Abstract

Since the emergence of the Internet there has been continuous evolution in interface design, with designers trying to offer a good experience to users. Website developers regularly turn to heuristic indicators that have been collected throughout years of research and praxis. Nevertheless, these indicators are diverse and it is complicated locating which ones are applicable in every case. The objective of this work is to identify and analyze the most scientifically and geographically relevant heuristic indicators through a mapping review, in order to classify them according to formal and content components. The results are two taxonomies which make it easier to identify the location, and analyze and apply the guidelines.

Abstract

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. Document type: Part of book or chapter of 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