A9 Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception

TL;DR

The A9 Intersection Dataset provides comprehensive labeled LiDAR and camera data for urban roadside perception, enabling advanced research in autonomous driving with diverse scenarios and high-quality annotations.

Contribution

This paper introduces a new large-scale dataset with synchronized LiDAR and camera data, detailed 3D labels, and calibration for intersection perception tasks, filling a critical gap in infrastructure-based autonomous driving research.

Findings

Provides 4.8k images and point clouds with 57.4k labeled 3D boxes

Includes diverse road user classes and complex maneuvers

Establishes baselines for perception tasks using the dataset

Abstract

Intelligent Transportation Systems (ITS) allow a drastic expansion of the visibility range and decrease occlusions for autonomous driving. To obtain accurate detections, detailed labeled sensor data for training is required. Unfortunately, high-quality 3D labels of LiDAR point clouds from the infrastructure perspective of an intersection are still rare. Therefore, we provide the A9 Intersection Dataset, which consists of labeled LiDAR point clouds and synchronized camera images. Here, we recorded the sensor output from two roadside cameras and LiDARs mounted on intersection gantry bridges. The point clouds were labeled in 3D by experienced annotators. Furthermore, we provide calibration data between all sensors, which allow the projection of the 3D labels into the camera images and an accurate data fusion. Our dataset consists of 4.8k images and point clouds with more than 57.4k manually labeled 3D boxes. With ten object classes, it has a high diversity of road users in complex driving maneuvers, such as left and right turns, overtaking, and U-turns. In experiments, we provided multiple baselines for the perception tasks. Overall, our dataset is a valuable contribution to the scientific community to perform complex 3D camera-LiDAR roadside perception tasks. Find data, code, and more information at https://a9-dataset.com.