Optimization Method of Energy Saving Strategy for Networked Driving in Road Sections with Frequent Traffic Flow Changes

It is of great significance to construct a networked energy-saving driving strategy method and application framework to solve the problems of traffic disorder, speed fluctuations, and high energy consumption caused by frequent acceleration, deceleration, and lane changing of vehicles in road sections with variable traffic flow. Considering the mixed traffic scenario where autonomous vehicles and manually driven vehicles interact and infiltrate, a hybrid traffic flow vehicle energy-saving driving model was established, and the Dueling Double Deep Q-Network (D3QN) was used to optimize and solve the energy-saving driving model; Selecting Qingdao urban intersections as application scenarios, energy-saving driving strategy application facilities were constructed in simulation experiments to carry out simulation verification of energy-saving driving strategies for mixed traffic flow in the context of vehicle networking. The simulation results show that in different scenarios with different proportions of CAVs, the energy-saving strategy based on D3QN deep reinforcement learning algorithm can achieve fuel savings of 8.41%~6.67% compared to conventional strategies. Compared with the ordinary reinforcement learning algorithm Q-learning, its fuel saving rate is increased by 1.94%~1.5%, and the energy-saving effect becomes more significant with the increase of traffic density; From the perspective of dynamic characteristics, the speed stability under the control of D3QN algorithm is superior to Q-learning algorithm, and significantly better than conventional strategies, further highlighting the comprehensive advantages of D3QN algorithm in optimizing traffic flow status and energy consumption control. The energy-saving driving strategy in the networked environment can reduce fuel consumption caused by speed fluctuations and traffic flow frequency disturbances, and optimize the stability of traffic flow operation.