Optimization of Energy-Saving Driving Strategy on Urban Ecological Road with Mixed Traffic Flows
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.
traffic engineeringecological roadsInternet of Vehiclesenergy-saving drivingreinforcement learning
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