A Memory-Attention Hierarchical Model for Driving-Behavior Recognition and Motion Prediction.
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| Title: | A Memory-Attention Hierarchical Model for Driving-Behavior Recognition and Motion Prediction. |
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| Authors: | Yin, Huilin1 (AUTHOR), Wang, Jie1 (AUTHOR), Lin, Jia1 (AUTHOR) lj0408@tongji.edu.cn, Han, Daguang2 (AUTHOR), Ying, Chunli3 (AUTHOR), Meng, Qian4 (AUTHOR) |
| Source: | International Journal of Automotive Technology. Aug2021, Vol. 22 Issue 4, p895-908. 14p. |
| Subjects: | Recurrent neural networks, Hidden Markov models, Motor vehicle driving, Brain physiology, Traffic safety, Autonomous vehicles, Short-term memory |
| Abstract: | Proper understanding and prediction of driving behavior of surrounding vehicles are one of the most significant requirements for automated driving especially when it comes to safety on a highway. In this paper, we propose a two-layer memory-attention hierarchical model (MAHM) for driving-behavior recognition and motion prediction. This model is based on the human driver's thinking as well as on brain physiology, i.e., working memory and the selective-attention mechanism. The first layer is a hidden Markov model (HMM), which is used to achieve efficient recognition of driving behavior. The second layer is a memory-attention recurrent neural network (MARNN) for motion prediction, which derives the data from vehicles of interest as input according to driving behavior. Finally, the experimental analysis is performed on the real-data NGSIM US-101 and HighD datasets for highway-driving scenes. We report our results from three perspectives: accuracy of driving-behavior classification, error of predicted trajectories, and execution time. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 151566768 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Memory-Attention Hierarchical Model for Driving-Behavior Recognition and Motion Prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yin%2C+Huilin%22">Yin, Huilin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jie%22">Wang, Jie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Jia%22">Lin, Jia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lj0408@tongji.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Han%2C+Daguang%22">Han, Daguang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ying%2C+Chunli%22">Ying, Chunli</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Meng%2C+Qian%22">Meng, Qian</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Automotive+Technology%22">International Journal of Automotive Technology</searchLink>. Aug2021, Vol. 22 Issue 4, p895-908. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+vehicle+driving%22">Motor vehicle driving</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+physiology%22">Brain physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+vehicles%22">Autonomous vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Short-term+memory%22">Short-term memory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Proper understanding and prediction of driving behavior of surrounding vehicles are one of the most significant requirements for automated driving especially when it comes to safety on a highway. In this paper, we propose a two-layer memory-attention hierarchical model (MAHM) for driving-behavior recognition and motion prediction. This model is based on the human driver's thinking as well as on brain physiology, i.e., working memory and the selective-attention mechanism. The first layer is a hidden Markov model (HMM), which is used to achieve efficient recognition of driving behavior. The second layer is a memory-attention recurrent neural network (MARNN) for motion prediction, which derives the data from vehicles of interest as input according to driving behavior. Finally, the experimental analysis is performed on the real-data NGSIM US-101 and HighD datasets for highway-driving scenes. We report our results from three perspectives: accuracy of driving-behavior classification, error of predicted trajectories, and execution time. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s12239-021-0081-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 895 Subjects: – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Hidden Markov models Type: general – SubjectFull: Motor vehicle driving Type: general – SubjectFull: Brain physiology Type: general – SubjectFull: Traffic safety Type: general – SubjectFull: Autonomous vehicles Type: general – SubjectFull: Short-term memory Type: general Titles: – TitleFull: A Memory-Attention Hierarchical Model for Driving-Behavior Recognition and Motion Prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yin, Huilin – PersonEntity: Name: NameFull: Wang, Jie – PersonEntity: Name: NameFull: Lin, Jia – PersonEntity: Name: NameFull: Han, Daguang – PersonEntity: Name: NameFull: Ying, Chunli – PersonEntity: Name: NameFull: Meng, Qian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 12299138 Numbering: – Type: volume Value: 22 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Automotive Technology Type: main |
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