Human drivers are endowed with an inborn ability to put themselves in the position of other drivers and reason about their behaviors and intended actions. State-of-the-art driving assistance systems, on the other hand, are generally limited to physical models and ad-hoc safety rules. In order to drive safely amongst humans, autonomous vehicles require a high-level description of the state of traffic participants. This paper presents a probabilistic model for estimating and predicting the behavior of drivers immersed in traffic. The model is defined within a stochastic filtering framework and estimation and prediction are carried out with statistical inference techniques. The approach is validated with real data from a fleet of mining vehicles.
A bayesian approach for driving behavior inference
2011-06-01
476829 byte
Conference paper
Electronic Resource
English
A Bayesian Approach for Driving Behavior Inference
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