Learning of a Bayesian autonomous driver mixture-of-behaviours (BAD-MoB) model

Abstract

The Human or Cognitive Centered Design (HCD) of intelligent transport systems requires computational Models of Human Behavior and Cognition (MHBC). They are developed and used as driver models in traffic scenario simulations and riskbased design. The conventional approach is first to develop handcrafted control-theoretic or artificial intelligence based prototypes and then to evaluate ex post their learnability, usability, and human likeness. We propose a machine-learning alternative: The Bayesian estimation of MHBCs from behavior traces. The learnt Bayesian Autonomous Driver (BAD) models are empirical valid by construction. An ex post evaluation of BAD models is not necessary. BAD models can be built so that they decompose or compose skills into or from project ISi-PADAS funded by the European Commission in the 7th Framework Program, Theme 7 Transport FP7-218552 project Integrated Modeling for Safe Transportation (IMOST) sponsored by the Government of Lower Saxony, Germany under contracts ZN2245, ZN2253, ZN2366 basic skills: BAD Mixture-of-Behaviors (BAD MoB) models. We present an efficient implementation which is able to control a simulated vehicle in real-time. It is able to generate complex behaviors of several layers of expertise by mixing and sequencing simpler behavior models.

6 Figures and Tables

Cite this paper

@inproceedings{Eilers2010LearningOA, title={Learning of a Bayesian autonomous driver mixture-of-behaviours (BAD-MoB) model}, author={Mark Eilers and Claus M{\"{o}bus}, year={2010} }