An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion

TL;DR

This paper presents a robust perception system for autonomous vehicles that fuses LiDAR, camera, and HD map data to accurately and reliably generate safe drivable space across diverse weather conditions, including snow.

Contribution

It introduces an adaptive perception module with innovative ground removal, curb detection, and clustering methods, enhancing generalization and safety in autonomous driving.

Findings

Improved drivable space detection accuracy in various weather conditions.

Enhanced obstacle detection reliability through HD map integration.

Validated system performance on real-world datasets and daily shuttle operation.

Abstract

In this paper, we propose an accurate and robust perception module for Autonomous Vehicles (AVs) for drivable space extraction. Perception is crucial in autonomous driving, where many deep learning-based methods, while accurate on benchmark datasets, fail to generalize effectively, especially in diverse and unpredictable environments. Our work introduces a robust easy-to-generalize perception module that leverages LiDAR, camera, and HD map data fusion to deliver a safe and reliable drivable space in all weather conditions. We present an adaptive ground removal and curb detection method integrated with HD map data for enhanced obstacle detection reliability. Additionally, we propose an adaptive DBSCAN clustering algorithm optimized for precipitation noise, and a cost-effective LiDAR-camera frustum association that is resilient to calibration discrepancies. Our comprehensive drivable space representation incorporates all perception data, ensuring compatibility with vehicle dimensions and road regulations. This approach not only improves generalization and efficiency, but also significantly enhances safety in autonomous vehicle operations. Our approach is tested on a real dataset and its reliability is verified during the daily (including harsh snowy weather) operation of our autonomous shuttle, WATonoBus