Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV

TL;DR

This paper introduces a channel-aware data fusion method for CAVs that uses adaptive compression and optimization to enhance network throughput and perception accuracy in cooperative autonomous vehicle systems.

Contribution

It presents a novel throughput maximization approach combining a self-supervised autoencoder with mixed integer programming for optimal data compression and transmission in CAV networks.

Findings

Over 20% throughput improvement

9.38% increase in average precision

Latency of approximately 20 ms

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.