Online V2X Scheduling for Raw-Level Cooperative Perception

Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee accuracy, it is demanding in communication bandwidth and computation power. Therefore, it is impo...

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Bibliographic Details
Published in:arXiv.org 2022-02
Main Authors: Jia, Yukuan, Mao, Ruiqing, Sun, Yuxuan, Zhou, Sheng, Niu, Zhisheng
Format: Article
Language:English
Subjects:
Algorithms
Field of view
Heterogeneity
Machine learning
Optimization
Perception
Schedules
Scheduling
Vehicles
Online Access:Get full text
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Summary:Cooperative perception of connected vehicles comes to the rescue when the field of view restricts stand-alone intelligence. While raw-level cooperative perception preserves most information to guarantee accuracy, it is demanding in communication bandwidth and computation power. Therefore, it is important to schedule the most beneficial vehicle to share its sensor in terms of supplementary view and stable network connection. In this paper, we present a model of raw-level cooperative perception and formulate the energy minimization problem of sensor sharing scheduling as a variant of the Multi-Armed Bandit (MAB) problem. Specifically, volatility of the neighboring vehicles, heterogeneity of V2X channels, and the time-varying traffic context are taken into consideration. Then we propose an online learning-based algorithm with logarithmic performance loss, achieving a decent trade-off between exploration and exploitation. Simulation results under different scenarios indicate that the proposed algorithm quickly learns to schedule the optimal cooperative vehicle and saves more energy as compared to baseline algorithms.
ISSN:2331-8422
DOI:10.48550/arxiv.2202.06085