AID4AD: Aerial Image Data for Automated Driving Perception

TL;DR

This paper introduces AID4AD, a high-resolution aerial imagery dataset aligned with nuScenes, enhancing perception tasks for automated vehicles by improving map construction and motion prediction accuracy.

Contribution

We present AID4AD, a novel dataset integrating aerial imagery with nuScenes, including an alignment workflow and ground truth for automated vehicle perception research.

Findings

Aerial imagery improves map construction accuracy by 15-23%.

Aerial data enhances trajectory prediction by 2%.

The dataset supports scalable environmental perception in AV systems.

Abstract

This work investigates the integration of spatially aligned aerial imagery into perception tasks for automated vehicles (AVs). As a central contribution, we present AID4AD, a publicly available dataset that augments the nuScenes dataset with high-resolution aerial imagery precisely aligned to its local coordinate system. The alignment is performed using SLAM-based point cloud maps provided by nuScenes, establishing a direct link between aerial data and nuScenes local coordinate system. To ensure spatial fidelity, we propose an alignment workflow that corrects for localization and projection distortions. A manual quality control process further refines the dataset by identifying a set of high-quality alignments, which we publish as ground truth to support future research on automated registration. We demonstrate the practical value of AID4AD in two representative tasks: in online map construction, aerial imagery serves as a complementary input that improves the mapping process; in motion prediction, it functions as a structured environmental representation that replaces high-definition maps. Experiments show that aerial imagery leads to a 15-23% improvement in map construction accuracy and a 2% gain in trajectory prediction performance. These results highlight the potential of aerial imagery as a scalable and adaptable source of environmental context in automated vehicle systems, particularly in scenarios where high-definition maps are unavailable, outdated, or costly to maintain. AID4AD, along with evaluation code and pretrained models, is publicly released to foster further research in this direction: https://github.com/DriverlessMobility/AID4AD.