PathFormer: A Transformer-Based Framework for Vision-Centric Autonomous Navigation in Off-Road Environments

Abstract

The efficient navigation of autonomous vehicles across rugged and unstructured terrains remains a significant challenge. Most existing research in this area emphasizes the need for complex mappings or intricate multi-step methodologies. However, these traditional approaches often struggle to adapt to dynamic changes in environmental conditions. In this paper, we introduce PathFormer, an end-to-end framework designed specifically to address these challenges. PathFormer utilizes transformers to decode free-space semantics and configurations directly from camera images, enabling efficient path planning without the reliance on detailed, pre-existing maps. The performance of PathFormer was rigorously evaluated across diverse datasets, where it demonstrated superior capabilities, outperforming other state-of-the-art methods by 3.68% in precisely segmenting free-space regions and showing a 13.65% improvement in correctly predicting traversable paths.