基于2D物体边界框的路径规划
Path Planning based on 2D Object Bounding-box
AI总结：
本文提出一种以视觉为中心的自动驾驶路径规划方法，利用2D物体边界框，结合高精地图与摄像头图像，通过GNN和Transformer进行时空特征聚合，在nuPlan任务上取得有竞争力的表现。
摘要：
在城市环境中实施自动驾驶（AD）技术面临重大挑战。这些挑战要求开发先进的感知系统和运动规划算法，以应对高度复杂的情况。尽管利用LiDAR传感器的端到端自动驾驶方法在该场景中取得了显著成功，但我们认为其缺点可能阻碍其实际应用。相反，我们提出以视觉为中心的自动驾驶作为一种有前景的替代方案，在简化模型的同时不牺牲性能。在本研究中，我们提出了一种利用物体2D边界框的路径规划方法，该方法通过在城市驾驶场景中进行模仿学习开发而成。具体实现方式是将高精（HD）地图数据与周围摄像头采集的图像进行融合。后续感知任务包括边界框检测与跟踪，而规划阶段则同时采用图神经网络（GNN）提取局部嵌入和Transformer提取全局嵌入，以进行时空特征聚合，最终生成最优路径规划信息。我们在nuPlan规划任务上评估了所提出的模型，并观察到其与现有以视觉为中心的方法相比具有竞争力。
英文摘要：
The implementation of Autonomous Driving (AD) technologies within urban environments presents significant challenges. These challenges necessitate the development of advanced perception systems and motion planning algorithms capable of managing situations of considerable complexity. Although the end-to-end AD method utilizing LiDAR sensors has achieved significant success in this scenario, we argue that its drawbacks may hinder its practical application. Instead, we propose the vision-centric AD as a promising alternative offering a streamlined model without compromising performance. In this study, we present a path planning method that utilizes 2D bounding boxes of objects, developed through imitation learning in urban driving scenarios. This is achieved by integrating high-definition (HD) map data with images captured by surrounding cameras. Subsequent perception tasks involve bounding-box detection and tracking, while the planning phase employs both local embeddings via Graph Neural Network (GNN) and global embeddings via Transformer for temporal-spatial feature aggregation, ultimately producing optimal path planning information. We evaluated our model on the nuPlan planning task and observed that it performs competitively in comparison to existing vision-centric methods.
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。
