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Qwen-Drive-1.0 integrates 3D perception, VQA, and motion planning for autonomous driving

By PulseAugur Editorial · [7 sources] ·

Qwen has introduced Qwen-Drive-1.0, a vision-language foundation model designed for autonomous driving. This model unifies 3D perception, visual question answering, and motion planning by leveraging a pretrained VLM architecture with external modules. It incorporates a bird's-eye-view perception head for tasks like 3D object detection and semantic occupancy prediction, and a planning expert for generating future trajectories. The model is trained using a staged recipe that combines driving supervision with general vision-language data to maintain broad capabilities. AI

IMPACT This model integrates multiple AI capabilities for autonomous driving, potentially advancing the field of self-driving AI systems.

RANK_REASON Frontier-lab model release with system card.

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 7 sources. How we write summaries →

Qwen-Drive-1.0 integrates 3D perception, VQA, and motion planning for autonomous driving

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Frontier Release
Frontier-lab model release with system card.
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COVERAGE [7]

  1. Qwen tech blog TIER_1 English(EN) · QwenTeam ·

    Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving

    We introduce Qwen-Drive-1.0, the first vision-language foundation model for autonomous driving that unifies 3D perception and visual question answering at the pretraining stage and further extends to motion planning, while keeping the pretrained VLM architecture entirely untouche…

  2. arXiv cs.AI TIER_1 English(EN) · Zhijie Qiao, Haowei Li, Zhong Cao, Henry X. Liu ·

    LightEMMA: A Longitudinal Evaluation of Vision-Language Models for Autonomous Driving

    arXiv:2505.00284v3 Announce Type: replace-cross Abstract: Rapid advances in vision-language models (VLMs) have generated growing interest in their application to autonomous driving. A prevailing assumption is that successive VLM generations will continually improve driving perfor…

  3. arXiv cs.CL TIER_1 English(EN) · Julian Truetsch, Felix Hauser, Christoph Stiller, Frank Bieder ·

    Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language

    arXiv:2609.03677v1 Announce Type: cross Abstract: Understanding the composition of large-scale autonomous driving datasets is essential for safety, robustness, and reliable operation across domains. For example, domain shift between locations could lead to the operating environme…

  4. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Frank Bieder ·

    Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language

    Understanding the composition of large-scale autonomous driving datasets is essential for safety, robustness, and reliable operation across domains. For example, domain shift between locations could lead to the operating environment being misaligned with the training data, result…

  5. Hugging Face Daily Papers TIER_1 English(EN) ·

    Understanding Autonomous Driving Datasets by Describing Differences between Image Subsets in Natural Language

    Understanding the composition of large-scale autonomous driving datasets is essential for safety, robustness, and reliable operation across domains. For example, domain shift between locations could lead to the operating environment being misaligned with the training data, result…

  6. Hugging Face Daily Papers TIER_1 English(EN) ·

    Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving

    Qwen-Drive-1.0 is a vision-language foundation model for autonomous driving that unifies 3D perception, visual question answering, and motion planning via shared representations and staged training.

  7. arXiv cs.CV TIER_1 English(EN) · Xin Zhou, Zongchuang Zhao, Zhibo Yang, Mingsheng Li, Humen Zhong, Shuai Bai, Du Chu, Ruizhe Chen, Zhaohai Li, Jun Tang, Qiuyue Wang, Mingkun Yang, Jiazhao Zhang, Dayiheng Liu, Dingkang Liang, Xiang Bai ·

    Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving

    arXiv:2609.00111v1 Announce Type: new Abstract: We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and integrates 3D perception, visual…

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