@article{ref1,
title="A Bayesian driver agent model for autonomous vehicles system based on knowledge-aware and real-time data",
journal="Sensors (Basel)",
year="2021",
author="Xie, Hui and Ma, Jichang and Liu, Hao and Song, Kang",
volume="21",
number="2",
pages="e331-e331",
abstract="A key research area in autonomous driving is how to model the driver's decision-making behavior, due to the fact it is significant for a self-driving  vehicles considering their traffic safety and efficiency. However, the uncertain  characteristics of vehicle and pedestrian trajectories affect urban roads, which  poses severe challenges to the cognitive understanding and decision-making of  autonomous vehicle systems in terms of accuracy and robustness. To overcome the  abovementioned problems, this paper proposes a Bayesian driver agent (BDA) model  which is a vision-based autonomous vehicle system with learning and inference  methods inspired by human driver's cognitive psychology. Different from the  end-to-end learning method and traditional rule-based methods, our approach breaks  the driving system up into a scene recognition module and a decision inference  module. The perception module, which is based on a multi-task learning neural  network (CNN), takes a driver's-view image as its input and predicts the traffic  scene's feature values. The decision module based on dynamic Bayesian network (DBN)  then makes an inferred decision using the traffic scene's feature values. To explore  the validity of the Bayesian driver agent model, we performed experiments on a  driving simulation platform. The BDA model can extract the scene feature values  effectively and predict the probability distribution of the human driver's  decision-making process accurately based on inference. We take the lane changing  scenario as an example to verify the model, the intraclass correlation coefficient  (ICC) correlation between the BDA model and human driver's decision process reached  0.984. This work suggests a research in scene perception and autonomous  decision-making that may apply to autonomous vehicle system.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21020331",
url="http://dx.doi.org/10.3390/s21020331"
}