{"678183":{"#nid":"678183","#data":{"type":"event","title":"PhD Defense by Jacob Knaup ","body":[{"value":"\u003Cp\u003ESafe, High-performance Motion Planning Under Uncertainty for Autonomous Driving Applications\u003Cbr\u003E\u003Cbr\u003EJacob Knaup | Ph.D. Student, Institute for Robotics and Intelligent Machines at Georgia Institute of Technology\u0026nbsp;\u003Cbr\u003EMonday November 18 @ 1:30pm in Montgomery Knight 317\u0026nbsp;\u003Cbr\u003E\u003Cbr\u003EAbstract: Autonomous driving is a high-performance, safety-critical task, wherein a robot must balance the trade-off between driving aggressively to meet its objectives while maintaining a suitable level of safety to avoid crashes. This challenge is further exacerbated by the necessity of maintaining this balance while dealing with uncertainty in the environment, whether from uncertainty in the autonomous vehicle\u2019s own dynamics or from uncertainty in other vehicles\u2019 behaviors. This dissertation will present theoretical and experimental results from two novel methods for motion planning for uncertain systems that integrate ideas from the model-based and learning-based stochastic optimal control communities. The first utilizes a convex programming-based stochastic model-predictive control method for aggressive off-road autonomous racing. The second addresses a highway merge scenario and employs active learning of other drivers\u2019 behaviors and model-based generative diffusion for interactive motion planning.\u003C\/p\u003E\u003Cp\u003EThesis committee:\u003Cbr\u003EDr. Panagiotis Tsiotras\u003Cbr\u003EThe Daniel Guggenheim School of Aerospace Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Samuel Coogan\u003Cbr\u003ESchool of Electrical and Computer Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Kyriakos Vamvoudakis\u003Cbr\u003EThe Daniel Guggenheim School of Aerospace Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Ye Zhao\u003Cbr\u003EGeorge W. Woodruff School of Mechanical Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003EDr. Francesco Borrelli\u003Cbr\u003EDepartment of Mechanical Engineering\u003Cbr\u003EUniversity of California, Berkeley\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003ESafe, High-performance Motion Planning Under Uncertainty for Autonomous Driving Applications\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Safe, High-performance Motion Planning Under Uncertainty for Autonomous Driving Applications"}],"uid":"27707","created_gmt":"2024-11-05 17:28:20","changed_gmt":"2024-11-05 17:28:48","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-11-18T13:30:00-05:00","event_time_end":"2024-11-18T16:00:00-05:00","event_time_end_last":"2024-11-18T16:00:00-05:00","gmt_time_start":"2024-11-18 18:30:00","gmt_time_end":"2024-11-18 21:00:00","gmt_time_end_last":"2024-11-18 21:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Montgomery Knight 317   ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}