FRED: A Multi-Modal Autonomous Driving Dataset for Flooded Road Environments

TL;DR

FRED is a comprehensive multi-modal dataset designed for autonomous driving in flooded environments, including diverse sensor data and semantic labels to facilitate water hazard detection and localization tasks.

Contribution

It introduces the first multi-modal dataset specifically capturing flooded road scenarios with synchronized sensor data and semantic labels for autonomous driving research.

Findings

Dataset includes images, LiDAR point clouds, and IMU data from flooded scenarios.

Provides semantic labels for water hazard detection.

Supports development of sensor-fusion and location-based detection methods.

Abstract

The Flooded Road Environments Dataset (FRED) is, to our knowledge, the first multi-modal autonomous driving dataset specifically targeting the collection of data from scenarios involving water hazards on the road. The dataset contains images from a 2.3 MP FLIR Blackfly USB3 camera, 64-beam 360$^\circ$ point clouds from an Ouster OS1-64 LiDAR, and data from an iXblue ATLANS-C IMU corrected by a Geoflex RTK GNSS, from five separate locations captured both during and after flooding events. The data has been released in two formats: a KITTI-style format for easy integration with existing data tools, and the RTMaps format for direct replay of the vehicle's data capture. We provide semantic labels to enable the training and evaluation of both single-sensor and sensor-fusion methods for water hazard detection. Position and velocity, as well as data captured under dry conditions, are provided to enable the development of location-based detection methods that may incorporate maps, and to evaluate other tasks such as localisation and SLAM.