RBF learning in a non-stationary environment: the stability-plasticity dilemma
Radial basis function networks 1
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Towards perceptual intelligence: statistical modeling of human individual and interactive behaviors
Towards perceptual intelligence: statistical modeling of human individual and interactive behaviors
Autonomous Robots
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems
Further Steps towards Driver Modeling According to the Bayesian Programming Approach
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Further Steps towards Driver Modeling According to the Bayesian Programming Approach
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Hybrid Automata as a Modelling Approach in the Behavioural Sciences
Electronic Notes in Theoretical Computer Science (ENTCS)
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The Human Centered Design (HCD) of Partial Autonomous Driver Assistance Systems (PADAS) requires Digital Human Models (DHMs) of human control strategies for simulations of traffic scenarios. The scenarios can be regarded as problem situations with one or more (partial) cooperative problem solvers. According to their roles models can be descriptive or normative . We present new model architectures and applications and discuss the suitability of dynamic Bayesian networks as control models of traffic agents: Bayesian Autonomous Driver (BAD) models. Descriptive BAD models can be used for simulating human agents in conventional traffic scenarios with Between-Vehicle-Cooperation (BVC) and in new scenarios with In-Vehicle-Cooperation (IVC). Normative BAD models representing error free behavior of ideal human drivers (e.g. driving instructors) may be used in these new IVC scenarios as a first Bayesian approximation or prototype of a PADAS.