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End-to-end (E2E) autonomous driving methods still struggle to make correct decisions in interactive closed-loop evaluation due to limited causal reasoning capability.
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Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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End-to-end urban driving by imitating a reinforcement learning coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, and Luc Van Gool · 2021
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Flamingo: a visual language model for few-shot learning
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Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline
Penghao Wu, Xiaosong Jia, Li Chen, Junchi Yan, Hongyang Li, and Yu Qiao · 2022
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Mathematical capabilities of chatgpt
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Emma: End-to-end multimodal model for autonomous driving
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Bench2drive: Towards multi-ability benchmarking of closed-loop end-to-end autonomous driving
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Continuously learning, adapting, and improving: A dual-process approach to autonomous driving
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Gemini: A family of highly capable multimodal models
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Vip3d: End-to-end visual trajectory prediction via 3d agent queries
Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, and Hang Zhao · 2023
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Planning-oriented autonomous driving
Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, et al · 2023
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Hidden biases of end-to-end driving models
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Vad: Vectorized scene representation for efficient autonomous driving
Bo Jiang, Shaoyu Chen, Qing Xu, Bencheng Liao, Jiajie Chen, Helong Zhou, Qian Zhang, Wenyu Liu, Chang Huang, and Xinggang Wang · 2023
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Carllava: Vision language models for camera-only closed-loop driving
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Lmdrive: Closed-loop end-to-end driving with large language models
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Drivevlm: The convergence of autonomous driving and large vision-language models
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Drivegpt4: Interpretable end-to-end autonomous driving via large language model
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Sparsead: Sparse query-centric paradigm for efficient end-to-end autonomous driving
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Genad: Generative end-to-end autonomous driving
Wenzhao Zheng, Ruiqi Song, Xianda Guo, Chenming Zhang, and Long Chen · 2024
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Hidden biases of end-to-end driving datasets
Julian Zimmerlin, Jens Beißwenger, Bernhard Jaeger, Andreas Geiger, and Kashyap Chitta · 2024
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Drivetransformer: Unified transformer for scalable end-to-end autonomous driving
Xiaosong Jia, Junqi You, Zhiyuan Zhang, and Junchi Yan · 2025
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Diffusiondrive: Truncated diffusion model for end-to-end autonomous driving
Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang, Cheng Wang, Sixu Yan, Xinbang Zhang, Xiangyu Li, Ying Zhang, Qian Zhang, et al · 2025
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Don’t shake the wheel: Momentum-aware planning in end-to-end autonomous driving
Ziying Song, Caiyan Jia, Lin Liu, Hongyu Pan, Yongchang Zhang, Junming Wang, Xingyu Zhang, Shaoqing Xu, Lei Yang, and Yadan Luo · 2025
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Openemma: Open-source multimodal model for end-to-end autonomous driving
Shuo Xing, Chengyuan Qian, Yuping Wang, Hongyuan Hua, Kexin Tian, Yang Zhou, and Zhengzhong Tu · 2025
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