Self-driving vehicles as high-performance computing systems – a hybrid end-device to cloud approach

Abstract

The rapid evolution of autonomous-vehicle (AV) technology has defined a new era of intelligent transportation systems, promising improved safety, efficiency, and scalability. However, the exponential increase in the number of onboard sensors and the corresponding growth in machine-generated data have created challenges in real-time processing, cloud dependency, and sustainable data management. Traditional centralized cloud infrastructures struggle to process this volume efficiently, motivating the need for distributed, edge–cloud hybrid computing paradigms.This research introduces a hybrid edge-to-cloud framework in which autonomous vehicles utilize their onboard supercomputers when underused or idle as distributed processing nodes. These vehicles collectively perform computational tasks such as lane feature extraction, harvesting data, HD-map generation, and update, operating as a parallel network of edge machines that seamlessly integrates with the cloud for aggregation and scalability. The proposed system demonstrates this concept through a crowdsourced HD-map creation pipeline that fuses lane level data from multiple vehicles and integrates it with open geographic information from OpenStreetMap (OSM). The system architecture includes edge-based preprocessing, cloud-based multi-vehicle fusion, and map redistribution. Each vehicle locally extracts lane features from camera and GPS data and transmits compact representations to the cloud. The cloud aligns and fuses these extractions using geometric registration, probabilistic averaging, and version-controlled map management. Experimental results on a self-collected three lane highway dataset achieved absolute accuracy of 0.98–1.15m RMSE (2σ ≤ 1.35m) and relative inter-lane accuracy of 0.11–0.13m mean |ΔW| (2σ ≤ 0.18m), satisfying commercial HD-map standards. Beyond HD mapping, this study illustrates the broader potential of autonomous vehicle data harvesting, where the collective computational and sensing power of a fleet can be leveraged for large scale tasks. The findings demonstrate that a camera-only, hybrid edge–cloud approach can achieve LiDAR comparable mapping precision on highway while significantly reducing the cost and cloud dependency, paving the way toward higher levels of autonomy within smarter and more sustainable transportation networks.