Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
RBF learning in a non-stationary environment: the stability-plasticity dilemma
Radial basis function networks 1
Autonomous Robots
Learning Bayesian Networks
Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems
Probabilistic Reasoning and Decision Making in Sensory-Motor Systems
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic and Empirical Grounded Modeling of Agents in (Partial) Cooperative Traffic Scenarios
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Probabilistic and Empirical Grounded Modeling of Agents in (Partial) Cooperative Traffic Scenarios
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
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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 simulating traffic scenarios. We describe first results to model lateral and longitudinal control behavior of drivers with simple dynamic Bayesian sensory-motor models according to the Bayesian Programming (BP) approach: Bayesian Autonomous Driver (BAD) models. BAD models are learnt from multivariate time series of driving episodes generated by single or groups of users. The variables of the time series describe phenomena and processes of perception, cognition, and action control of drivers. BAD models reconstruct the joint probability distribution (JPD) of those variables by a composition of conditional probability distributions (CPDs). The real-time control of virtual vehicles is achieved by inferring the appropriate actions under the evidence of sensory percepts with the help of the reconstructed JPD.