用动态网格图提升自动驾驶中任意动态物体的检测精度
Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps
- 基于激光雷达构建在线动态网格，捕捉复杂场景中的移动物体
- 采用旋转等变检测器，显著降低误检率，性能优于传统聚类方法
- 适用于边缘场景下的通用动态物体检测，对自动驾驶安全至关重要
本文提出一种面向自动驾驶的通用动态物体检测方法。首先，基于激光雷达实时生成动态网格图；其次，利用深度学习检测器在该网格上识别任意类型动态物体，以满足复杂边缘场景下的安全需求。选用原本用于航拍图像定向检测的旋转等变检测器（ReDet），因其高检测性能而被采用。实验基于真实传感器数据展开，结果表明，相比经典动态单元聚类策略，该方法显著降低了误检率。
原文摘要 · Abstract (English)
This paper describes a method to detect generic dynamic objects for automated driving. First, a LiDAR-based dynamic grid is generated online. Second, a deep learning-based detector is trained on the dynamic grid to infer the presence of dynamic objects of any type, which is a prerequisite for safe automated vehicles in arbitrary, edge-case scenarios. The Rotation-equivariant Detector (ReDet) - originally designed for oriented object detection on aerial images - was chosen due to its high detection performance. Experiments are conducted based on real sensor data and the benefits in comparison to classic dynamic cell clustering strategies are highlighted. The false positive object detection rate is strongly reduced by the proposed approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。