Tag: Automotive

April 26, 2024

Vehicle and Automotive Engineering 2 Proceedings of the 2nd VAE2018, Miskolc, Hungary (2024)

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Free Download Vehicle and Automotive Engineering 2: Proceedings of the 2nd VAE2018, Miskolc, Hungary By Károly Jármai
English | PDF,EPUB | 2018 | 801 Pages | ISBN : 3319756761 | 156.06 MB
This book presents the proceedings of the second Vehicle Engineering and Vehicle Industry conference, reflecting the outcomes of theoretical and practical studies and outlining future development trends in a broad field of automotive research.

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January 30, 2024

Proceedings of the 19th Asia Pacific Automotive Engineering Conference & SAE-China Congress 2017 Selected Papers (2024)

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Free Download Proceedings of the 19th Asia Pacific Automotive Engineering Conference & SAE-China Congress 2017: Selected Papers by Society of Automotive Engineers (SAE-China)
English | PDF,EPUB | 2018 (2019 Edition) | 1158 Pages | ISBN : 9811085056 | 106.24 MB
This Proceedings volume gathers outstanding papers submitted to the 19th Asia Pacific Automotive Engineering Conference & 2017 SAE-China Congress, the majority of which are from China – the largest car-maker as well as most dynamic car market in the world. The book covers a wide range of automotive topics, presenting the latest technical advances and approaches to help technicians solve the practical problems that most affect their daily work.

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January 30, 2024

Deep Learning Methods for Automotive Radar Signal Processing

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Free Download Rodrigo Pérez González, "Deep Learning Methods for Automotive Radar Signal Processing"
English | 2021 | ISBN: 3736974620 | PDF | pages: 137 | 9.6 mb
For autonomous driving to become a reality, future sensor systems must be able to not only capture the vehicle’s environment, but also to provide semantic information. In this work, deep learning methods, meant to enhance-or even replace-the classical radar signal processing chain, are developed and evaluated in the context of automotive applications. For this purpose, state of the art computer vision approaches are adapted and applied to radar signals in order to detect and classify different road users.

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