Bandwidth-adaptive Cloud-Assisted 360-Degree 3D Perception for Autonomous Vehicles

TL;DR

This paper presents a cloud-assisted, bandwidth-adaptive 360-degree perception system for autonomous vehicles that reduces latency and improves detection accuracy by dynamically offloading processing and compressing data based on network conditions.

Contribution

It introduces a hybrid processing framework using transformer models and a dynamic optimization algorithm to adaptively split computation and compression levels for real-time 3D perception.

Findings

Achieved 72% latency reduction compared to onboard processing.

Improved detection accuracy by up to 20% with adaptive strategies.

Demonstrated effectiveness under realistic bandwidth variability.

Abstract

A key challenge for autonomous driving lies in maintaining real-time situational awareness regarding surrounding obstacles under strict latency constraints. The high processing requirements coupled with limited onboard computational resources can cause delay issues, particularly in complex urban settings. To address this, we propose leveraging Vehicle-to-Everything (V2X) communication to partially offload processing to the cloud, where compute resources are abundant, thus reducing overall latency. Our approach utilizes transformer-based models to fuse multi-camera sensor data into a comprehensive Bird's-Eye View (BEV) representation, enabling accurate 360-degree 3D object detection. The computation is dynamically split between the vehicle and the cloud based on the number of layers processed locally and the quantization level of the features. To further reduce network load, we apply feature vector clipping and compression prior to transmission. In a real-world experimental evaluation, our hybrid strategy achieved a 72 \% reduction in end-to-end latency compared to a traditional onboard solution. To adapt to fluctuating network conditions, we introduce a dynamic optimization algorithm that selects the split point and quantization level to maximize detection accuracy while satisfying real-time latency constraints. Trace-based evaluation under realistic bandwidth variability shows that this adaptive approach improves accuracy by up to 20 \% over static parameterization with the same latency performance.