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.

About the Author

Jeonghoon Mo received the B.S. degree from Seoul National University, Korea, and the M.S. and Ph.D. degrees from the University of California, Berkeley. He is currently a professor in the department of Information and Industrial Engineering at Yonsei University, Korea. Before joining Yonsei, he has previously worked at AT&T Labs, Tera Blaze, and KAIST. His research interests include network economics, wireless communications and mobile services, optimization, game theory, and performance analysis.

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