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End-to-end driving has made significant progress in recent years, demonstrating benefits such as system simplicity and competitive driving performance under both open-loop and closed-loop settings.
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Chitta, K., Prakash, A., Geiger, A.: Neat: Neural attention fields for end-to-end autonomous driving. In: ICCV (2021)
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Zhang, Z., Liniger, A., Dai, D., Yu, F., Van Gool, L.: End-to-end urban driving by imitating a reinforcement learning coach. In: ICCV (2021)
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Shao, H., Wang, L., Chen, R., Li, H., Liu, Y.: Safety-enhanced autonomous driving using interpretable sensor fusion transformer. In: CoRL (2022)
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Wu, P., Jia, X., Chen, L., Yan, J., Li, H., Qiao, Y.: Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline. In: NeurIPS (2022)
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