Qwen-Drive-1.0-4B

모델 소개

> [!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. 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.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。