{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/11412"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/11412","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Towards Safety-Assured Environments of Autonomous Robots via Intent Expressive Autonomy","abstract":"This work focuses on improving the safety of human-robot coexistence via intent expressive autonomy. Intent expression between robots and humans is crucial for developing autonomous robots and will enhance human safety. Our research investigated intent expressive autonomy with autonomous-vehicle-to-pedestrian and autonomous-vehicle-to-bicyclist feedback displays. We studied several possible options for an external vehicle display for effective nonverbal communication between an autonomous vehicle and vulnerable road users to identify a feedback module, which increases most legibility, public acceptance and trust in the autonomous vehicle's decision. The initial on-screen study focused on autonomous-vehicle-to-pedestrian communication and was extended by investigating feedback modules for vehicle-to-bicyclist communication. We validated the results from the on-screen studies with immersive Virtual Reality studies, which incorporated real-world setups to replicate pedestrian and bicyclist experiences in traffic. The results overall show that symbols should be selected over text, light, or road projection interaction modes. Further, we investigated intent-expressive autonomy with legible motion in human-robot collaboration to enhance human safety, particularly in cluttered environments. While previous research has focused on uncluttered settings, our work introduces a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Tested in a cluttered environment simulating a tool-sorting task, our approach significantly improves legible robot motion compared to the current state-of-the-art legible planner and emphasizes the need to address legible motion in cluttered environments. Additionally, we conducted a human-human study to identify key factors in expressing intent. Through the study we showed that the primary factors which people considered are: timing, direction, avoidance behavior, consistency, angles, position, speed, upper body movement, hand gestures, object proximity, and training effect. We found that legibility correlates with perceived safety, social intelligence, collaboration quality, and trust, underscoring the importance of legible motion. In conclusion, this research demonstrates the critical role of intent-expressive autonomy in enhancing human-robot interaction safety. By focusing on nonverbal communication between autonomous vehicles and vulnerable road users as well as legible motion in cluttered environments, we present important insights for developing autonomous systems that are both intuitive and safe for humans.","abstract_html":"This work focuses on improving the safety of human-robot coexistence via intent expressive autonomy. Intent expression between robots and humans is crucial for developing autonomous robots and will enhance human safety. Our research investigated intent expressive autonomy with autonomous-vehicle-to-pedestrian and autonomous-vehicle-to-bicyclist feedback displays. We studied several possible options for an external vehicle display for effective nonverbal communication between an autonomous vehicle and vulnerable road users to identify a feedback module, which increases most legibility, public acceptance and trust in the autonomous vehicle's decision. The initial on-screen study focused on autonomous-vehicle-to-pedestrian communication and was extended by investigating feedback modules for vehicle-to-bicyclist communication. We validated the results from the on-screen studies with immersive Virtual Reality studies, which incorporated real-world setups to replicate pedestrian and bicyclist experiences in traffic. The results overall show that symbols should be selected over text, light, or road projection interaction modes. Further, we investigated intent-expressive autonomy with legible motion in human-robot collaboration to enhance human safety, particularly in cluttered environments. While previous research has focused on uncluttered settings, our work introduces a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Tested in a cluttered environment simulating a tool-sorting task, our approach significantly improves legible robot motion compared to the current state-of-the-art legible planner and emphasizes the need to address legible motion in cluttered environments. Additionally, we conducted a human-human study to identify key factors in expressing intent. Through the study we showed that the primary factors which people considered are: timing, direction, avoidance behavior, consistency, angles, position, speed, upper body movement, hand gestures, object proximity, and training effect. We found that legibility correlates with perceived safety, social intelligence, collaboration quality, and trust, underscoring the importance of legible motion. In conclusion, this research demonstrates the critical role of intent-expressive autonomy in enhancing human-robot interaction safety. By focusing on nonverbal communication between autonomous vehicles and vulnerable road users as well as legible motion in cluttered environments, we present important insights for developing autonomous systems that are both intuitive and safe for humans.","abstract_has_math":false,"creators":["Schmidt-Wolf, Melanie"],"institution":null,"degree_name":null,"degree_level":"Doctorate Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Feil-Seifer, David"],"committee_chairs":[],"committee_members":["Folmer, Eelke","Papachristos, Christos","Maini, Parikshit","van Breugel, Floris"],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:45:45Z","subjects":["Autonomous Vehicles","Human-Robot Interaction","Intent Expression","Legibility","Motion Planning","Virtual Reality"],"languages":["en_US","English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarwolf.unr.edu/handle/11714/11412","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Feil-Seifer, David"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Folmer, Eelke","Papachristos, Christos","Maini, Parikshit","van Breugel, Floris"]},{"key":"dc:creator","label":"Author","values":["Schmidt-Wolf, Melanie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-02T18:40:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-02T18:40:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctorate Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Autonomous Vehicles","Human-Robot Interaction","Intent Expression","Legibility","Motion Planning","Virtual Reality"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarwolf.unr.edu/handle/11714/11412"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This work focuses on improving the safety of human-robot coexistence via intent expressive autonomy. Intent expression between robots and humans is crucial for developing autonomous robots and will enhance human safety. Our research investigated intent expressive autonomy with autonomous-vehicle-to-pedestrian and autonomous-vehicle-to-bicyclist feedback displays. We studied several possible options for an external vehicle display for effective nonverbal communication between an autonomous vehicle and vulnerable road users to identify a feedback module, which increases most legibility, public acceptance and trust in the autonomous vehicle's decision. The initial on-screen study focused on autonomous-vehicle-to-pedestrian communication and was extended by investigating feedback modules for vehicle-to-bicyclist communication. We validated the results from the on-screen studies with immersive Virtual Reality studies, which incorporated real-world setups to replicate pedestrian and bicyclist experiences in traffic. The results overall show that symbols should be selected over text, light, or road projection interaction modes. Further, we investigated intent-expressive autonomy with legible motion in human-robot collaboration to enhance human safety, particularly in cluttered environments. While previous research has focused on uncluttered settings, our work introduces a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Tested in a cluttered environment simulating a tool-sorting task, our approach significantly improves legible robot motion compared to the current state-of-the-art legible planner and emphasizes the need to address legible motion in cluttered environments. Additionally, we conducted a human-human study to identify key factors in expressing intent. Through the study we showed that the primary factors which people considered are: timing, direction, avoidance behavior, consistency, angles, position, speed, upper body movement, hand gestures, object proximity, and training effect. We found that legibility correlates with perceived safety, social intelligence, collaboration quality, and trust, underscoring the importance of legible motion. In conclusion, this research demonstrates the critical role of intent-expressive autonomy in enhancing human-robot interaction safety. By focusing on nonverbal communication between autonomous vehicles and vulnerable road users as well as legible motion in cluttered environments, we present important insights for developing autonomous systems that are both intuitive and safe for humans."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Towards Safety-Assured Environments of Autonomous Robots via Intent Expressive Autonomy"]}]}],"canonical_facts":{"dc:contributor.advisor":["Feil-Seifer, David"],"dc:contributor.committeemember":["Folmer, Eelke","Papachristos, Christos","Maini, Parikshit","van Breugel, Floris"],"dc:creator":["Schmidt-Wolf, Melanie"],"dc:date.accessioned":["2025-07-02T18:40:47Z"],"dc:date.available":["2025-07-02T18:40:47Z"],"dc:date.issued":["2025"],"dc:description.abstract":["This work focuses on improving the safety of human-robot coexistence via intent expressive autonomy. Intent expression between robots and humans is crucial for developing autonomous robots and will enhance human safety. Our research investigated intent expressive autonomy with autonomous-vehicle-to-pedestrian and autonomous-vehicle-to-bicyclist feedback displays. We studied several possible options for an external vehicle display for effective nonverbal communication between an autonomous vehicle and vulnerable road users to identify a feedback module, which increases most legibility, public acceptance and trust in the autonomous vehicle's decision. The initial on-screen study focused on autonomous-vehicle-to-pedestrian communication and was extended by investigating feedback modules for vehicle-to-bicyclist communication. We validated the results from the on-screen studies with immersive Virtual Reality studies, which incorporated real-world setups to replicate pedestrian and bicyclist experiences in traffic. The results overall show that symbols should be selected over text, light, or road projection interaction modes. Further, we investigated intent-expressive autonomy with legible motion in human-robot collaboration to enhance human safety, particularly in cluttered environments. While previous research has focused on uncluttered settings, our work introduces a measure for clutteredness based on an entropic measure of the environment, and a novel motion planner based on potential fields. Tested in a cluttered environment simulating a tool-sorting task, our approach significantly improves legible robot motion compared to the current state-of-the-art legible planner and emphasizes the need to address legible motion in cluttered environments. Additionally, we conducted a human-human study to identify key factors in expressing intent. Through the study we showed that the primary factors which people considered are: timing, direction, avoidance behavior, consistency, angles, position, speed, upper body movement, hand gestures, object proximity, and training effect. We found that legibility correlates with perceived safety, social intelligence, collaboration quality, and trust, underscoring the importance of legible motion. In conclusion, this research demonstrates the critical role of intent-expressive autonomy in enhancing human-robot interaction safety. By focusing on nonverbal communication between autonomous vehicles and vulnerable road users as well as legible motion in cluttered environments, we present important insights for developing autonomous systems that are both intuitive and safe for humans."],"dc:format":["PDF"],"dc:identifier.uri":["https://scholarwolf.unr.edu/handle/11714/11412"],"dc:language":["English"],"dc:language.iso":["en_US"],"dc:subject":["Autonomous Vehicles","Human-Robot Interaction","Intent Expression","Legibility","Motion Planning","Virtual Reality"],"dc:title":["Towards Safety-Assured Environments of Autonomous Robots via Intent Expressive Autonomy"],"dc:type":["Dissertation"],"thesis:degree_level":["Doctorate Degree"]},"updated_at":"2026-07-27T21:45:45Z"}