Prototyping Smart Assistance with Bayesian Autonomous Driver Models

Abstract

The Human or Cognitive Centered Design (HCD) of intelligent transport systems requires digital Models of Human Behavior and Cognition (MHBC) enabling Ambient Intelligence e.g. in a smart car. Currently MBHC are developed and used as driver models in traffic scenario simulations, in proving safety assertions and in supporting risk-based design. Furthermore, it is tempting to prototype assistance systems (AS) on the basis of a human driver model cloning an expert driver. To that end we propose the Bayesian estimation of MHBCs from human behavior traces generated in new kind of learning experiments: Bayesian model learning under driver control. The models learnt are called Bayesian Autonomous Driver (BAD) models. For the purpose of smart assistance in simulated or real world scenarios the obtained BAD models can be used as Bayesian Assistance Systems (BAS). The critical question is, whether the driving competence of the BAD model is the same as the driving competence of the human driver when generating the training data for the BAD model. We believe that our approach is superior to the proposal to model the strategic and tactical skills of an AS with a Markov Decision Process (MDP). The usage of the BAD model or BAS as a prototype for a smart Partial Autonomous Driving Assistant System (PADAS) is demonstrated within a racing game simulation.