DSORL: Data Source Optimization With Reinforcement Learning Scheme for Vehicular Named Data Networks

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Abstract
Highly-dynamic (HD) map is an indispensable building block in the future of autonomous driving, allowing for fine-grained environmental awareness, precise localization, and route planning. However, since HD maps include rich, multidimensional information, the volume of HD map data is substantial and cannot be transmitted frequently by several vehicles over vehicular networks in real-time. Therefore, in this paper, we propose a data source selection scheme for effective HD map transmissions in vehicular named data networking (NDN) scenarios. To achieve our goal, we created a vehicular NDN environment for data collection, processing, and transmission using the CARLA simulator and robot operating system 2 (ROS2). Next, due to our vehicular NDN’s dynamic and complex nature, we formulate the data source selection problem as a Markov decision process (MDP) and solve it using a reinforcement learning approach. For simplicity, we termed our proposed scheme data source optimization with reinforcement learning (DSORL), which selects suitable vehicles for HD map data transmission to MEC servers. The experiment results indicate that our suggested method outperformed existing baseline schemes, such as RLSS, Pro-RTT, and HDM-RTT, across all performance criteria in the evaluation. For instance, the system throughput increases by $65\%-72.68\%$ compared to other baseline systems. Similarly, the proposed approach can minimize packet loss rate, data size, and transmission time by up to $60.6\%$ , $77.5\%$ , and $54.1\%$ , respectively.
Funding Information
  • Funds from Toyota Motor North America, Amazon
  • U.S. National Science Foundation (1910667)
  • U.S. National Science Foundation (1910891)
  • U.S. National Science Foundation (2025284)
  • U.S. National Science Foundation (CNS-2107216)
  • U.S. National Science Foundation (CNS-2128368)
  • U.S. National Science Foundation (CMMI-2222810)

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