Explore
Deep Neural Networks and Data for Automated Driving
0 Ungluers have
Faved this Work
Login to Fave
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.
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.
Rights Information
Are you the author or publisher of this work? If so, you can claim it as yours by registering as an Unglue.it rights holder.Downloads
This work has been downloaded 1 times via unglue.it ebook links.
- 1 - pdf (CC BY) at Unglue.it.
Keywords
- Autonomous driving
- deep learning
- Environment Perception
- highly automated driving
- Safety
- thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling
- thema EDItEUR::U Computing and Information Technology::UN Databases
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYT Image processing
Links
DOI: 10.1007/978-3-031-01233-4Editions