2026-09-18T21:07:29Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32991911 2026-07-15T12:03:53Z portal_693 item_type_8 month_year_07_2026

Drivable Area Segmentation on LiDAR Range View Images for Autonomous Driving Simone Bevilacqua (24399026) Autonomous Driving Deep Learning LiDAR Semantic Segmentation Drivable Area Road Line Grid Map Autonomous driving is transforming modern mobility and redefining transportation paradigms. A fundamental requirement for such systems is the detection of drivable areas around the vehicle, which enables understanding of the surrounding environment and supports the motion-planning decisions that control the vehicle. This thesis tackles this problem by implementing a segmentation pipeline to identify road regions that the vehicle can traverse, with the goal of providing a detailed representation of the road structure and its components. The central focus of this work is the segmentation of drivable areas and road markings using a neural network–based approach. The main novelty lies in the use of Range View images generated by a LiDAR sensor, which significantly differ from conventional camera images. This design choice is motivated by the stringent real-time constraints of autonomous driving, necessary to build highly responsive systems. The resulting segmentation model delivers encouraging performance in terms of both segmentation quality and inference speed. Because no LiDAR-based datasets for road segmentation are available, both training and evaluation are based both on the BDD100K dataset and on a proprietary dataset developed specifically for this thesis. To construct it, a hybrid annotation pipeline is introduced, combining automated and manual labeling methods to efficiently produce a high-quality annotated dataset. The annotation statistics gathered during this process highlight the effectiveness of this semi-automatic strategy. The final element of the proposed segmentation pipeline is a grid map that applies a perspective transformation to the model's outputs, converting the front-facing view into a bird’s-eye-view representation. Road-related classes detected by the model are then aggregated over consecutive frames, resulting in a more complete depiction of the vehicle’s surroundings that remains robust in the presence of occasional segmentation outliers. 2026-07-15T12:03:53Z Text Thesis 10.25417/uic.32991911.v1 https://figshare.com/articles/thesis/Drivable_Area_Segmentation_on_LiDAR_Range_View_Images_for_Autonomous_Driving/32991911 In Copyright