The accurate simulation and prediction of human behavior is critical in many transportation applications, including safety and energy management systems. Construction of human driving models by hand is time-consuming and error-prone, and small modeling inaccuracies can have a significant impact on the estimated performance of a candidate system. This paper presents a comparative evaluation of several probabilistic microscopic human behavior models from the literature trained on naturalistic data for free-flow, car following, and lane change context-classes on highways. We propose several metrics to quantify model quality and use these metrics to demonstrate that a new class of Bayesian network models outperforms the state of the art.
Analysis of microscopic behavior models for probabilistic modeling of driver behavior
2016-11-01
293092 byte
Conference paper
Electronic Resource
English
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