城市生态道路混合交通流节能驾驶策略优化

Optimization of Energy-Saving Driving Strategy on Urban Ecological Road with Mixed Traffic Flows

摘要:

针对生态道路自动网联车在混合交通流条件下的驾驶节能问题，提炼对于生态道路节能驾驶问题影响较大的野生动物通道场景，构建面向车联网的野生动物通道应用框架；并构建生态道路车联网环境下的车辆驾驶模型，运用动态规划进行离散化分析和状态划分，优化建立混合交通流车辆节能驾驶模型；通过强化学习Q-learning算法，对单辆汽车的节能驾驶模型进行优化求解；基于上海城市生态道路，建构考虑野生动物通道动物穿行风险的仿真场景，开展对车联网环境下的混合交通流节能驾驶策略仿真验证。结果表明该节能策略在车联网环境下能够使生态道路上车辆节省油耗量在6 %~11 %之间，并且节能效果将随着混合车流密度的增加而更优，验证了模型的合理性，以及算法求解的有效性。

Abstract:

This paper addresses energy-efficient driving strategies for autonomous connected vehicles on ecological roads under mixed traffic flow conditions. A wildlife passage scenario， which significantly impacts energy-saving driving， is extracted， and an application framework for wildlife passages within the Internet of Vehicles （IoV） is developed. A driving model for vehicles on ecological roads under the IoV environment is also constructed， utilizing dynamic programming for discretized analysis and state division. An energy-efficient driving model for vehicles within mixed traffic flow is optimized and established. The Q-learning algorithm is applied to optimize and solve the energy-saving driving model for a single vehicle. Based on the ecological roads in Shanghai， a simulation scenario considering the risk of wildlife crossing is created to validate the energy-saving driving strategies in the IoV environment. The results show that the proposed energy-saving strategy can reduce vehicle fuel consumption by 6 % to 11 %. Additionally， the energy-saving effect improves with increasing traffic density of vehicles， verifying both the reasonableness of the model and the effectiveness of the algorithm.