Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments
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 un...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/150099 |
| _version_ | 1811079194840399872 |
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| author | Han, Weiqiao |
| author2 | Williams, Brian C. |
| author_facet | Williams, Brian C. Han, Weiqiao |
| author_sort | Han, Weiqiao |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:11:22Z |
| format | Thesis |
| id | mit-1721.1/150099 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:11:22Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1500992023-04-01T03:41:03Z Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments Han, Weiqiao Williams, Brian C. Jasour, Ashkan Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. 2023-03-31T14:31:57Z 2023-03-31T14:31:57Z 2023-02 2023-02-28T14:39:35.168Z Thesis https://hdl.handle.net/1721.1/150099 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Han, Weiqiao Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments |
| title | Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments |
| title_full | Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments |
| title_fullStr | Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments |
| title_full_unstemmed | Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments |
| title_short | Risk Aware Planning and Probabilistic Prediction for Autonomous Systems under Uncertain Environments |
| title_sort | risk aware planning and probabilistic prediction for autonomous systems under uncertain environments |
| url | https://hdl.handle.net/1721.1/150099 |
| work_keys_str_mv | AT hanweiqiao riskawareplanningandprobabilisticpredictionforautonomoussystemsunderuncertainenvironments |