Task-Oriented Communication for Vehicle-to-Infrastructure Cooperative Perception

TL;DR

This paper introduces TOCOM-V2I, a task-oriented communication framework for vehicle-to-infrastructure perception that reduces bandwidth by transmitting only relevant information, enhancing efficiency without sacrificing perception accuracy.

Contribution

It presents a novel spatial-aware feature selection, hierarchical entropy compression, and attention-based feature fusion for efficient V2I perception.

Findings

Significant bandwidth reduction achieved.

Improved perception accuracy with less data transmission.

Effective feature compression and fusion demonstrated.

Abstract

Vehicle-to-infrastructure (V2I) cooperative perception plays a crucial role in autonomous driving scenarios. Despite its potential to improve perception accuracy and robustness, the large amount of raw sensor data inevitably results in high communication overhead. To mitigate this issue, we propose TOCOM-V2I, a task-oriented communication framework for V2I cooperative perception, which reduces bandwidth consumption by transmitting only task-relevant information, instead of the raw data stream, for perceiving the surrounding environment. Our contributions are threefold. First, we propose a spatial-aware feature selection module to filter out irrelevant information based on spatial relationships and perceptual prior. Second, we introduce a hierarchical entropy model to exploit redundancy within the features for efficient compression and transmission. Finally, we utilize a scaled dot-product attention architecture to fuse vehicle-side and infrastructure-side features to improve perception performance. Experimental results demonstrate the effectiveness of TOCOM-V2I.