{"681416":{"#nid":"681416","#data":{"type":"event","title":"PhD Defense by Ji Yin","body":[{"value":"\u003Cp\u003E\u003Cbr\u003EJi Yin\u003Cbr\u003E(Advisor: Prof. Panagiotis Tsiotras]\u003Cbr\u003Ewill defend a doctoral thesis entitled,\u003Cbr\u003ESafe and Efficient Variational Inference Model Predictive Control with Application to Aggressive Autonomous Driving\u003Cbr\u003EOn\u003Cbr\u003ETuesday, April 1 at 9:00 a.m.\u003Cbr\u003EVirtual\u003Cbr\u003EZoom Link\u003Cbr\u003EAbstract\u003Cbr\u003EAutonomous driving demands a careful balance between optimal performance, safety, and real-time feasibility. Variational Inference Model Predictive Control (VIMPC), particularly Model Predictive Path Integral (MPPI) control, offers flexibility in handling nonlinear dynamics but is hindered by high computational costs, a lack of risk-awareness, and the absence of formal safety guarantees. This research enhances VIMPC by improving sampling efficiency, incorporating risk-aware decision-making, and enforcing formal safety constraints. To optimize trajectory sampling, Covariance-controlled MPPI (CC-MPPI) dynamically adjusts the covariance of its sampling distribution, significantly enhancing efficiency. Risk-aware MPPI (RA-MPPI) integrates Conditional Value-at-Risk (CVaR) to penalize high-risk trajectories, improving decision-making under uncertainty. Shield MPPI enforces safety through Control Barrier Function (CBF) constraints, extending to Belief-space Stochastic MPPI (BSS-MPPI) for robust planning under state estimation uncertainty. Finally, Neural Shield MPPI (NS-MPPI) leverages Neural Control Barrier Functions (NCBF) to further enhance sampling efficiency and overall autonomous driving performance. Validated on 1\/28-scale BuzzRacer and 1\/5-scale AutoRally platforms, these methods significantly improve efficiency, risk-awareness, and safety, making VIMPC a more viable solution for real-time, high-performance autonomous driving.\u003C\/p\u003E\u003Cp\u003ECommittee\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Dr. Panagiotis Tsiotras (Advisor), Daniel Guggenheim School of Aerospace Engineering, Georgia Tech\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Dr. Samuel Coogan, School of Electrical and Computer Engineering, Georgia Tech\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Dr. Ye Zhao, George Woodruff School of Mechanical Engineering, Georgia Tech\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Dr. Shreyas Kousik, George W. Woodruff School of Mechanical Engineering, Georgia Tech\u003Cbr\u003E\u2022\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Dr. Chuchu Fan, Aeronautics and Astronautics, Massachusetts Institute of Technology\u003Cbr\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESafe and Efficient Variational Inference Model Predictive Control with Application to Aggressive Autonomous Driving\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Safe and Efficient Variational Inference Model Predictive Control with Application to Aggressive Autonomous Driving"}],"uid":"27707","created_gmt":"2025-03-27 15:49:05","changed_gmt":"2025-03-27 15:49:39","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-01T09:00:00-04:00","event_time_end":"2025-04-01T10:00:00-04:00","event_time_end_last":"2025-04-01T10:00:00-04:00","gmt_time_start":"2025-04-01 13:00:00","gmt_time_end":"2025-04-01 14:00:00","gmt_time_end_last":"2025-04-01 14:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Virtual Zoom Link","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":""}}}