Cooperative Perception Using V2X Communications: An Experimental Study
Faisal Hawlader, François Robinet, Raphaël Frank · 2024
The development and testing of real-time perception capabilities is crucial to fully realize the potential of connected and automated driving. This study presents a comprehensive analysis of end-to-end delay and object detection quality for distributed perception. We investigate object detection from camera images in two distinct scenarios: local and cloud processing. In the local scenario, objects are detected using only on-board hardware. The results are then used to generate Cooperative Perception Messages (CPM), which are broadcast to nearby vehicles via the ITS-G5 communication technology. In the cloud processing scenario, images are compressed using H.265 before being transmitted to the cloud via C-V2X, where objects are detected on more demanding hardware. The detection results are then transmitted to vehicles in the vicinity, enabling cooperative perception scenarios. This study evaluates real-time object detection systems in real-world conditions, and highlights the trade-offs between the end-to-end detection delay and quality. By leveraging emerging technologies such as ITS-G5 and C-V2X, our research provides crucial insights for the development of efficient perception systems in the connected driving ecosystem.