Qwen/Qwen-Drive-1.0-4B

Qwen-Drive-1.0-4B

> [!Note] > This repository contains the weights and configurations of Qwen-Drive-1.0 in the Hugging > Face format. The accompanying code, demo data and documentation are released at > QwenLM/Qwen-Drive-1.0.

Qwen-Drive-1.0 retains the architecture of the pretrained Qwen3.5 vision-language model and integrates 3D perception, visual question answering, and motion planning within a unified framework. The natively multimodal Qwen3.5-4B serves as the shared VLM, with two external modules attached: a BEV perception head jointly performing 3D object detection, semantic occupancy prediction and BEV map segmentation, and a Planning Expert that conditions on the shared VLM representations to generate future ego trajectories through flow matching. The unchanged VLM answers free-form questions about driving scenes. A staged training recipe combines driving supervision with general-purpose vision-language data, so the model acquires driving-specific competence while preserving broad visual understanding and instruction-following capability.

For more details, please refer to our Technical Report: Qwen-Drive-1.0.

Highlights

• Qwen-Drive-1.0 is 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 untouched.

• An external BEV perception head serves as an explicit, inspectable 3D probe, jointly

learning 3D detection, semantic occupancy prediction, and BEV map segmentation, equipping the same pretrained VLM with clear perception outputs while preserving highly competitive vision-language performance.

• A staged training and data recipe unifies cross-dataset labels, rewrites responses, filters

samples for consistency, and combines driving data with general-purpose vision-language supervision, supporting domain adaptation while mitigating catastrophic forgetting.

• A Planning Expert tailored to pretrained VLM representations generates future ego

trajectories with flow matching. Unified trajectory annotations enable joint training across multiple public driving datasets and yield highly competitive results across open-loop, pseudo-closed-loop, and closed-loop evaluations.

• Two Planning Experts are released: planner-sft supports both direct and reasoning

planning; planner-rl is further reward-optimized on NAVSIM PDMS, WOD-E2E RFS and a displacement term, and runs best in the reasoning planning mode.

Motion planning

AutoVLASpanVLAMindVLA-U1Alpamayo-1.5SimWAMILQwen-Drive-1.0-SFTQwen-Drive-1.0-RL

Open-loop

WOD-E2E (RFS val/test ↑) --/7.56 -- 8.20/7.87 -- -- 7.95/7.78 8.45/7.91

WOD-E2E (ADE 5s val/test ↓) --/2.96 -- 2.28/2.66 -- -- 2.31/2.65 1.27/2.67

PAI-AV (Avg. ADE 3s ↓) -- -- -- 0.35 0.41 0.37 0.42

PAI-AV (Avg. ADE 5s ↓) -- -- -- 1.05 -- 1.07 1.11

Pseudo-closed-loop

NAVSIM (PDMS ↑) 89.6 90.3 -- -- 90.3 88.2 90.7

NAVSIM best-of-6 (PDMS ↑) -- -- -- -- -- 89.3 91.4

Closed-loop

AlpaSim (at-fault score ↑) -- -- -- 0.45 0.30 0.27 0.37

• AutoVLA and SimWAM train a separate model on each dataset.

• The SFT column reports Qwen-Drive-1.0-SFT conditioned on planning reasoning.

• IL denotes imitation learning.

• -- indicates that the method does not report a result on the corresponding benchmark.

With planning samples assembled purely from public sources, Qwen-Drive-1.0 unifies the trajectory format across datasets and evaluates from open-loop prediction to closed-loop driving. The SFT model is already competitive across all benchmarks. After reinforcement learning, the model trades only a marginal open-loop displacement for comprehensive gains in human-preference alignment and closed-loop safety.

Vision-language understanding

InternVL3.5-8B-InstructLLaVA-OV2-8BQwen3.5-4BCosmos-Reason1-7BCosmos-Reason2-8BCosmos3-nanoMiMo-Embodied-7BAlpamayo-1.5-10BQwen-Drive-1.0-SFT

Driving VQA

LingoQA 46.4 41.2 70.4 45.2 59.6 65.0 72.0 64.0 77.8

Ego3D RMSE ↓ 23.01 24.97 13.17 26.71 12.62 22.41 9.85 25.31 7.78

VLAD 54.5 58.7 65.4 33.6 56.4 57.7 50.3 9.1 66.5

SURDS 32.8 38.6 53.0 8.5 19.5 39.7 43.1 3.1 66.1

WaymoQA safety 54.5 49.7 62.5 39.5 57.7 56.9 66.5 42.6 70.7

WaymoQA all 58.1 55.2 67.1 43.9 <td style="padding:7px 7