.8, 1.0) & (\(-\)10.0, \(-\)8.0) & (\(-\)10.0, 4.0) & (7.0, 2.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Static obstacle points information. Figure 5: Five-wheel accessibility experiment path. The tests include five sets of test environments. We tested each environment for 20 rounds, took the average of the test results, and compared the planning time and planning path length of Algorithms 4 and 5 in the five test environments, as shown in Tables 11 and 12. In the conducted test, the APF-D3 QNPER algorithm achieved an average driving time of 9.365 s and an average driving path length of 38.082 m. When compared to Algorithm 4, the APF-D3 QNPER algorithm exhibited a 25.23% reduction in driving time and a 17.44% reduction in planning path length. During the experiment, collisions between autonomous vehicles occurred due to the presence of a large number of obstacles and their random movements, resulting in planning failures. The failure rate of obstacle avoidance serves as a crucial indicator to evaluate the obstacle avoidance ability of autonomous vehicles. Table 12 provides the number of failures for Algorithms 4 and 5 across five different environments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Environment Coordinate (m)** & \multicolumn{5}{c}{**Dynamic Obstacle Number**} \\ \hline  & 6 & 7 & 8 & 9 & 10 \\ Env 1 & \(x\) & \(-7.417\) & \(-11.573\) & \(-14.959\) & \(-5.253\) & \(14.099\) \\ \(y\) & \(-3.343\) & \(6.833\) & \(7.881\) & \(-11.140\) & \(-3.405\) \\ Env 2 & \(x\) & \(2.551\) & \(8.424\) & \(9.998\) & \(-9.033\) & \(9.428\) \\ \(y\) & \(10.639\) & \(8.505\) & \(-7.697\) & \(8.490\) & \(-12.164\) \\ Env 3 & \(x\) & \(0.562\) & \(6.270\) & \(6.427\) & \(-13.812\) & \(12.791\) \\ \(y\) & \(-10.467\) & \(11.200\) & \(7.159\) & \(2.218\) & \(2.460\) \\ Env 4 & \(x\) & \(5.577\) & \(8.347\) & \(-14.126\) & \(10.805\) & \(14.843\) \\ \(y\) & \(2.145\) & \(5.429\) & \(5.661\) & \(-8.730\) & \(5.579\) \\ Env 5 & \(x\) & \(-11.071\) & \(8.007\) & \(11.488\) & \(-14.740\) & \(-12.999\) \\ \(y\) & \(11.938\) & \(1.794\) & \(-7.051\) & \(11.176\) & \(-1.302\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Dynamic obstacle points information. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Env 1 (m)** & **Env 2 (m)** & **Env 3 (m)** & **Env 4 (m)** & **Env 5 (m)** \\ \hline Algorithm 4 & \(39.753\) & \(52.673\) & \(40.101\) & \(54.611\) & \(43.505\) \\ Algorithm 5 & \(29.344\) & \(48.182\) & \(31.453\) & \(48.134\) & \(33.324\) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The average length of the planned path.