Abstract

Civil aviation air traffic undergoes many random disturbances. Air traffic controllers watch the real traffic and ensure that it is free from collision hazards in the whole airspace — the ATC sector — under their control. The ATFM service takes care that the flow capacity in each sector, called capacity of the air traffic sector, is not exceeded. In the paper an original method of model estimation of the capacity of the designed ATC sectors is described. In this method an experimentally established statistical relationship between the random variables of control load and the air traffic density is used. The capacity of a sector was found from the joint probability distribution of such a two-dimensional random variable model determined as a quanti le of adequate order. This method was verified in practice by determining the capacity of the designed new ATC sectors in FIR Warsaw. Document type: Book

Abstract

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. Document type: Part of book or chapter of book