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 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.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Han, Weiqiao

Advisors dc:contributor.advisor

• Williams, Brian C.
• Jasour, Ashkan

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/150099

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/150099