We propose a SLAM (Simultaneous Localization and Mapping) method for dynamic environments based on the fusion of semantic and optical flow information. Traditional visual SLAM systems suffer a decline in localization accuracy due to interference from moving objects in the scene. To address this issue, we employ a method that combines semantic and optical flow information. First, we extract semantic and optical flow information from the scene using the deep learning network. Then we divide the scene into different regions based on the semantic information. Finally, we employ suitable fusion methods according to the different regions to identify and eliminate the interference of moving objects on localization. We validated and assessed the performance of the proposed method on the public TUM dataset. The final experimental results demonstrate that the proposed method exhibits excellent performance in dynamic scenarios. This is of significant importance for improving the performance of the SLAM in dynamic environments.
Combined with the current development trend of the Internet of Vehicles, this paper proposes a predictive energy management strategy based on working conditions for plug-in fuel cell vehicles. First, relying on the micro-traffic simulation platform, based on the use of neural networks to predict changes in road traffic in the future, a method for constructing global operating conditions is proposed. Combining vehicle speed conditions with predictive energy management strategies, introducing final value constraints in the control time domain into model predictive control (MPC), which improves the energy-saving effect of real-time control strategies from a global perspective. The simulation results show that the comprehensive energy-saving effect of the proposed fuel cell vehicle predicted energy management strategy based on working conditions can reach 93.99% of the theoretical optimal control effect.
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