## 7 Experiment
### Results and Analysis
Our algorithm's response time was as quick as 0.005 s, which is negligible in practical terms, particularly as it would be further reduced within a C++ framework. This study uses active preference learning to construct a reward function attuned to human preferences. The primary aim is to empower the agent to develop a policy that aligns with human criteria, contingent upon the reward structure and the immediate environment. Our approach to active preference learning integrates a novel methodology that weddings the mutual information algorithm with the NUTS (No-U-Turn Sampler) algorithm. The mutual information algorithm is designed to balance acquiring information gain with reducing the agent's uncertainty, leading the agent to pose direct and informative questions, considering the human's capacity to respond. The NUTS algorithm obviates the need for meticulous manual parameter tuning, enhancing computational efficiency and user accessibility. This algorithm improves the--belief model's iterative refinement by generating samples more efficiently concurrently. Our synthesis of these algorithms demonstrates enhanced efficacy in computing cosine similarity between the user and belief models, surpassing previous methods. These findings affirm the belief model's capacity to assimilate human preferences effectively. We subsequently employ the reward weights derived from active preference learning to facilitate policy learning. This method enables an autonomous driving vehicle to learn a policy for navigating between randomized initial and terminal positions in an environment devoid of high-precision maps and predefined routing. The vehicle operates solely on forward-facing vision and self-collected data as the state representation. Our algorithm emerges as a robust and fundamental approach, as evidenced by our experimental results. It significantly outperforms policies trained using manually constructed reward functions. The efficacy of the reward function, developed through active preference learning, is authenticated by its ability to resonate with human cognitive processes and to train policies that meet human-driven criteria. Our work establishes a crucial foundational framework for the progression of autonomous driving intelligence. Significant challenges persist in the practical application of our method, particularly concerning the safety of reinforcement learning-based autonomous driving systems in real-world settings. To ensure the generalizability and transferability of reinforcement learning algorithms, it is crucial to maintain congruence between simulations and actual environments. This congruence lays the groundwork for transitioning from virtual to real scenarios. Regrettably, our laboratory lacks both autonomous driving vehicles and realistic autonomous driving simulation scenarios that align with the real world, precluding the validation of our algorithms in a genuine context. Nonetheless, we selected the CARLA simulator as our testbed due to its fidelity in emulating data from real-world sensors. We are optimistic that, in the future, our algorithms will successfully achieve virtual-to-real transfer, which remains an avenue for our ongoing research.