Edge-Assisted Accelerated Cooperative Sensing for CAVs: Task Placement and Resource Allocation

TL;DR

This paper introduces a novel RSU-assisted cooperative sensing scheme for CAVs that optimizes task placement and resource allocation to minimize sensing task completion time while maintaining accuracy.

Contribution

It proposes a joint task placement and resource allocation framework with a two-layer algorithm, combining Gibbs sampling and convex optimization, for real-time autonomous vehicle sensing.

Findings

Significant reduction in task completion time compared to benchmarks

Effective fusion of LiDAR data improves sensing accuracy

Algorithm demonstrates practical efficiency in autonomous driving simulations

Abstract

In this paper, we propose a novel road side unit (RSU)-assisted cooperative sensing scheme for connected autonomous vehicles (CAVs), with the objective to reduce completion time of sensing tasks. Specifically, LiDAR sensing data of both RSU and CAVs are selectively fused to improve sensing accuracy, and computing resources therein are cooperatively utilized to process tasks in real time. To this end, for each task, we decide whether to compute it at the CAV or at the RSU and allocate resources accordingly. We first formulate a joint task placement and resource allocation problem for minimizing the total task completion time while satisfying sensing accuracy constraint. We then decouple the problem into two subproblems and propose a two-layer algorithm to solve them. The outer layer first makes task placement decision based on the Gibbs sampling theory, while the inner layer makes spectrum and computing resource allocation decisions via greedy-based and convex optimization subroutines, respectively. Simulation results based on the autonomous driving simulator CARLA demonstrate the effectiveness of the proposed scheme in reducing total task completion time, comparing to benchmark schemes.