实际环境中基于深度Q学习的无人车路径规划

实际交通环境规划最优路径的重要问题是无人车智能导航,而无人车全局路径规划研究主要在于模拟环境中算法求解速度的提升,考虑大部分仅路径距离最优或局限于当前道路的自身状况,本研究针对实际环境中的其他因素及其未来的变化和动态路网中无人车全局路径规划的复杂任务,基于预测后再规划的思想提出面向实际环境的无人车驾驶系统框架,并结合深...

Unmanned vehicle path planning based on deep Q learning in real environment

It was an important problem for the intelligent navigation of unmanned vehicles that planning the optimal path in the actual traffic environment. At present, many researches about global path planning of unmanned vehicle mainly focused on the improvement of algorithm solution speed in the simulation environment. Most of them just only considered the optimal path distance or the current road conditions, also ignored other factors and future changes in the actual environment. In order to complete the complex task that competing global path planning of unmanned vehicle in dynamic road network, this research put forward a framework of unmanned vehicle driving system for practical environment based on the thought of planning after prediction, and put forward DP-DQN which was a fast global path planning method combined with deep Q learning and deep prediction network technology. This method used the road characteristic data such as time and space, weather et al to predict the future traffic situation, and then competed the global optimal path. Finally, experimental results based on open datasets showed that the proposed method reduced driving time 17.97% at most than Dijkstra, A^*, algorithm et al.