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Open Access

Joint Resource Optimization for Secure Cooperative Perception in Vehicular Networks

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
School of Electrical and Information Engineering, The University of Sydney, Sydney NSW2006, Australia
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Abstract

In the realm of autonomous driving, cooperative perception serves as a crucial technology for mitigating the inherent constraints of individual vehicle’s perception. To enable cooperative perception, vehicle-to-vehicle (V2V) communication plays an indispensable role. Unfortunately, owing to weak virus protection in V2V networks, the emergence and widespread adoption of V2V communications have also created fertile soil for the breeding and rapid spreading of worms. To stimulate vehicles to participate in cooperative perception while blocking the spreading of worms through V2V communications, we design an incentive mechanism, in which the utility of each sensory data requester and that of each sensory data provider are defined, respectively, to maximize the total utility of all the vehicles. To deal with the highly non-convex problem, we propose a pairing and resource allocation (PRA) scheme based on the Stackelberg game theory. Specifically, we decompose the problem into two subproblems. The subproblem of maximizing the utility of the requester is solved via a two-stage iterative algorithm, while the subproblem of maximizing the utility of the provider is addressed using the linear search method. The results demonstrate that our proposed PRA approach addresses the challenges of cooperative perception and worm spreading while efficiently converging to the Stackelberg equilibrium point, jointly maximizing the utilities for both the requester and the provider.

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Tsinghua Science and Technology
Pages 1044-1059
Cite this article:
Kang Y, Song Q, Song J, et al. Joint Resource Optimization for Secure Cooperative Perception in Vehicular Networks. Tsinghua Science and Technology, 2025, 30(3): 1044-1059. https://doi.org/10.26599/TST.2024.9010068

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Received: 15 December 2023
Revised: 19 February 2024
Accepted: 25 March 2024
Published: 30 December 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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