Volume 26 Issue 6
Jun.  2026
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2026  >  26(6): 153-166
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ZHAO Hong-zhuan, WANG Yi-chen, ZHANG Ji-kang, YUAN Quan, WANG Jian-qiang, YANG Liang-yi, WANG Tao, ZHOU Dan. Adaptive offloading model for remote driving takeover task based on cloud-edge collaboration[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 153-166. doi: 10.19818/j.cnki.1671-1637.2026.030
Citation: ZHAO Hong-zhuan, WANG Yi-chen, ZHANG Ji-kang, YUAN Quan, WANG Jian-qiang, YANG Liang-yi, WANG Tao, ZHOU Dan. Adaptive offloading model for remote driving takeover task based on cloud-edge collaboration[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 153-166. doi: 10.19818/j.cnki.1671-1637.2026.030
shu

Adaptive offloading model for remote driving takeover task based on cloud-edge collaboration

doi: 10.19818/j.cnki.1671-1637.2026.030
  • 1.

    Guangxi Key Laboratory of ITS, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China

  • 2.

    Key Laboratory of Holographic Perception and Intelligent Driving, Transportation Department of Guangxi Zhuang Autonomous Region, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China

  • 3.

    School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

  • 4.

    China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China

Funds:

National Natural Science Foundation of China 52362045

Science and Technology Major Project of Guangxi Province Guike AA23062053

Science and Technology Major Project of Guangxi Province Guike AA22068101

Key Research and Development Program of Guangxi Province Guike AB25069283

Natural Science Foundation of Beijing L247007

Open Project of Guangxi Key Laboratory of Precision Navigation Technology and Application DH202225

More Information
  • Corresponding author: ZHAO Hong-zhuan, professor, PhD, E-mail: zhaohongzhuan@guet.edu.cn
  • Received Date: 2025-03-19
  • Accepted Date: 2025-08-25
  • Rev Recd Date: 2025-07-01
  • Publish Date: 2026-06-28
    Abstract
  • An adaptive task offloading model based on cloud-edge collaboration was established, and the problems of high latency and unstable connection during remote driving takeover caused by network fluctuation and insufficient computing power were deeply analyzed. Three types of tasks, namely real-time control, computation-intensive, and interactive service tasks, were defined, and a two-level priority system of urgent and general levels was set to accurately distinguish the differentiated requirements of different tasks for latency and reliability. A collaborative computing environment integrating cloud center, edge node, and onboard terminal was constructed; a hierarchical offloading rule dynamically allocating computing nodes based on task priority and adaptively adjusting resource weights combined with real-time network bandwidth and edge load was proposed; a breakpoint resume mechanism based on backup nodes was studied to enhance the robustness of the system in unstable environments. A decision model with global minimum latency as the optimization objective was constructed using a dynamic programming algorithm, and a corresponding reward function was set to quantitatively evaluate the effectiveness of different offloading strategies. A dedicated dataset was constructed based on 11 parameters such as task data volume and processor frequency, and comparative experiments were designed to systematically study the performance of the model under dynamic load and different resource states. The research results indicate that under the scenario of dynamically changing edge load, the reward value obtained by the proposed adaptive offloading strategy is increased by 13.2% compared to the traditional fixed-threshold edge computing method; after introducing cloud collaborative computing, the overall reward value of the system is increased by 23.6% compared to the edge-only computing scheme; especially when the edge node load exceeds 60%, the proposed strategy can effectively reduce the task blocking rate by 45%.

     

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