Abstract
Public policy makers have high expectations from automation in driving. The autonomous vehicle is expected to perform as a minimum the driving tasks of an alert non-aggressive driver. To go beyond these requirements, automation functions are to be designed to extend human driver capability in the form of processing large volume of data on driving environment in support of decision-making. This paper defines the role of Bayesian artificial intelligence (AI) in the development of car-following and lane change models for assisted and autonomous driving. Variables and model forms are covered. Driving simulator data in association with models enable illustrations on how new Bayesian AI-based models in driving assistance and automation system design can overcome safety problems caused by deficient perception-reaction performance. Finally, conclusions are presented on the roles of the Bayesian AI in adding cognitive capability to car-following and lane change models to be used for automation in driving.
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