Goal-Oriented Transformer to Predict Context-Aware Trajectories in Urban Scenarios
Author:
Affiliation:
1. Computer Engineering Department, Universidad de Alcalá, 28801 Alcalá de Henares, Spain
2. Joint Research Centre, European Commission, 41092 Seville, Spain
Publisher
MDPI
Link
https://www.mdpi.com/2673-4591/39/1/57/pdf
Reference30 articles.
1. Quintanar, A., Fernández-Llorca, D., Parra, I., Izquierdo, R., and Sotelo, M.A. (2021, January 11–17). Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.
2. Giuliari, F., Hasan, I., Cristani, M., and Galasso, F. (2021, January 10–15). Transformer Networks for Trajectory Forecasting. Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.
3. Izquierdo, R., Quintanar, A., Parra, I., Fernández-Llorca, D., and Sotelo, M.A. (2019, January 27–30). The PREVENTION dataset: A novel benchmark for PREdiction of VEhicles iNTentIONs. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.
4. Video Action Recognition for Lane-Change Classification and Prediction of Surrounding Vehicles;Biparva;IEEE Trans. Intell. Veh.,2022
5. Izquierdo, R., Quintanar, A., Parra, I., Fernández-Llorca, D., and Sotelo, M.A. (2020, January 20–23). Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs. Proceedings of the IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece.
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