论文/arXiv 2607.17767·3 个月前

VLN-AVP：面向自主代客泊车的混合长短期记忆零样本视觉语言导航

提出VLN-AVP，结合BEV与VLM及混合记忆，在仿真和真实车辆上验证零样本自主代客泊车导航。

AI 整理

• 感知与导航
• 仿真与训练
• 数据与采集

原文链接：VLN-AVP: Zero-Shot Vision-Language Navigation with Hybrid Long-Short-Term Memory for Autonomous Valet Parking（arXiv 2607.17767 · 发布日期：2026-07-20）

VLN-AVP: Zero-Shot Vision-Language Navigation with Hybrid Long-Short-Term Memory for Autonomous Valet Parking

提出VLN-AVP，结合BEV与VLM及混合记忆，在仿真和真实车辆上验证零样本自主代客泊车导航。

核心要点

• 现有AVP方法通常依赖预建地图，限制其扩展到未知环境和开放词汇目标。
• 框架结合BEV空间感知与VLM通用智能，按自然语言指令理解停车场语义并导航。
• 短期感知记忆跟踪语义视觉线索，长期拓扑记忆利用过往经验稳定策略学习。
• 提出VLN-AVP数据集与基准，含10个高保真停车场景和1000多个导航片段；实验显示成功率领先。

验证范围与局限：验证范围包括仿真中的停车场导航实验和真实车辆实验，摘要未说明真实车辆数量。

英文摘要 (Abstract)

Existing methods in Autonomous Valet Parking (AVP) typically rely on pre-built maps, which severely restricts their scalability to unseen environments and open-vocabulary targets. Inspired by the application of Vision-Language Models (VLMs) in Vision-Language Navigation (VLN) tasks, we propose VLN-AVP, a zero-shot navigation framework for AVP tasks. By combining the precise spatial perception of a Bird's-Eye-View (BEV) model with the general intelligence of VLMs, our framework 1) eliminates the dependency on pre-built maps, 2) interprets semantic environmental contexts in parking scenarios, and 3) enables intuitive navigation following natural language instructions. Specifically, we introduce a hybrid memory system: a short-term perception memory tracks semantic visual cues to address the limitations of VLM's single-frame reasoning in existing methods, while a long-term topological memory facilitates stable policy learning from past experiences. To bridge the gap in existing benchmarks, we also present the VLN-AVP dataset and benchmark. Featuring 10 high-fidelity parking scenes and over 1,000 navigation episodes, it has the largest number of garage scenes to date and is the first VLN benchmark for underground parking. Extensive experiments demonstrate that in simulation, our method achieves an over 25% improvement in success rate compared to VLN methods and an over 15% improvement compared to other autonomous driving methods. Furthermore, it attains a leading success rate in real-world vehicle experiments, proving its practical feasibility.

作者：Yijian Li, Xiangru Mu, Changze Li, Hantian Shi, Jiyuan Cai, Jia Cai, Xiaoxue Liu, Yajing Sun, Ming Yang, Tong Qin

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2607.17767：VLN-AVP: Zero-Shot Vision-Language Navigation with Hybrid Long-Short-Term Memory for Autonomous Valet Parking访问 2026-10-21