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Traffic accident prediction in driving videos aims to provide an early warning of the accident occurrence, and supports the decision making of safe driving systems.
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2016
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2016
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2017
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2017
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T. Suzuki, H. Kataoka, Y. Aoki, and Y. Satoh, “Anticipating traffic accidents with adaptive loss and large-scale incident DB,” in
2018
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2018
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2018
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Y. Yao, M. Xu, Y. Wang, D. J. Crandall, and E. M. Atkins, “Unsupervised traffic accident detection in first-person videos,” in
2019
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J. Fang, D. Yan, J. Qiao, J. Xue, H. Wang, and S. Li, “DADA-2000: can driving accident be predicted by driver attention? analyzed by A benchmark,” in
2019
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2019
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Z. Zheng, T. Ruan, Y. Wei, Y. Yang, and T. Mei, “Vehiclenet: Learning robust visual representation for vehicle re-identification,”
2020
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W. Bao, Q. Yu, and Y. Kong, “Uncertainty-based traffic accident anticipation with spatio-temporal relational learning,” in
2020
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T. You and B. Han, “Traffic accident benchmark for causality recognition,” in
2020
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Z. Zheng, Y. Wei, and Y. Yang, “University-1652: A multi-view multi-source benchmark for drone-based geo-localization,”
2020
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B. Mersch, T. Höllen, K. Zhao, C. Stachniss, and R. Roscher, “Maneuver-based trajectory prediction for self-driving cars using spatio-temporal convolutional networks,” in
2021
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X. Wu, R. Wang, J. Hou, H. Lin, and J. Luo, “Spatial-temporal relation reasoning for action prediction in videos,”
2021
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Y. Yao, E. M. Atkins, M. Johnson-Roberson, R. Vasudevan, and X. Du, “Coupling intent and action for pedestrian crossing behavior prediction,” in
2021
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L. Chen, J. Lu, Z. Song, and J. Zhou, “Recurrent semantic preserving generation for action prediction,”
2021
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W. Bao, Q. Yu, and Y. Kong, “DRIVE: deep reinforced accident anticipation with visual explanation,” in
2021
Cited alongside, same era.
2021
Cited alongside, same era.
L. Xu, H. Huang, and J. Liu, “Sutd-trafficqa: A question answering benchmark and an efficient network for video reasoning over traffic events,” in
2021
Cited alongside, same era.
M. M. Karim, Y. Li, R. Qin, and Z. Yin, “A dynamic spatial-temporal attention network for early anticipation of traffic accidents,”
2022
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2022
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M. Li, T. Wang, H. Zhang, S. Zhang, Z. Zhao, W. Zhang, J. Miao, S. Pu, and F. Wu, “HERO: hierarchical spatio-temporal reasoning with contrastive action correspondence for end-to-end video object grounding,” in
2022
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Y. Li, X. Wang, J. Xiao, W. Ji, and T. Chua, “Invariant grounding for video question answering,” in
2022
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Y. Li, X. Wang, J. Xiao, and T. Chua, “Equivariant and invariant grounding for video question answering,” in
2022
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S. Baee, E. Pakdamanian, I. Kim, L. Feng, V. Ordonez, and L. E. Barnes, “MEDIRL: predicting the visual attention of drivers via maximum entropy deep inverse reinforcement learning,” in
2021
Cited alongside, same era.
2021
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L. Zhuang, L. Wayne, S. Ya, and Z. Jun, “A robustly optimized bert pre-training approach with post-training,” in
2021
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W. Wang, J. Shen, J. Xie, M. Cheng, H. Ling, and A. Borji, “Revisiting video saliency prediction in the deep learning era,”
2021
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Z. Sheng, Y. Xu, S. Xue, and D. Li, “Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving,”
2022
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Y. Su, J. Du, Y. Li, X. Li, R. Liang, Z. Hua, and J. Zhou, “Trajectory forecasting based on prior-aware directed graph convolutional neural network,”
2022
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2022
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J. Xiao, A. Yao, Z. Liu, Y. Li, W. Ji, and T. Chua, “Video as conditional graph hierarchy for multi-granular question answering,” in
2022
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Y. Yao, X. Wang, M. Xu, Z. Pu, Y. Wang, E. Atkins, and D. Crandall, “DoTA: unsupervised detection of traffic anomaly in driving videos,”
2022
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Y. Kong and Y. Fu, “Human action recognition and prediction: A survey,”
2022
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G. Li, N. Li, F. Chang, and C. Liu, “Adaptive graph convolutional network with adversarial learning for skeleton-based action prediction,”
2022
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Z. Zheng, X. Wang, N. Zheng, and Y. Yang, “Parameter-efficient person re-identification in the 3d space,”
2022
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É. Zablocki, H. Ben-Younes, P. Pérez, and M. Cord, “Explainability of deep vision-based autonomous driving systems: Review and challenges,”
2022
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A. V. Malawade, S. Yu, B. Hsu, D. Muthirayan, P. P. Khargonekar, and M. A. A. Faruque, “Spatiotemporal scene-graph embedding for autonomous vehicle collision prediction,”
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T. J. Schoonbeek, F. J. Piva, H. R. Abdolhay, and G. Dubbelman, “Learning to predict collision risk from simulated video data,” in
2022
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J. Fang, J. Qiao, J. Bai, H. Yu, and J. Xue, “Traffic accident detection via self-supervised consistency learning in driving scenarios,”
2022
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P. Xu, X. Zhu, and D. A. Clifton, “Multimodal learning with transformers: A survey,”
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S. Yang, K. Wilson, T. Roady, J. Kuo, and M. G. Lenné, “Beyond gaze fixation: Modeling peripheral vision in relation to speed, tesla autopilot, cognitive load, and age in highway driving,”
2022
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