Electronic Resource

DEEP NEURAL NETWORKS AND DATA FOR AUTOMATED DRIVING : ROBUSTNESS, UNCERTAINTY QUANTIFICATION, AND INSIGHTS TOWARDS SAFETY

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This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence.

Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety?

This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

629.2 TIM d

Penerbit

inggris : Springer Cham., 2022

Deskripsi Fisik

XVIII, 427 ; 103 illustrations in colour

Bahasa

English

ISBN/ISSN

978-3-031-01233-4

Klasifikasi

629.2

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

1

Subjek

AUTOMOTIVE ENGINEERING
DATA ENGINEERING
IMAGE PROCESSING AND COMPUTER VISION

Info Detail Spesifik

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Pernyataan Tanggungjawab

TIM FINGSCHEIDT

Versi lain/terkait

Lampiran Berkas

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