Authors: Minghao Ning, Ahmad Reza Alghooneh, Chen Sun, Ruihe Zhang, Pouya Panahandeh, Steven Tuer, Ehsan Hashemi, Amir Khajepour
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
Source: http://arxiv.org/abs/2410.22314v1
