The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms
Abstract
Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system applications ranging from traffic simulations to driver assistance. The need for this ability is pronounced with the emergence of autonomous vehicles as they require the prediction of nearby vehicles’ trajectories to navigate safely and efficiently. Recent studies based on deep learning have greatly improved prediction accuracy. However, one prominent issue of these models is the lack of model explainability. We alleviate this issue by proposing spatiotemporal attention long short-term memory (STA-LSTM), an LSTM model with spatial-temporal attention mechanisms for explainability in vehicle trajectory prediction. STA-LSTM not only achieves comparable prediction performance against other state-of-the-art models but, more importantly, explains the influence of historical trajectories and neighboring vehicles on the target vehicle. We provide in-depth analyses of the learned spatial–temporal attention weights in various highway scenarios based on different vehicle and environment factors, including target vehicle class, target vehicle location, and traffic density. A demonstration illustrating that STA-LSTM can capture and explain fine-grained lane-changing behaviors is also provided. The data and implementation of STA-LSTM can be found athttps://github.com/leilin-research/VTP.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · cited 1×
“Finally, it would be interesting to extend our approach to other trajectory data sets through transfer learning [35].”
BackgroundHierarchical Attention Networks for Document Classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1174 · Cited by 4,712
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
Zhou, P., Shi, W., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p16-2034 · Cited by 2,012
ALVINN, an autonomous land vehicle in a neural network
Pomerleau, D. (2018) · doi:10.1184/r1/6603146 · Cited by 1,338
A survey on motion prediction and risk assessment for intelligent vehicles
Lefèvre, S., Vasquez, D., & Laugier, C. (2014) · ROBOMECH Journal · doi:10.1186/s40648-014-0001-z · Cited by 1,112
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Lee, N., Choi, W., Vernaza, P., Choy, C. B., Torr, P. H. S., & Chandraker, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.233 · Cited by 1,067
Cited by
Wu, X., Dong, J., Bao, W., Zou, B., Wang, L., & Wang, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3417285 · Cited by 95
Metadata
- Authors
- Lin, L., Li, W., Bi, H., & Qin, L.
- Venue
- IEEE Intelligent Transportation Systems Magazine
- Published
- 2022-03
- Volume
- 14
- Issue
- 2
- Pages
- 197-208
- ISSN
- 1939-1390 (ISSN-L)
- 1941-1197
- OpenAlex
- W3128196514
- Cited by
- 285
- References
- 28
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- closed
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