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| title: | Safety verification of Deep Neural Networks for highly automated driving |
| keywords: | deep learning, neural networks, verification, safety, automated driving, prototype development, industrial collaboration |
| topics: | Algorithms and Data Structures , Case studies and Applications , Dependability, security and performance , Software Technology |
| committee: |
Vahid Hashemi
, Ernst Moritz Hahn |
Description
The Dependable AI Systems team at AUDI AG is pursuing cutting edge multidisciplinary research in dependability analysis and safety verification of learning-based AI systems with a particular focus on providing methods and tools for the development of Audi next-generation highly automated driving. Your involvement in the pre-development of automated driving at AUDI AG enables you to gain experience in this rapidly growing field and to participate in the development of conditionally and highly automated driving functions in an environment that is both scientifically oriented and practical at the same time.
Deep neural networks have been frequently applied in various domains such as image classification; however, they can surprisingly be unstable with respect to adversarial attacks or perturbations. This has led to safety concerns of deploying deep neural networks to safety-critical systems such as autonomous driving. To account for the latter and as a part of your thesis, you will develop new concepts and algorithms to ensure robustness and safety of neural networks.
For further information, please consult the following website: