Fetching the paper…
Reading the bibliography…
The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving.
Grubb, A., Bagnell, D.: Speedboost: Anytime prediction with uniform near-optimality. In: Artificial Intelligence and Statistics. pp. 458–466. PMLR (2012)
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: ECCV (2015)
2015
Earlier work this paper cites.
Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: Unified, real-time object detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. PAMI (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117–2125 (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. In: CVPR (2017)
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. NeurIPS (2017)
2017
Earlier work this paper cites.
Zhou, Y., Tuzel, O.: Voxelnet: End-to-end learning for point cloud based 3d object detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2017)
2017
Earlier work this paper cites.
Casas, S., Luo, W., Urtasun, R.: Intentnet: Learning to predict intention from raw sensor data. In: CoRL (2018)
2018
Earlier work this paper cites.
Cui, H., Radosavljevic, V., Chou, F.C., Lin, T.H., Nguyen, T., Huang, T.K., Schneider, J.G., Djuric, N.: Multimodal trajectory predictions for autonomous driving using deep convolutional networks. 2019 ICRA pp. 2090–2096 (2018), https://api.semanticscholar.org/CorpusID:52891221
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)
2018
Earlier work this paper cites.
Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: Fast encoders for object detection from point clouds. CVPR (2018)
2018
Earlier work this paper cites.
Luo, W., Yang, B., Urtasun, R.: Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In: CVPR (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Yang, B., Luo, W., Urtasun, R.: Pixor: Real-time 3d object detection from point clouds. In: CVPR (2018)
2018
Earlier work this paper cites.
Cai, Z., Vasconcelos, N.: Cascade r-cnn: High quality object detection and instance segmentation. PAMI (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Hu, H., Dey, D., Hebert, M., Bagnell, J.A.: Learning anytime predictions in neural networks via adaptive loss balancing. In: Proceedings of the AAAI Conference on Artificial Intelligence (2019)
2019
Earlier work this paper cites.
Phan-Minh, T., Grigore, E.C., Boulton, F.A., Beijbom, O., Wolff, E.M.: Covernet: Multimodal behavior prediction using trajectory sets. CVPR (2019)
2019
Cited alongside, same era.
Sadat, A., Ren, M., Pokrovsky, A., Lin, Y.C., Yumer, E., Urtasun, R.: Jointly learnable behavior and trajectory planning for self-driving vehicles. In: 2019 IROS. pp. 3949–3956. IEEE (2019)
2019
Cited alongside, same era.
Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., Urtasun, R.: End-to-end interpretable neural motion planner. In: CVPR (2019)
2019
Cited alongside, same era.
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points. ArXiv (2019)
2019
Cited alongside, same era.
Zhou, Y., Sun, P., Zhang, Y., Anguelov, D., Gao, J., Ouyang, T.Y., Guo, J., Ngiam, J., Vasudevan, V.: End-to-end multi-view fusion for 3d object detection in lidar point clouds. ArXiv (2019)
2019
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Meyer, G.P., Charland, J., Pandey, S., Laddha, A.G., Gautam, S., Vallespi-Gonzalez, C., Wellington, C.K.: Laserflow: Efficient and probabilistic object detection and motion forecasting. IEEE Robotics and Automation Letters 6
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: ECCV (2020)
2020
Cited alongside, same era.
Casas, S., Gulino, C., Liao, R., Urtasun, R.: Spagnn: Spatially-aware graph neural networks for relational behavior forecasting from sensor data. In: ICRA (2020)
2020
Cited alongside, same era.
Casas, S., Gulino, C., Suo, S., Luo, K., Liao, R., Urtasun, R.: Implicit latent variable model for scene-consistent motion forecasting. In: ECCV (2020)
2020
Cited alongside, same era.
Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: Attention over convolution kernels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 11030–11039 (2020)
2020
Cited alongside, same era.
Gao, J., Sun, C., Zhao, H., Shen, Y., Anguelov, D., Li, C., Schmid, C.: Vectornet: Encoding hd maps and agent dynamics from vectorized representation. CVPR (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Li, L.L., Yang, B., Liang, M., Zeng, W., Ren, M., Segal, S., Urtasun, R.: End-to-end contextual perception and prediction with interaction transformer. IROS (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
Qi, C.R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., Anguelov, D.: Offboard 3d object detection from point cloud sequences. In: CVPR (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Yuan, Y., Weng, X., Ou, Y., Kitani, K.M.: Agentformer: Agent-aware transformers for socio-temporal multi-agent forecasting. In: ICCV (2021)
2021
Later among the works it cites.
Chitta, K., Prakash, A., Jaeger, B., Yu, Z., Renz, K., Geiger, A.: Transfuser: Imitation with transformer-based sensor fusion for autonomous driving. PAMI (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Ivanovic, B., Lin, Y., Shrivastava, S., Chakravarty, P., Pavone, M.: Propagating state uncertainty through trajectory forecasting. In: ICRA (2022)
2022
Later among the works it cites.
Liu, S., Li, F., Zhang, H., Yang, X., Qi, X., Su, H., Zhu, J., Zhang, L.: DAB-DETR: Dynamic anchor boxes are better queries for DETR. In: ICLR (2022)
2022
Later among the works it cites.
Mahjourian, R., Kim, J., Chai, Y., Tan, M., Sapp, B., Anguelov, D.: Occupancy flow fields for motion forecasting in autonomous driving. IEEE Robotics and Automation Letters 7
2022
Later among the works it cites.
Mo, X., Huang, Z., Xing, Y., Lv, C.: Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network. IEEE Transactions on Intelligent Transportation Systems (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Varadarajan, B., Hefny, A., Srivastava, A., Refaat, K.S., Nayakanti, N., Cornman, A., Chen, K., Douillard, B., Lam, C.P., Anguelov, D., et al.: Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction. In: 2022 ICRA (2022)
2022
Later among the works it cites.
Weng, X., Ivanovic, B., Pavone, M.: Mtp: Multi-hypothesis tracking and prediction for reduced error propagation. In: IV (2022)
2022
Later among the works it cites.
Zhou, Z., Ye, L., Wang, J., Wu, K., Lu, K.: Hivt: Hierarchical vector transformer for multi-agent motion prediction. CVPR (2022)
2022
Later among the works it cites.
Agro, B., Sykora, Q., Casas, S., Urtasun, R.: Implicit occupancy flow fields for perception and prediction in self-driving. In: CVPR (2023)
2023
Later among the works it cites.
Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., Lu, L., Jia, X., Liu, Q., Dai, J., Qiao, Y., Li, H.: Planning-oriented autonomous driving. In: CVPR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Yang, A.J., Casas, S., Dvornik, N., Segal, S., Xiong, Y., Hu, J.S.K., Fang, C., Urtasun, R.: Labelformer: Object trajectory refinement for offboard perception from lidar point clouds. In: CoRL. PMLR (2023)
2023
Later among the works it cites.
Zhang, L., Yang, A.J., Xiong, Y., Casas, S., Yang, B., Ren, M., Urtasun, R.: Towards unsupervised object detection from lidar point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9317–9328 (2023)
2023
Later among the works it cites.
Zhou, Z., Wang, J., Li, Y.H., Huang, Y.K.: Query-centric trajectory prediction. In: CVPR (2023)
2023
Later among the works it cites.