{"id":{"repo_id":"uic","oai_identifier":"oai:figshare.com:article/32991911"},"canonical_url":"https://search.dev.ndltd.org/etd/uic/oai:figshare.com:article/32991911","repository":{"repo_id":"uic","name":"University of Illinois - Chicago","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"Drivable Area Segmentation on LiDAR Range View Images for Autonomous Driving","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Simone Bevilacqua (24399026)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-07-15T12:03:53Z","date_published":"2026-07-15T12:03:53Z","updated_at":"2026-07-27T21:33:06Z","subjects":["Autonomous Driving","Deep Learning","LiDAR","Semantic Segmentation","Drivable Area","Road Line","Grid Map"],"languages":[],"rights":["In Copyright"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.25417/uic.32991911.v1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Simone Bevilacqua (24399026)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-07-15T12:03:53Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/Drivable_Area_Segmentation_on_LiDAR_Range_View_Images_for_Autonomous_Driving/32991911"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Autonomous Driving","Deep Learning","LiDAR","Semantic Segmentation","Drivable Area","Road Line","Grid Map"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25417/uic.32991911.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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."]},{"key":"dc:title","label":"Title","values":["Drivable Area Segmentation on LiDAR Range View Images for Autonomous Driving"]}]}],"canonical_facts":{"dc:creator":["Simone Bevilacqua (24399026)"],"dc:date":["2026-07-15T12:03:53Z"],"dc:description":["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. 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