In order to solve the problem of frequent deceleration of unmanned vehicles when approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the Double Deep Q-Network (DDQN), to develop a local navigation system that adapts to obstacles while maintaining optimal speed planning. By integrating improved reward functions and obstacle angle determination methods, the system demonstrates significant enhancements in maneuvering capabilities without frequent decelerations. Experiments conducted in simulated environments with varying obstacle densities confirm the effectiveness of the proposed method in achieving more stable and efficient path planning.

翻译：为解决无人车接近障碍物时频繁减速的问题，本文采用深度Q网络（DQN）及其扩展——双深度Q网络（DDQN），开发了一种能够自适应障碍物并保持最优速度规划的局部导航系统。通过集成改进的奖励函数与障碍物角度判定方法，该系统在避免频繁减速的同时展现出显著的机动性能提升。在具有不同障碍物密度的模拟环境中进行的实验验证了所提方法在实现更稳定、高效路径规划方面的有效性。