Learning the human longitudinal control behavior with a modular hierarchical Bayesian Mixture-of-Behaviors model

Modeling drivers' behavior is believed to be essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. These models are handcrafted in a top-down software engineering process. Here we propose a machine-...

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Bibliographic Details
Main Authors: Eilers, Mark, Mobus, Claus
Format: Conference Proceeding
Language:English
Subjects:
Acceleration
Bayesian methods
Computational modeling
Correlation
Driver circuits
Humans
Lead
Online Access:Request full text
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Summary:Modeling drivers' behavior is believed to be essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. These models are handcrafted in a top-down software engineering process. Here we propose a machine-learning alternative by estimating stochastic driver models from behavior traces. They are more robust than their non-stochastic predecessors. In this paper we present a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) model for the longitudinal control of human drivers in an inner-city traffic scenario. It is learnt on the basis of multivariate time-series obtained in simulator studies. Percepts relevant for longitudinal control were included in the model by a structure-learning method using Bayesian information criteria. Besides mimicking human driver behavior we suggest using the model for prototyping intelligent assistance systems with human-like behavior.
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2011.5940530