CARRADA

High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness that are required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used for a long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions). To a large extent, this situation is due to the relative lack of automotive datasets with real radar signals that are both raw and annotated. In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with rangeangle-Doppler annotations. We also present a semi-automatic annotation approach, which was used to annotate the dataset, and a radar semantic segmentation baseline, which we evaluate on several metrics. Both our code and dataset are available online.高质量的环境感知对于自动驾驶（Autonomous Driving，AD）系统至关重要。为满足此类系统所需的精度与鲁棒性要求，需融合多种类型的传感器。当前主流方案多采用摄像头与激光雷达（LiDAR）来构建车辆周边的环境表征。尽管雷达（radar）传感器在汽车工业中已应用多年，但在自动驾驶领域仍未得到充分利用，尽管其具备诸多极具吸引力的特性——尤其能够测量障碍物的相对速度，且可在恶劣天气条件下正常工作。造成这一现状的核心原因之一，便是当前汽车领域中兼具原始雷达信号与标注信息的真实数据集相对匮乏。本研究提出了CARRADA数据集：该数据集包含同步采集的摄像头与雷达数据，并附带距离-角度-多普勒（Range-Angle-Doppler）标注信息。此外，我们还提出了用于该数据集标注的半自动标注方案，以及一款雷达语义分割基线模型，并通过多项性能指标对其进行了评估。本研究的代码与数据集均可在线获取。

搜集汇总

背景与挑战

背景概述

CARRADA是一个面向自动驾驶感知的同步相机和雷达记录数据集，包含距离-角度-多普勒标注，填补了该领域缺乏原始雷达信号和标注数据的空白。该数据集还提供了半自动标注方法和雷达语义分割基线，可用于提升自动驾驶系统在复杂环境下的感知能力。