V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models

Distilling the knowledge in a neural network

Original

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Occupancy flow fields for motion forecasting in autonomous driving

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Efficientvlm: Fast and accurate vision-language models via knowledge distillation and modal-adaptive pruning

Original

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Dair-v2x: A large-scale dataset for vehicle-infrastructure cooperative 3d object detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 21361–21370

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Chen, Z., Shi, Y., Jia, J., 2023 · 2023

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Vad: Vectorized scene representation for efficient autonomous driving, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8340–8350

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Contrastive vision-language alignment makes efficient instruction learner

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Robust collaborative 3d object detection in presence of pose errors, in: 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE. pp. 4812–4818

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Reasonnet: End-to-end driving with temporal and global reasoning, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 13723–13733

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Fusionad: Multi-modality fusion for prediction and planning tasks of autonomous driving

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V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5486–5495

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Vadv2: End-to-end vectorized autonomous driving via probabilistic planning

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Drive like a human: Rethinking autonomous driving with large language models, in: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), IEEE. pp. 910–919

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Enhance sample efficiency and robustness of end-to-end urban autonomous driving via semantic masked world model

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Drivelm: Driving with graph visual question answering, in: European Conference on Computer Vision, Springer. pp. 256–274

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Sparsedrive: End-to-end autonomous driving via sparse scene representation

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Drivevlm: The convergence of autonomous driving and large vision-language models

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Florence-2: Advancing a unified representation for a variety of vision tasks, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4818–4829

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