112 citations
- Technical University of MunichDE41 papers
- Blekinge Institute of TechnologySE26 papers
- Ludwig-Maximilians-Universität MünchenDE7 papers
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5 papers · 1 filter
Analysis and Optimisation of Bellman Residual Errors with Neural Function Approximation
Martin Gottwald, Sven Gronauer, Hao Shen +1
Recent development of Deep Reinforcement Learning (DRL) has demonstrated superior performance of neural networks in solving challenging problems with large or even continuous state…
Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization
Patrick Hart, Leonard Rychly, Alois Knol
Many current behavior generation methods struggle to handle real-world traffic situations as they do not scale well with complexity. However, behaviors can be learned off-line usin…
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Autonomous Driving Systems
Chih-Hong Cheng, Chung-Hao Huang, Georg Nührenberg
Can engineering neural networks be approached in a disciplined way similar to how engineers build software for civil aircraft? We present nn-dependability-kit, an open-source toolb…
Runtime Monitoring Neuron Activation Patterns
Chih-Hong Cheng, Georg Nührenberg, Hirotoshi Yasuoka
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runt…
Towards Dependability Metrics for Neural Networks
Chih-Hong Cheng, Georg Nührenberg, Chung-Hao Huang +2
Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality. An overarching challenge is to identify best safety engineering practices for…