Drivable Area Segmentation in Unstructured Roads for Autonomous Vehicles based on Multi-sensor Fusion

Abstract

Drivable area segmentation is vital for autonomous vehicle driving safety, especially on unstructured roads. Mainstream drivable area algorithms are suited for structured environments, such as urban roads. However, these algorithms perform poorly in unstructured environments. This paper proposes a drivable area segmentation algorithm based on multi-sensor late-fusion for unstructured environments. The algorithm uses the visual segmentation results to correct the light detection and ranging (LiDAR) segmentation results, which can effectively solve those environments with unapparent boundary height differences. Desert experiments show that our algorithm achieves 96.02 on Intersection over Union (IoU), which is 36.75 and 38.31 higher than the LiDAR-based and the Vision-based algorithm, respectively.