Spatial-temporal ConvLSTM for vehicle driving intention prediction Article Swipe
He Huang
,
Zheni Zeng
,
Danya Yao
,
Xin Pei
,
Yi Zhang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.26599/tst.2020.9010061
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.26599/tst.2020.9010061
Driving intention prediction from a bird's-eye view has always been an active research area. However, existing research, on one hand, has only focused on predicting lane change intention in highway scenarios and, on the other hand, has not modeled the influence and spatiotemporal relationship of surrounding vehicles. This study extends the application scenarios to urban road scenarios. A spatial-temporal convolutional long short-term memory (ConvLSTM) model is proposed to predict the vehicle's lateral and longitudinal driving intentions simultaneously. This network includes two modules: the first module mines the information of the target vehicle using the long short-term memory (LSTM) network and the second module uses ConvLSTM to capture the spatial interactions and temporal evolution of surrounding vehicles simultaneously when modeling the influence of surrounding vehicles. The model is trained and verified on a real road dataset, and the results show that the spatial-temporal ConvLSTM model is superior to the traditional LSTM in terms of accuracy, precision, and recall, which helps improve the prediction accuracy at different time horizons.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.26599/tst.2020.9010061
- https://ieeexplore.ieee.org/ielx7/5971803/9614063/09614077.pdf
- OA Status
- diamond
- Cited By
- 46
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3212495134
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Raw OpenAlex JSON
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https://openalex.org/W3212495134Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26599/tst.2020.9010061Digital Object Identifier
- Title
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Spatial-temporal ConvLSTM for vehicle driving intention predictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-13Full publication date if available
- Authors
-
He Huang, Zheni Zeng, Danya Yao, Xin Pei, Yi ZhangList of authors in order
- Landing page
-
https://doi.org/10.26599/tst.2020.9010061Publisher landing page
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https://ieeexplore.ieee.org/ielx7/5971803/9614063/09614077.pdfDirect link to full text PDF
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diamondOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/5971803/9614063/09614077.pdfDirect OA link when available
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Computer science, Artificial intelligence, Recall, Machine learning, Simulation, Cognitive psychology, PsychologyTop concepts (fields/topics) attached by OpenAlex
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46Total citation count in OpenAlex
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2025: 7, 2024: 13, 2023: 10, 2022: 16Per-year citation counts (last 5 years)
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.trained | 127 |
| abstract_inverted_index.vehicle | 91 |
| abstract_inverted_index.ConvLSTM | 104, 142 |
| abstract_inverted_index.However, | 14 |
| abstract_inverted_index.accuracy | 162 |
| abstract_inverted_index.dataset, | 134 |
| abstract_inverted_index.existing | 15 |
| abstract_inverted_index.includes | 79 |
| abstract_inverted_index.modeling | 118 |
| abstract_inverted_index.modules: | 81 |
| abstract_inverted_index.proposed | 66 |
| abstract_inverted_index.research | 12 |
| abstract_inverted_index.superior | 145 |
| abstract_inverted_index.temporal | 111 |
| abstract_inverted_index.vehicles | 115 |
| abstract_inverted_index.verified | 129 |
| abstract_inverted_index.accuracy, | 153 |
| abstract_inverted_index.different | 164 |
| abstract_inverted_index.evolution | 112 |
| abstract_inverted_index.horizons. | 166 |
| abstract_inverted_index.influence | 40, 120 |
| abstract_inverted_index.intention | 1, 27 |
| abstract_inverted_index.research, | 16 |
| abstract_inverted_index.scenarios | 30, 52 |
| abstract_inverted_index.vehicle's | 70 |
| abstract_inverted_index.vehicles. | 46, 123 |
| abstract_inverted_index.(ConvLSTM) | 63 |
| abstract_inverted_index.bird's-eye | 5 |
| abstract_inverted_index.intentions | 75 |
| abstract_inverted_index.precision, | 154 |
| abstract_inverted_index.predicting | 24 |
| abstract_inverted_index.prediction | 2, 161 |
| abstract_inverted_index.scenarios. | 56 |
| abstract_inverted_index.short-term | 61, 95 |
| abstract_inverted_index.application | 51 |
| abstract_inverted_index.information | 87 |
| abstract_inverted_index.surrounding | 45, 114, 122 |
| abstract_inverted_index.traditional | 148 |
| abstract_inverted_index.interactions | 109 |
| abstract_inverted_index.longitudinal | 73 |
| abstract_inverted_index.relationship | 43 |
| abstract_inverted_index.convolutional | 59 |
| abstract_inverted_index.simultaneously | 116 |
| abstract_inverted_index.spatiotemporal | 42 |
| abstract_inverted_index.simultaneously. | 76 |
| abstract_inverted_index.spatial-temporal | 58, 141 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.9310098 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |