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Traditional autonomous driving systems often struggle to connect high-level reasoning with low-level control, leading to suboptimal and sometimes unsafe behaviors.
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Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Banerjee, S. and Lavie, A · 2005
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Markov logic networks
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Cider: Consensus-based image description evaluation
Vedantam, R., Lawrence Zitnick, C., and Parikh, D · 2015
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Vision for looking at traffic lights: Issues, survey, and perspectives
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Textual explanations for self-driving vehicles
Kim, J., Rohrbach, A., Darrell, T., Canny, J., and Akata, Z · 2018
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nuscenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
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Learning transferable visual models from natural language supervision
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Li, L., Zhang, J., Xie, T., and Li, B · 2022
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Video swin transformer
Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., and Hu, H · 2022
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
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Improving certified robustness via statistical learning with logical reasoning
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al · 2023
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Planning-oriented autonomous driving
Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., et al · 2023
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Vad: Vectorized scene representation for efficient autonomous driving
Jiang, B., Chen, S., Xu, Q., Liao, B., Chen, J., Zhou, H., Zhang, Q., Liu, W., Huang, C., and Wang, X · 2023
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Drivelm: Driving with graph visual question answering
Sima, C., Renz, K., Chitta, K., Chen, L., Zhang, H., Xie, C., Luo, P., Geiger, A., and Li, H · 2023
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Zhang, J., Li, L., Zhang, C., and Li, B · 2023
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Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
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Large language models are zero-shot time series forecasters
Gruver, N., Finzi, M., Qiu, S., and Wilson, A. G · 2024
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Adapt: Action-aware driving caption transformer
Jin, B., Liu, X., Zheng, Y., Li, P., Zhao, H., Zhang, T., Zheng, Y., Zhou, G., and Liu, J · 2023
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Ultralytics YOLO, January 2023
Jocher, G., Qiu, J., and Chaurasia, A · 2023
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Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Li, J., Li, D., Savarese, S., and Hoi, S · 2023
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Video-llava: Learning united visual representation by alignment before projection
Lin, B., Zhu, B., Ye, Y., Ning, M., Jin, P., and Yuan, L · 2023
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A language agent for autonomous driving
Mao, J., Ye, J., Qian, Y., Pavone, M., and Wang, Y · 2023
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Semnani, S., Yao, V., Zhang, H., and Lam, M · 2023
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Time-llm: Time series forecasting by reprogramming large language models
Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-f., Pan, S., et al
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Are language models actually useful for time series forecasting?
Tan, M., Merrill, M. A., Gupta, V., Althoff, T., and Hartvigsen, T · 2024
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Wang, S., Yu, Z., Jiang, X., Lan, S., Shi, M., Chang, N., Kautz, J., Li, Y., and Alvarez, J. M · 2024
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Drivegpt4: Interpretable end-to-end autonomous driving via large language model
Xu, Z., Zhang, Y., Xie, E., Zhao, Z., Guo, Y., Wong, K.-Y. K., Li, Z., and Zhao, H · 2024
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Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment
Zhu, B., Lin, B., Ning, M., Yan, Y., Cui, J., HongFa, W., Pang, Y., Jiang, W., Zhang, J., Li, Z., et al · 2024
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