Logo eprints

Learning-based Stochastic Model Predictive Control for Autonomous Driving

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]

[img] Text (Doctoral thesis)
Surya Soman's PhD_thesis_final.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (4MB)


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

Actions (login required, only for staff repository)

View Item View Item