## 7 Experiment
### Results and Analysis  
In our preceding study, we introduced an algorithm known as DRLNDT, which yielded high-quality results in tasks involving fixed starting and ending positions within the CARLA simulation environment, with a success rate of 99%. We refer to this particular algorithm, termed the Standard DRLNDT, as the baseline for comparison. We contrast our algorithm with this baseline to underscore its inherent benefits. This study compares a standard DRLNDT algorithm, as previously described in our research, with a novel approach for reward generation via active preference learning. In order to differentiate our novel methodology from the baseline, we designated this approach as DRLNDT + APRL. The \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reward Feature** & **APRL** & **Human Design** \\ \hline \(r_{\text{success}}\) & 0.4099 & 0.20 \\ \(r_{\text{collision}}\) & 0.038 & 0.40 \\ \(r_{\text{long\_time}}\) & 0.5687 & 0.50 \\ \(r_{\text{time\_out}}\) & \(-0.1763\) & 0.50 \\ \(r_{\text{potential}}\) & 0.6869 & 0.40 \\ \(r_{\text{velocity}}\) & 0.0643 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Reward weights. DRLNDT + APRL framework ensures that the baseline algorithm maintains consistency across training and evaluation environments. Following 50 million training steps, we systematically record the experiment's _mean_length_, _mean_rewards_, and _success_rate_. Interim evaluations are conducted to monitor these metrics at predefined intervals. The model that achieves the highest reward is selected as the optimal model for the final evaluation, which compares the performance of the DRLNDT + APRL method against the baseline. In the final evaluation, we investigate the average response times of various algorithms within autonomous driving systems, evaluating their decision-making capabilities in novel scenarios to ensure the safety of these systems. The variable _mean_length_ represents the mean distance traveled during each episode during training. The variable _mean_length_ represents the average length of episodes calculated over batches of 100 episodes. A smaller average step length indicates more favorable experimental results, suggesting a decreased time for the autonomous vehicle to accomplish its goal, provided it maintains a high success rate. This is attributable to the fixed duration of each phase. The variable _mean_reward_ indicates the average reward accumulated across numerous iterations, with a higher value suggesting a greater probability of the algorithm's operational success. The variable _mean_reward_ represents the average reward per episode, calculated over intervals of 100 episodes throughout the training period. Additionally, the _success_rate_ variable measures the proportion of tasks completed to the total number of attempts. The _success_rate_ represents the proportion of tasks completed successfully to the total number of episodes from the start to the conclusion of the training period. An increased success rate signifies enhanced algorithmic efficacy. It is crucial to emphasize that only instances where the autonomous vehicle reaches the destination without human intervention and without engaging in risky maneuvers are considered successful. Thus, the success rate serves as a critical metric for the safety of autonomous driving systems. The experimental data are presented in Tables 2-4, and visually represented in Figures 6 and 8. In Figure 8, the x-axis represents the timestep, while the y-axis corresponds to _mean_reward_, _mean_length_, and _success_rate_, respectively. Graphical representations with the timestep on the x-axis offer a nuanced view of the dynamics in reward patterns during an agent's learning process. Such visualizations are instrumental in monitoring both the advancement and the variability in the agent's learning. However, interpreting data from extensive time series can introduce complexity and potential confusion.