الصفحة 1
الصفحة 1
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety
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.
Computer vision and machine learning for intelligent sensing systems
Offers a selection of high-quality research articles that tackle the major difficulties in computer vision and machine learning for intelligent sensing systems from both theoretical and practical standpoints. This publication includes intelligent sensing techniques, twelve foundational investigations into sense-making methods, and discusses particular uses of intelligent sensing systems in autonomous driving and virtual reality.
Autonomous driving : Technical, legal and social aspects
This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies?
Autonomes fahren : Technische, rechtliche und gesellschaftliche aspekte = Autonomous driving : Technical, legal and social aspects
This book provides answers to a wide range of these and other questions. Experts from Germany and the USA describe central topics related to the automation of vehicles on public roads from an engineering and social science perspective. They show which "decisions" are required of an autonomous vehicle or which "ethics" must be programmed. The authors discuss expectations and concerns that characterize the individual and societal acceptance of autonomous driving. An increased safety potential through autonomous vehicles is compared to the challenges and solution approaches that play a role in securing the safety concept. In addition, they explain what possibilities for change and opportunities arise for our mobility and the reorganization of traffic, not least for freight traffic. The book thus offers an up-to-date, comprehensive and scientifically sound examination of the topic of "autonomous driving".
Advances in artificial intelligence: models, optimization, and machine learning
Contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems.
