Evaluating Cloud Platforms for Real-Time Cooperative Perception Systems Using RSU Cameras: A Comparative Study of Google Colab, Azure, and AWS Oakland University

Format:

Book

Conference/Event

Author/Creator:

Alkharabsheh, Ekhlass, author.

Alawneh, Shadi, author.

Rawashdeh, Osamah, author.

Conference Name:

WCX SAE World Congress Experience (2026-04-14 : Detroit, Michigan, United States)

Language:

English

Subjects (All):

Intelligent transportation systems.

Autonomous vehicles.

Cameras.

Local Subjects:

Intelligent transportation systems.

Autonomous vehicles.

Cameras.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

This paper presents a comparative study of three widely used cloud platforms, Google Colab, Microsoft Azure, and Amazon Web Services (AWS), for running a real-time cooperative perception system based on roadside unit (RSU) cameras. The goal is to evaluate the performance, scalability, and cost-efficiency of each platform when handling high-volume video data for object detection, a key task in autonomous driving. A unified perception pipeline using the YOLOv8 Small model was deployed on all platforms, with the same dataset and settings to ensure fair comparison. The evaluation focused on key metrics such as latency, frame processing rate, detection accuracy, cost, scalability, and reliability. The results show that Google Colab is a cost-effective starting point but has limitations in uptime and scalability. Azure offers stable performance and balanced cost, making it suitable for medium-scale applications. AWS delivers the best scalability and speed but at a higher cost. This study provides practical guidance for choosing the right cloud platform for deploying cooperative perception and intelligent transportation systems

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license