The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (5): 10-20.doi: 10.19682/j.cnki.1005-8885.2022.0025

Special Issue: Special Topic on Artificial Intelligence of Things

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Vehicle-following system based on deep reinforcement learning in marine scene

Zhang Xin, Lou Haoran, Jiang Li, Xiao Qianhao, Cai Zhuwen   

  • Received:2022-04-19 Revised:2022-09-06 Online:2022-10-31 Published:2022-10-28
  • Contact: Hao-Ran Lou
  • Supported by:
    Remote analysis and monitoring system technology development of vibration parameters and energy efficiency index of power shafting;Research on Torque Dynamic Measurement and Accuracy Verification Technology


In order to solve the problems that the feature data type are not rich enough in the data collection process about the vehicle-following task in marine scene which results in a long model convergence time and high training difficulty, a two-stage vehicle-following system was proposed. Firstly, semantic segmentation model predicts the number of pixels of the followed target, then the number of pixels of the followed target is mapped to the position feature. Secondly, deep reinforcement learning algorithm enables the control equipment to make decision action, to ensure that two moving objects remain within the safe distance. The experimental results show that the two-stage vehicle-following system has a 40% faster convergence rate than the model without position feature, and the following stability is significantly improved by adding the position feature.

Key words: vehicle-following| semantic segmentation| reinforcement learning| double deep Q-network (DDQN)