{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/135434"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/135434","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Terrain and Vehicle-Terrain Sensing and Estimation in Real-Time for Use in Autonomous Vehicles","abstract":"Autonomous vehicles are being used more and more every day. While there are many applications for autonomy in off-road scenarios terrain, such as in military and agriculture, most autonomous models focus on on-road applications and do not account for the effect of deformable terrains. This project focuses on obtaining information about vehicle-terrain interaction in real-time, so it can be used in an autonomous model in the future. This is done mainly in two ways: direct sensing and parameter estimation. In the area of direct sensing the tire rut depth left behind by a tire is measured using a system of two Intel RealSense D405 stereo cameras. The rut depth can be assumed to be the same as the tire sinkage in this application and is then used to get the tire entry angle. The tire entry angle can then be used to further obtain the drawbar pull and the tractive effort. The cameras were experimentally tested and validated using the Terramechanics testing rig at the TMVS laboratory at Virgina Tech. Both single pass and multi-pass scenarios were tested and the results analyzed. The terrain tested was GRC-1 lunar simulant, sandy loam, and clay. In the area of parameter estimation, the estimation model of interest is the generalized polynomial chaos extended Kalman filter (gPC-EKF). This filter is used to estimate the vehicle and tire slip angles, as well as the yaw rate using a regression model. Project Chrono was used to collect data from a FED Alpha for the filter.","abstract_html":"Autonomous vehicles are being used more and more every day. While there are many applications for autonomy in off-road scenarios terrain, such as in military and agriculture, most autonomous models focus on on-road applications and do not account for the effect of deformable terrains. This project focuses on obtaining information about vehicle-terrain interaction in real-time, so it can be used in an autonomous model in the future. This is done mainly in two ways: direct sensing and parameter estimation. In the area of direct sensing the tire rut depth left behind by a tire is measured using a system of two Intel RealSense D405 stereo cameras. The rut depth can be assumed to be the same as the tire sinkage in this application and is then used to get the tire entry angle. The tire entry angle can then be used to further obtain the drawbar pull and the tractive effort. The cameras were experimentally tested and validated using the Terramechanics testing rig at the TMVS laboratory at Virgina Tech. Both single pass and multi-pass scenarios were tested and the results analyzed. The terrain tested was GRC-1 lunar simulant, sandy loam, and clay. In the area of parameter estimation, the estimation model of interest is the generalized polynomial chaos extended Kalman filter (gPC-EKF). This filter is used to estimate the vehicle and tire slip angles, as well as the yaw rate using a regression model. Project Chrono was used to collect data from a FED Alpha for the filter.","abstract_has_math":false,"creators":["White, Hannah Nicole"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Sandu, Corina","L'Afflitto, Andrea"],"committee_members":["Southward, Steve C.","Akbari Hamed, Kaveh","Gorsich, David J."],"year":2025,"date_issued":"2025-06-09","date_published":"2025-06-09","updated_at":"2026-07-24T05:56:09Z","subjects":["Terramechanics","Terrain-Vehicle Interaction","Sensing","Stereo Cameras","Estimation"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:43898"],"render_values":[{"text":"vt_gsexam:43898","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/135434","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Sandu, Corina","L'Afflitto, Andrea"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Southward, Steve C.","Akbari Hamed, Kaveh","Gorsich, David J."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["White, Hannah Nicole"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-06-10T08:01:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-06-10T08:01:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-06-09"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Terramechanics","Terrain-Vehicle Interaction","Sensing","Stereo Cameras","Estimation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:43898"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/135434"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Autonomous vehicles are being used more and more every day. While there are many applications for autonomy in off-road scenarios terrain, such as in military and agriculture, most autonomous models focus on on-road applications and do not account for the effect of deformable terrains. This project focuses on obtaining information about vehicle-terrain interaction in real-time, so it can be used in an autonomous model in the future. This is done mainly in two ways: direct sensing and parameter estimation. In the area of direct sensing the tire rut depth left behind by a tire is measured using a system of two Intel RealSense D405 stereo cameras. The rut depth can be assumed to be the same as the tire sinkage in this application and is then used to get the tire entry angle. The tire entry angle can then be used to further obtain the drawbar pull and the tractive effort. The cameras were experimentally tested and validated using the Terramechanics testing rig at the TMVS laboratory at Virgina Tech. Both single pass and multi-pass scenarios were tested and the results analyzed. The terrain tested was GRC-1 lunar simulant, sandy loam, and clay. In the area of parameter estimation, the estimation model of interest is the generalized polynomial chaos extended Kalman filter (gPC-EKF). This filter is used to estimate the vehicle and tire slip angles, as well as the yaw rate using a regression model. Project Chrono was used to collect data from a FED Alpha for the filter."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["While the popularity of autonomous vehicles has been growing in recent years, the area of autonomy in off-roading is still an area with a lot of room for growth. When considering off-road vs. on-road vehicles, the terrain has a large effect on how the vehicle moves. While on-road vehicles typically have a very solid and constant terrain, off-road vehicles traverse over several types of terrain, such as soil, sand, or mud, all of which deforms. Due to this, off-road autonomy needs to consider the conditions of the terrain to be able to properly drive over it. This work focuses on how to directly measure or estimate vehicle-terrain interaction parameters. This experiment uses a set of stereo cameras to measure the rut left behind by a tire on a Terramechanics testing rig. These cameras have been used to collect data from the tire driving over the terrain once, as well as multiple times. Three different types of terrain have been used in testing: a dry, fine lunar simulant, sandy loam, and clay. The tire sinkage was then used to find the entry angle of the tire, which is an important variable for finding the forces that the vehicle uses and has available for towing. When the desired parameters are unable to be directly measured, they can be estimated. This study looks at estimating front and rear tire slip angles, vehicle slip angle, and the yaw rate."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Terrain and Vehicle-Terrain Sensing and Estimation in Real-Time for Use in Autonomous Vehicles"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Sandu, Corina","L'Afflitto, Andrea"],"dc:contributor.committeemember":["Southward, Steve C.","Akbari Hamed, Kaveh","Gorsich, David J."],"dc:contributor.department":["Mechanical Engineering"],"dc:creator":["White, Hannah Nicole"],"dc:date.accessioned":["2025-06-10T08:01:57Z"],"dc:date.available":["2025-06-10T08:01:57Z"],"dc:date.issued":["2025-06-09"],"dc:description.abstract":["Autonomous vehicles are being used more and more every day. While there are many applications for autonomy in off-road scenarios terrain, such as in military and agriculture, most autonomous models focus on on-road applications and do not account for the effect of deformable terrains. This project focuses on obtaining information about vehicle-terrain interaction in real-time, so it can be used in an autonomous model in the future. This is done mainly in two ways: direct sensing and parameter estimation. In the area of direct sensing the tire rut depth left behind by a tire is measured using a system of two Intel RealSense D405 stereo cameras. The rut depth can be assumed to be the same as the tire sinkage in this application and is then used to get the tire entry angle. The tire entry angle can then be used to further obtain the drawbar pull and the tractive effort. The cameras were experimentally tested and validated using the Terramechanics testing rig at the TMVS laboratory at Virgina Tech. Both single pass and multi-pass scenarios were tested and the results analyzed. The terrain tested was GRC-1 lunar simulant, sandy loam, and clay. In the area of parameter estimation, the estimation model of interest is the generalized polynomial chaos extended Kalman filter (gPC-EKF). This filter is used to estimate the vehicle and tire slip angles, as well as the yaw rate using a regression model. Project Chrono was used to collect data from a FED Alpha for the filter."],"dc:description.abstractgeneral":["While the popularity of autonomous vehicles has been growing in recent years, the area of autonomy in off-roading is still an area with a lot of room for growth. When considering off-road vs. on-road vehicles, the terrain has a large effect on how the vehicle moves. While on-road vehicles typically have a very solid and constant terrain, off-road vehicles traverse over several types of terrain, such as soil, sand, or mud, all of which deforms. Due to this, off-road autonomy needs to consider the conditions of the terrain to be able to properly drive over it. This work focuses on how to directly measure or estimate vehicle-terrain interaction parameters. This experiment uses a set of stereo cameras to measure the rut left behind by a tire on a Terramechanics testing rig. These cameras have been used to collect data from the tire driving over the terrain once, as well as multiple times. Three different types of terrain have been used in testing: a dry, fine lunar simulant, sandy loam, and clay. The tire sinkage was then used to find the entry angle of the tire, which is an important variable for finding the forces that the vehicle uses and has available for towing. When the desired parameters are unable to be directly measured, they can be estimated. This study looks at estimating front and rear tire slip angles, vehicle slip angle, and the yaw rate."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:43898"],"dc:identifier.uri":["https://hdl.handle.net/10919/135434"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Terramechanics","Terrain-Vehicle Interaction","Sensing","Stereo Cameras","Estimation"],"dc:title":["Terrain and Vehicle-Terrain Sensing and Estimation in Real-Time for Use in Autonomous Vehicles"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-24T05:56:09Z"}