The authors present a new approach to the modelling of human driving behaviour, which describes driving behaviour as the result of an optimisation process within the formal framework of hybrid automata. In contrast to most approaches, the aim is not to construct a (cognitive) model of a human driver, but to directly model driving behaviour. The authors assume human driving to be controlled by the anticipated outcomes of possible behaviours. These positive and negative outcomes are mapped onto a single theoretical variable – the so called reinforcement value. Behaviour is assumed to be chosen in such a way that the reinforcement value is optimised in any given situation. To formalise the authors models they use hybrid automata, which allow for both continuous variables and discrete states. The models are evaluated using simulations of the optimised driving behaviours. A car entering a freeway served as the scenario to demonstrate our approach. First results yield plausible predictions for car trajectories and the chronological sequence of speed, depending on the surrounding traffic, indicating the feasibility of the approach.
Modelling driving behaviour using hybrid automata
IET Intelligent Transport Systems ; 7 , 2 ; 251-256
2013-06-01
6 pages
Article (Journal)
Electronic Resource
English
human factors , chronological speed sequence , continuous variables , optimisation process , optimised driving behaviours , formal hybrid automata framework , theoretical variable , human driver cognitive model , discrete states , behavioural sciences computing , automata theory , human driving behaviour modelling approach , reinforcement value , optimisation , car trajectory predictions , cognition , driver information systems
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