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oai:dspace.mit.edu:1721.1/1500992023-04-01T03:41:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Williams, Brian C. Jasour, Ashkan Han, Weiqiao Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2023-03-31T14:31:57Z 2023-03-31T14:31:57Z 2023-02 2023-02-28T14:39:35.168Z https://hdl.handle.net/1721.1/150099 This thesis considers risk aware planning and probabilistic prediction for autonomous systems under uncertain environments. Motion planning under uncertainty looks for trajectories with bounded probability of collision with uncertain obstacles. Existing methods to address motion planning problems under uncertainty are either limited to Gaussian uncertainties and convex linear obstacles, or rely on sampling based methods that need uncertainty samples. In this thesis, we consider non-convex uncertain obstacles, stochastic nonlinear systems, and non-Gaussian uncertainty. We utilize concentration inequalities, higher order moments, and risk contours to handle non-Gaussian uncertainties. Without considering dynamics, we use RRT to plan trajectories together with SOS programming to verify the safety of the trajectory. Considering stochastic nonlinear dynamics, we solve nonlinear programming problems in terms of moments of random variables and controls using off-the-self solvers to generate trajectories with guaranteed bounded risk. Then we consider trajectory prediction for autonomous vehicles. We propose a hierarchical end-to-end deep learning framework for autonomous driving trajectory prediction: Keyframe MultiPath (KEMP). Our model is not only more general but also simpler than previous methods. Our model achieves state-of-the-art performance in autonomous driving trajectory prediction tasks. Ph.D. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments Thesis application/pdf Doctoral Doctor of Philosophy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="5a51fb82-5f87-405b-814f-20a8c020e270"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2023-02</PublicationDate> <Authors> <Author> <DisplayName>Han, Weiqiao</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>This thesis considers risk aware planning and probabilistic prediction for autonomous systems under uncertain environments. Motion planning under uncertainty looks for trajectories with bounded probability of collision with uncertain obstacles. Existing methods to address motion planning problems under uncertainty are either limited to Gaussian uncertainties and convex linear obstacles, or rely on sampling based methods that need uncertainty samples. In this thesis, we consider non-convex uncertain obstacles, stochastic nonlinear systems, and non-Gaussian uncertainty. We utilize concentration inequalities, higher order moments, and risk contours to handle non-Gaussian uncertainties. Without considering dynamics, we use RRT to plan trajectories together with SOS programming to verify the safety of the trajectory. Considering stochastic nonlinear dynamics, we solve nonlinear programming problems in terms of moments of random variables and controls using off-the-self solvers to generate trajectories with guaranteed bounded risk. Then we consider trajectory prediction for autonomous vehicles. We propose a hierarchical end-to-end deep learning framework for autonomous driving trajectory prediction: Keyframe MultiPath (KEMP). Our model is not only more general but also simpler than previous methods. Our model achieves state-of-the-art performance in autonomous driving trajectory prediction tasks.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>