Diverse and Admissible Trajectory Forecasting Through Multimodal Context Understanding-Reference-Cited by-同舟云学术

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Diverse and Admissible Trajectory Forecasting Through Multimodal Context Understanding

Published:2020 Issue: Volume: Page:282-298
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2020
language:
Short-container-title:

Author:

Park Seong Hyeon,Lee Gyubok,Seo Jimin,Bhat Manoj,Kang Minseok,Francis Jonathan,Jadhav Ashwin,Liang Paul Pu,Morency Louis-Philippe

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58621-8_17

Reference33 articles.

1. Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social lSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961–971 (2016)

2. Lecture Notes in Computer Science;L Ballan,2016

3. Bansal, M., Krizhevsky, A., Ogale, A.: Chauffeurnet: learning to drive by imitating the best and synthesizing the worst. arXiv preprint arXiv:1812.03079 (2018)

4. Bernstein, D.S., So, W.: Some explicit formulas for the matrix exponential. IEEE Trans. Autom. Control 38(8), 1228–1232 (1993)

5. Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621–11631 (2020)

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