Modelling driving behaviour using hybrid automata
| Autor: | Ursula Goltz, Matthias Buntins, Anke Schwarze, Frank Eggert, Jens Schicke-Uffmann |
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| Rok vydání: | 2020 |
| Předmět: |
Sequence
Engineering Mathematical optimization business.industry Process (engineering) Mechanical Engineering Contrast (statistics) Transportation Control engineering Automaton Variable (computer science) Automata theory business Reinforcement Construct (philosophy) Law General Environmental Science |
| DOI: | 10.20378/irb-47239 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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