Work

Trajectory-Based Road Autolabeling With Lidar-Camera Fusion in Winter Conditions

Year: 2025

Type: article

Abstract: Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their training data but cannot be trusted in out-of-distribution scenarios. Including the whole distribution in the trainset is challenging, as each sample must be labeled by hand. Trajectory-based self-supervised methods offer a potential solution as they can learn from the traversed route without manual labels. However, existing trajectory-based methods use learning schemes that rely only on the camera or only on the lidar. In this paper, trajectory-based learning is implemented jointly with lidar and camera for increased performance. Our method outperforms recent standalone camera- and lidar-based methods when evaluated with a challenging winter driving dataset, including countryside and suburban driving scenes. The source code is available at https://github.com/eerik98/lidar-camera-road-autolabeling.git. The winter driving dataset is made publicly available at https://zenodo.org/records/14856338.

Source: IEEE Access

DOI: 10.1109/access.2025.3582061

Authors: Eerik Alamikkotervo, Henrik Toikka, Kari Tammi, Risto Ojala

Institution: Aalto University

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,075