Soman, Surya (2023) Learning-based Stochastic Model Predictive Control for Autonomous Driving. Advisor: Bemporad, Prof. Alberto. Coadvisor: Zanon, Prof. Mario . pp. 80. [IMT PhD Thesis]
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Text (Doctoral thesis)
Surya Soman's PhD_thesis_final.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (4MB) |
Abstract
Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving obstacles, for instance, the intention of other vehicles while crossing an uncontrolled intersection. This thesis addresses the aforementioned problem by proposing a stochastic model predictive control (SMPC) approach. In this approach, we consider robust collision avoidance as a constraint to guarantee safety and a stochastic performance index that will increase the quality of the closed-loop tracking by ignoring the unlikely obstacle configurations that could occur. We compute the probabilities associated with different obstacle trajectories by training a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation in a simulated real intersection. This thesis is divided into two parts: first, discuss the formulation of the existing control algorithm and our proposed approach, and second, the scenario prediction of the obstacle vehicles.
| Item Type: | IMT PhD Thesis |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| PhD Course: | Computer science and systems engineering |
| Identification Number: | https://doi.org/10.13118/imtlucca/e-theses/383 |
| NBN Number: | urn:nbn:it:imtlucca-29374 |
| Date Deposited: | 21 Jul 2023 08:33 |
| URI: | http://e-theses.imtlucca.it/id/eprint/383 |
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