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Scalable and Dynamic Cooperative Perception: A Data/Model Co-Driven Framework
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AI
DOI：10.1109/MNET.2024.3354209.png)
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Abstract
En 中文
Cooperative perception (CP) is a key approach to ensuring reliable situation awareness of connected and autonomous vehicles (CAVs). In this article, we discuss the key challenges in terms of scalability, dynamics, and performance uncertainty for supporting CP in a practical network environment. Then, we present a data/model co-driven framework for scalable and dynamic CP with performance awareness, as an engineering solution to address the challenges. Specifically, we propose a performance-aware scalable CP scheme based on a learningassisted optimization approach and a dynamic CP scheme based on an optimization-assisted learning approach for different scenarios, both exploiting data-driven and model-based methods to enhance each other. Finally, a case study is presented to show the effectiveness of our scheme in handling the network dynamics with resource efficiency.
Keywords:
Vehicle dynamics
Object detection
Uncertainty
Feature extraction
Sensors
Scalability
Parallel processing
Connected and autonomous vehicles (CAVs)
cooperative perception
data fusion
performance estimation
machine learning
data/model co-driven methods