Safe, High-performance Motion Planning Under Uncertainty for Autonomous Driving Applications
Author(s)
Knaup, Jacob
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Abstract
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’s own dynamics or from uncertainty in other vehicles’ 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’ behaviors and model-based generative diffusion for interactive motion planning.
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Date
2024-12-08
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Dissertation