@article{ref1,
title="An intention-aware and online driving style estimation based personalized autonomous driving strategy",
journal="International journal of automotive technology",
year="2020",
author="Sun, Bohua and Deng, Weiwen and Wu, Jian and Li, Yaxin and Wang, Jinsong",
volume="21",
number="6",
pages="1431-1446",
abstract="Autonomous vehicles are aiming at improving driving safety and comfort. They need to perform socially accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. What's more, understanding human drivers' driving styles that make the systems more human-like or personalized is the key to improve the system performance, in particular, the acceptance and adaption of autonomous vehicles to human passengers. In this study, a personalized intention-aware autonomous driving strategy is proposed. An online driving style identification is proposed based on double-level Multi-dimension Gaussian Hidden Markov Process (MGHMP) with arbitration mechanism and evaluated in field test. A Mixed Observable Markov Decision Process (MOMDP) is built to model the general personalized intention-aware framework. A human-like policy generation mechanism is used to generate the possible candidates to overcome the difficulty in solving MOMDP. The index of surrounding vehicles' intention of the upper-level MGHMP is updated during each prediction time step. The weighting factors of the reward function are configured with the identification result of lower-level MGHMP. The personalized intention-aware autonomous driving strategy is evaluated on a Real-Time Intelligent Simulation Platform. <br><br>RESULTS show that the proposed strategy can achieve the online identification accuracy above 95 % and for personalized autonomous driving in scenarios mixed with human-driven vehicles with uncertain intentions.<p /> <p>Language: en</p>",
language="en",
issn="1229-9138",
doi="10.1007/s12239-020-0135-3",
url="http://dx.doi.org/10.1007/s12239-020-0135-3"
}