Scholarlink

By improving reliable communication between cellular vehicle-to-everything (C-V2X), intelligent transportation systems (ITS) have the potential to revolutionize the real-time transportation sector. However, one element that hinders the seamless deployment of ITS is security issues. Resource limitations, anomaly types, false positives, and sensor interference are among the difficulties. Discrete Wavelet-Based Deep Reinforcement Learning with Double Q Learning (DWT-DDQN), a robust hybrid approach that combines the strengths of both discrete wavelet transform (DWT) and Double Deep Q Network (DDQN), is presented in the paper as an integrated mechanism that addresses the majority of these issues. It can dynamically adapt to the network, enhancing the Connected and Automated Vehicles (CAV) system’s safety and dependability. The dynamic approach is achieved by incorporating both the filtering and detection processes, which give a more robust and reliable performance output. Our numerical results clearly demonstrate the superior performance of DWT-DDQN over the existing conventional method at low and high levels of attack rates of \(\alpha \) levels of 1% and 3%, and 5% and 7%.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Safeguarding Autonomous Transportation: Deep Learning Strategies for Detecting Anomalies in Vehicle Sensor Data

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Elvin Eziama,
  • Remigius Chidiebere Diovu,
  • Gerald Onwujekwe,
  • Jacob Kapita,
  • Victor L. Y. Jegede,
  • Jegede T. T. Jegede,
  • Solomon G. Olumba,
  • Harrison Edokpolor,
  • Adeleye Olaniyan,
  • Paul A. Orenuga,
  • Anthony C. Ikekwere,
  • Emmanuel A. Ikekwere,
  • Uchechukwu Okonkwo,
  • Egwuatu C.A. Egwuatu,
  • Charles Anyim,
  • Jacob A. Alebiosu,
  • Victor N. Mbogu,
  • Benjamin O. Enobakhare,
  • Toheeb A. Oladimeji,
  • Anthony Junior Odigie,
  • Adeleye Olufemi

摘要

By improving reliable communication between cellular vehicle-to-everything (C-V2X), intelligent transportation systems (ITS) have the potential to revolutionize the real-time transportation sector. However, one element that hinders the seamless deployment of ITS is security issues. Resource limitations, anomaly types, false positives, and sensor interference are among the difficulties. Discrete Wavelet-Based Deep Reinforcement Learning with Double Q Learning (DWT-DDQN), a robust hybrid approach that combines the strengths of both discrete wavelet transform (DWT) and Double Deep Q Network (DDQN), is presented in the paper as an integrated mechanism that addresses the majority of these issues. It can dynamically adapt to the network, enhancing the Connected and Automated Vehicles (CAV) system’s safety and dependability. The dynamic approach is achieved by incorporating both the filtering and detection processes, which give a more robust and reliable performance output. Our numerical results clearly demonstrate the superior performance of DWT-DDQN over the existing conventional method at low and high levels of attack rates of \(\alpha \) levels of 1% and 3%, and 5% and 7%.