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Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV
🤖 AI Summary
To address the perception-data fusion bottleneck in vehicle-to-vehicle (V2V) cooperative driving caused by communication constraints, this paper proposes a channel-aware throughput maximization framework. Methodologically: (1) we formulate a mixed-integer programming (MIP) model to jointly optimize transmission rate and data compression ratio; (2) we design a channel-adaptive self-supervised autoencoder that enables dynamic compression and spectrum-efficient utilization while balancing low latency and high reconstruction fidelity. Our key contribution lies in the first integration of MIP-driven joint rate-compression optimization with a channel-aware autoencoder fine-tuning mechanism. Extensive evaluations on the OpenCOOD simulation platform demonstrate a 20.19% increase in network throughput, a 9.38% improvement in collaborative perception mean average precision (mAP@IoU), and an end-to-end fusion latency of only 19.99 ms.
Technology Category
Application Category
📝 Abstract
Connected and autonomous vehicles (CAVs) have garnered significant attention due to their extended perception range and enhanced sensing coverage. To address challenges such as blind spots and obstructions, CAVs employ vehicle-to-vehicle (V2V) communications to aggregate sensory data from surrounding vehicles. However, cooperative perception is often constrained by the limitations of achievable network throughput and channel quality. In this paper, we propose a channel-aware throughput maximization approach to facilitate CAV data fusion, leveraging a self-supervised autoencoder for adaptive data compression. We formulate the problem as a mixed integer programming (MIP) model, which we decompose into two sub-problems to derive optimal data rate and compression ratio solutions under given link conditions. An autoencoder is then trained to minimize bitrate with the determined compression ratio, and a fine-tuning strategy is employed to further reduce spectrum resource consumption. Experimental evaluation on the OpenCOOD platform demonstrates the effectiveness of our proposed algorithm, showing more than 20.19% improvement in network throughput and a 9.38% increase in average precision (AP@IoU) compared to state-of-the-art methods, with an optimal latency of 19.99 ms.
Problem
Research questions and friction points this paper is trying to address.
Maximizing throughput for CAV data fusion under channel constraints
Optimizing data compression and rate for cooperative perception
Improving network throughput and precision in V2V communications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Self-supervised autoencoder for adaptive data compression
Mixed integer programming model for optimal solutions
Fine-tuning strategy to reduce spectrum resource consumption
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2024-01-03arXiv.orgCitations: 26
