期刊
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
卷 69, 期 -, 页码 497-514出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trc.2015.11.007
关键词
Lane changing; Intention recognition; Hidden Markov models; Bayesian filtering; Speech recognition model; Driver assistance systems
资金
- National Natural Science Foundation of China [51175290, 51475254]
- Tsinghua University
- Daimler(R)
Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver's lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage. (C) 2015 Elsevier Ltd. All rights reserved.
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