Collaborative Computation in Integrated Sensing, Communication, and Computation System for Autonomous Driving

In autonomous driving scenarios, limited sensing range of individual autonomous vehicles (AVs) and exponential growth of sensing data have drawn increasing attention. This paper focuses on an integrated system combining communication and computation assistance for sensing enhancement, exploring functional fusion and performance optimization of the autonomous driving integrated sensing, communication, and computation (ISCC) system. Specifically, we first establish a cloud-edge-terminal collaborative ISCC system tailored for autonomous driving. For this system, we model sensing, communication, and computation separately, where the sensing model incorporates task-dependent characteristics, specifically considering the sequential execution of detection and tracking as well as the parallel nature of localization. Given that the collaborative computation between AVs and edge nodes aims to maximize system performance, we formulate a mixed integer nonlinear optimization problem. To solve this problem, we design two independent agents for resource and offloading configuration based on deep reinforcement learning. The former can adaptively allocate resources in each time slot without requiring prior knowledge of task arrival times, while the latter employs a partial offloading strategy to leverage the local computing capabilities of AVs, thereby addressing the limitations of existing approaches that rely on fixed resource allocation or neglect local computation. The simulation results show that the average task completion rate of the proposed scheme is significantly improved, the system cost is notably reduced compared with traditional schemes.

• Publication year

  2026

• Venue

  IEEE transactions on intelligent transportation systems (Print)

• Publication date

  2026-01-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/TITS.2025.3625173
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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