The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Attention Based Vehicle Trajectory Prediction
Open accessAbstract
Self-driving vehicles need to continuously analyse the driving scene, understand the behavior of other road users and predict their future trajectories in order to plan a safe motion and reduce their reaction time. Motivated by this idea, this paper addresses the problem of vehicle trajectory prediction over an extended horizon. On highways, human drivers continuously adapt their speed and paths according to the behavior of their neighboring vehicles. Therefore, vehicles' trajectories are very correlated and considering vehicle interactions makes motion prediction possible even before the start of a clear maneuver pattern. To this end, we introduce and analyze trajectory prediction methods based on how they model the vehicles interactions. Inspired by human reasoning, we use an attention mechanism that explicitly highlights the importance of neighboring vehicles with respect to their future states. We go beyond pairwise vehicle interactions and model higher order interactions. Moreover, the existence of different goals and driving behaviors induces multiple potential futures. We exploit a combination of global and partial attention paid to surrounding vehicles to generate different possible trajectory. Experiments on highway datasets show that the proposed model outperforms the state-of-the-art performances.
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Cites
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
(2017) · Cited by 11,625
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.110 · Cited by 3,483
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
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Generative adversarial imitation learning
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A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G. T., Malinowski, M., Pascanu, R., Battaglia, P., & Lillicrap, T. (2017) · arXiv · doi:10.48550/arxiv.1706.01427 · Cited by 1,281
Cited by
A Survey on Trajectory-Prediction Methods for Autonomous Driving
Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., & Chen, H. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3167103 · Cited by 677
Target-Driven Visual Navigation by Using Causal Intervention
Zhao, X., Wang, T., Li, Y., Zhang, B., Liu, K., Liu, D., Wang, C., & Snoussi, H. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3288810 · Cited by 86
Metadata
- Authors
- Messaoud, K., Yahiaoui, I., Verroust-Blondet, A., & Nashashibi, F.
- Venue
- IEEE Transactions on Intelligent Vehicles
- Published
- 2021-03
- Volume
- 6
- Issue
- 1
- Pages
- 175-185
- ISSN
- 2379-8858 (ISSN-L)
- 2379-8904
- OpenAlex
- W3016826426
- Cited by
- 425
- References
- 34
- FWCI
- 15.51
- Type
- journal article
- Language
- en
- Open access
- green
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- Messaoud, K., Yahiaoui, I., Verroust-Blondet, A., & Nashashibi, F. (2021). Attention Based Vehicle Trajectory Prediction. IEEE Transactions on Intelligent Vehicles. https://doi.org/10.1109/tiv.2020.2991952
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