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Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of tasks.
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Chen, D., Krähenbühl, P.: Learning from all vehicles. In: CVPR (2022)
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Chen, L., Sima, C., Li, Y., Zheng, Z., Xu, J., Geng, X., Li, H., He, C., Shi, J., Qiao, Y., Yan, J.: Persformer: 3d lane detection via perspective transformer and the openlane benchmark. In: ECCV (2022)
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commaai: Openpilot. https://github.com/commaai/openpilot (2022)
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Fadadu, S., Pandey, S., Hegde, D., Shi, Y., Chou, F.C., Djuric, N., Vallespi-Gonzalez, C.: Multi-view fusion of sensor data for improved perception and prediction in autonomous driving. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (2022)
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Jia, X., Sun, L., Zhao, H., Tomizuka, M., Zhan, W.: Multi-agent trajectory prediction by combining egocentric and allocentric views. In: CoRL (2022)
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Liao, W., Chen, X., Zhang, W., Liu, H., Yan, J., Lou, Y., Xue, T., Mei, S.: Trajectory prediction from ego view: a coordinate transform and tail-light event driven approach. In: ICME (2022)
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