BEV-VLM：通过统一的鸟瞰抽象进行轨迹规划
BEV-VLM: Trajectory Planning via Unified BEV Abstraction
AI总结：
BEV-VLM通过统一的BEV抽象与VLMs实现高精度轨迹规划，实验显示其在准确性上比现有方法提升53.1%并实现无碰撞。
摘要：
本文介绍了BEV-VLM，一种用于自动驾驶轨迹规划的新方法，该方法利用视觉-语言模型（VLMs）结合鸟瞰图（BEV）特征图作为视觉输入。与传统的仅依赖原始视觉数据（如摄像头图像）的轨迹规划方法不同，我们的方法利用通过融合摄像头和激光雷达数据生成的高压缩且信息丰富的BEV表示，随后对齐到高分辨率（HD）地图。这种统一的BEV-HD地图格式提供了几何一致且语义丰富的场景描述，使VLMs能够进行准确且稳健的轨迹规划。在nuScenes数据集上的实验结果表明，与最先进的视觉-only方法相比，我们的方法在规划准确性上提高了53.1%，并在评估场景中实现了完全的碰撞避免。我们的工作表明，VLMs可以有效解释处理过的视觉表示，如BEV特征，从而扩展了其在轨迹规划任务中的应用范围。
英文摘要：
This paper introduces BEV-VLM, a novel approach for trajectory planning in autonomous driving that leverages Vision-Language Models (VLMs) with Bird's-Eye View (BEV) feature maps as visual input. Unlike conventional trajectory planning approaches that rely solely on raw visual data (e.g., camera images), our method utilizes a highly compressed and informative BEV representation generated by fusing camera and LiDAR data, with subsequent alignment to High-Definition (HD) maps. This unified BEV-HD map format provides a geometrically consistent and semantically rich scene description, which enables VLMs to perform accurate and robust trajectory planning. Experimental results on the nuScenes dataset demonstrate that, compared with state-of-the-art vision-only methods, our approach achieves a 53.1% improvement in planning accuracy and realizes complete collision avoidance in evaluation scenarios. Our work highlights that VLMs can effectively interpret processed visual representations such as BEV features, expanding their applicability beyond raw image inputs for the task of trajectory planning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
