Theses and Dissertations

Author

Andrew Hegman

Issuing Body

Mississippi State University

Advisor

Shi, Jian

Committee Member

Fowler, James E.

Committee Member

Mazzola, Michael

Date of Degree

8-1-2018

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

Abstract

Collision avoidance is an essential capability for autonomous and assisted-driving ground vehicles. In this work, we developed a novel model predictive control based intelligent collision avoidance (CA) algorithm for a multi-trailer industrial ground vehicle implemented on a General Purpose Graphical Processing Unit (GPGPU). The CA problem is formulated as a multi-objective optimal control problem and solved using a limited look-ahead control scheme in real-time. Through hardware-in-the-loop-simulations and experimental results obtained in this work, we have demonstrated that the proposed algorithm, using NVIDA’s CUDA framework and the NVIDIA Jetson TX2 development platform, is capable of dynamically assisting drivers and maintaining the vehicle a safe distance from the detected obstacles on-thely. We have demonstrated that a GPGPU, paired with an appropriate algorithm, can be the key enabler in relieving the computational burden that is commonly associated with model-based control problems and thus make them suitable for real-time applications.

URI

https://hdl.handle.net/11668/21003

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