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The Fifth Driver is a project for the Adaptative Computing Developer Contest for Xilinx. We pretend to demonstrate that the Xilinx Ultrascale+ MPSoC architecture is suitable for developing machine learning applications applied to Autonomous Driving.

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Fifth_Driver

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The fifth driver

The Fifth Driver is a project for the Adaptative Computing Developer Contest for Xilinx. We pretend to demonstrate that the Xilinx Ultrascale+ MPSoC architecture is suitable for developing machine learning applications applied to Autonomous Driving.

Table of Contents

Contributing

This project exists thanks to Xilinx and Hackster for promoting and supporting the Adaptative Computing Developer Contest with hardware and interactive expert technical support. We also would like to thank to the reviewers of this work for porting and validating our findings.

Contributors and maintainers

Javier Acevedo - Dresden - Project maintainer and hardware developer - jracevedob@gmail.com - acevedo@fifthdriverai.com

Cristian Perez - Paris - Project maintainer and software developer - felipebrokate@gmail.com - pbrokate@fifthdriverai.com

Citations

Published internet articles

https://developer.xilinx.com/en/articles/the-fifth-driver-ai--hardware-based-accelerators-for-adas.html https://www.hackster.io/javier-cristian/thefifthdriver-machine-learning-driving-assistance-on-fpga-98f295

Academic publications

@Article{Acevedo2021,
AUTHOR = {Acevedo, Javier and Pérez Brokate, Cristian and Andy Luo},
TITLE = {The Fifth Driver AI: Hardware Accelerators for Autonomous Driving and ADAS},
JOURNAL = {Electronics},
VOLUME = {},
YEAR = {2021},
NUMBER = {},
ARTICLE-NUMBER = {180},
URL = {},
ISSN = {},
ABSTRACT = {}
}

Press release

License

This project is licensed under the MIT license.

News

06.07.2021 - Project deployment on real testbed. Vitis AI + Docker

About

The Fifth Driver is a project for the Adaptative Computing Developer Contest for Xilinx. We pretend to demonstrate that the Xilinx Ultrascale+ MPSoC architecture is suitable for developing machine learning applications applied to Autonomous Driving.

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