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Autonomous Driving (AD) systems have made notable progress, but their performance in long-tail, safety-critical scenarios remains limited.
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2023
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2023
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2023
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2023
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2024
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McKinsey, “Autonomous driving’s future: Convenient and connected,”
2025
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D. Hegde, R. Yasarla, H. Cai, S. Han, A. Bhattacharyya, S. Mahajan, L. Liu, R. Garrepalli, V. M. Patel, and F. Porikli, “Distilling multi-modal large language models for autonomous driving,” in
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X. Jia, Y. Gao, L. Chen, J. Yan, P. L. Liu, and H. Li, “Driveadapter: Breaking the coupling barrier of perception and planning in end-to-end autonomous driving,” in
2023
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Z. Lan, Y. Ren, H. Yu, L. Liu, Z. Li, Y. Wang, and Z. Cui, “Hi-scl: Fighting long-tailed challenges in trajectory prediction with hierarchical wave-semantic contrastive learning,”
2024
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C. Pan, B. Yaman, T. Nesti, A. Mallik, A. G. Allievi, S. Velipasalar, and L. Ren, “Vlp: Vision language planning for autonomous driving,” in
2024
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L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li, “End-to-end autonomous driving: Challenges and frontiers,”
2024
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Z. Guo, Z. Yagudin, A. Lykov, M. Konenkov, and D. Tsetserukou, “Vlm-auto: Vlm-based autonomous driving assistant with human-like behavior and understanding for complex road scenes,” in
2024
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Q. Zhang, M. Zhu, and H. F. Yang, “Think-driver: From driving-scene understanding to decision-making with vision language models,” in
2024
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T.-H. Wang, A. Maalouf, W. Xiao, Y. Ban, A. Amini, G. Rosman, S. Karaman, and D. Rus, “Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models,” in
2024
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2025
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2025
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2025
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2025
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H. Chi, H.-a. Gao, Z. Liu, J. Liu, C. Liu, J. Li, K. Yang, Y. Yu, Z. Wang, W. Li
2025
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2025
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