# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 5 Discussion  
### Result Analysis  
This experiment was conducted within a simulated environment and did not account for factors such as localization errors, perception uncertainties, or dynamic traffic variations that may occur in real-world driving conditions. The specific experimental results are presented in Table 4, comparing the performance of A*, DQN, and the proposed A*-DQN hybrid method across four evaluation metrics. Figure 5: Comparison of planned paths with and without dynamic obstacles. The schematic illustrates the vehicle’s path adjustment in response to a moving obstacle, shown at two time points (T1 and T2). The dark blue circle indicates the vehicle’s position at T1, while the yellow circle represents the obstacle’s position at T1. The light blue circle marks the vehicle’s position at T2, and the orange circle denotes the obstacle’s position at T2. In quantitative terms, the proposed hybrid approach achieves a 4.1% increase in path length over the theoretical optimum (A*), while reducing the risk coefficient by 55.6% compared to A*, and improving the obstacle avoidance success rate from 10% to 95%. Planning time is also reduced by 61.9% relative to DQN-only methods. The analysis of data from 100 repeated experiments revealed significant differences among the three algorithms across multiple performance metrics. In terms of path length, the A* algorithm produced a 31.5 km route under static conditions, reflecting theoretical optimality. However, its inability to adapt to dynamic obstacles severely limits its practical applicability. The pure DQN approach, lacking global path guidance, generated redundant detours during obstacle avoidance, resulting in an average path length of 38.2 km--an increase of 21.6% compared to the optimal solution. In contrast, the hybrid A-DQN algorithm demonstrated a well-balanced performance. It achieved an average path length of 32.8 km, only 4.1% longer than the theoretical optimum. This difference was confirmed to be statistically significant through a paired-sample t-test (\(p<0.01\)). These findings validate the dual advantages of the hybrid algorithm: maintaining the global path optimization capability of A* while effectively handling dynamic obstacles through local DQN-based adjustments. The analysis of the planning time metric reveals distinct computational efficiency characteristics among the algorithms. The A* algorithm, leveraging its deterministic search strategy, completed path planning in static environments within just 0.3 s. In contrast, the pure DQN approach, which relies on real-time neural network inference, exhibited an average planning time of 2.1 s--a potential bottleneck in applications with stringent real-time requirements. The A-DQN hybrid algorithm incorporates an intelligent triggering mechanism that activates the DQN module only upon detecting a potential collision risk. As a result, the overall planning time is maintained under 0.8 s, representing a 61.9% improvement in efficiency compared to the pure DQN method. This approach effectively balances computational complexity and real-time performance demands. In terms of safety assessment, this study proposes a risk coefficient model that comprehensively accounts for both collision risk and path deviation. The analysis reveals that the A* algorithm, which entirely disregards dynamic obstacles, yields a high risk coefficient of 0.9, indicating significant safety concerns in real-world applications, as shown in Figure 6. The pure DQN method demonstrates a more conservative approach, reducing the risk coefficient to 0.3. However, its excessive avoidance behavior negatively impacts path smoothness. The A-DQN hybrid algorithm achieves a risk coefficient of 0.4, effectively balancing safety with path continuity. Further evidence is provided by the dynamic obstacle avoidance success rate: the hybrid algorithm achieves a 95% success rate, substantially outperforming the A* algorithm (10%) and exceeding the pure DQN method (88%).