Date of Award

6-18-2025

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2025

Department

Computer Science and Engineering

First Advisor

David C. Anastasiu

Second Advisor

Radhika Grover

Abstract

In the real world, vehicular crashes are the result of human error and inadequate reaction time and are often deadly. Due to the issue of accidents being frequent occurrences, using machine learning to drive vehicles has quickly become a relevant topic as it could potentially become a way to minimize fatalities and damages. Thus, the project aims to approach self-automation with the focus on maximizing the vehicle’s performance and the machine’s ability to make the best decisions within that time.

Today’s self-driving cars use radar or cameras as well as digital signal processing algorithms to sense the environment while using machine learning algorithms to process that data to make informed decisions. Currently, the bulk of these algorithms run in software. This implementation is simpler than in hardware and is often the chosen method due to the already complex nature of the software stack. However, it incurs more overhead than hardware implementations in terms of performance which is an especially pressing issue in the design of these time and safety-critical systems.

This project aims to implement vehicle lane detection into hardware using the Xilinx PYNQ-Z2 FPGA board in order to produce an autonomous robot. To gather data from the environment, a camera onboard the robot captures and transfers frames as input data to the FPGA board. Image processing algorithms are then used to process the information and make decisions based on the results. At this stage, the usage of hardware acceleration maximizes the performance speed of these algorithms in which the computation load is transferred to the FPGA fabric from the CPU. Finally, based on the decision made, the board controls the motors driving power to the wheels in order to direct the movement of the robot.

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