Adver-City Dataset

Adverse weather conditions pose a significant challenge to the widespread adoption of Autonomous Vehicles (AVs) by impacting sensors like LiDARs and cameras. Even though Collaborative Perception (CP) improves AV perception in difficult conditions, existing CP datasets lack adverse weather conditions. To address this, we introduce Adver-City, the first open-source synthetic CP dataset focused on adverse weather conditions. Simulated in CARLA with OpenCDA, it contains over 24 thousand frames, over 890 thousand annotations, and 110 unique scenarios across six different weather conditions: clear weather, soft rain, heavy rain, fog, foggy heavy rain and, for the first time in a synthetic CP dataset, glare. It has six object categories including pedestrians and cyclists, and uses data from vehicles and roadside units featuring LiDARs, RGB and semantic segmentation cameras, GNSS, and IMUs. Its scenarios, based on real crash reports, depict the most relevant road configurations for adverse weather and poor visibility conditions, varying in object density, with both dense and sparse scenes, allowing for novel testing conditions of CP models. Benchmarks run on the dataset show that weather conditions created challenging conditions for perception models, reducing multi-modal object detection performance by up to 19%, while object density affected LiDAR-based detection by up to 29%. The code and documentation are available at https://labs.cs.queensu.ca/quarrg/datasets/adver-city/.恶劣天气会对激光雷达（LiDAR）、摄像头等传感器造成干扰，进而对自动驾驶汽车（Autonomous Vehicles, AVs）的大规模应用构成严峻挑战。尽管协同感知（Collaborative Perception, CP）可在复杂工况下提升自动驾驶汽车的环境感知能力，但现有协同感知数据集均未覆盖恶劣天气场景。为此，我们推出了Adver-City——首个聚焦恶劣天气场景的开源合成式协同感知数据集。该数据集基于CARLA与OpenCDA仿真平台构建，包含超2.4万帧数据、近89万条标注信息，以及覆盖6种不同天气类型的110个独特场景：晴好天气、小雨、大雨、雾天、雾中大雨，以及合成式协同感知数据集首次引入的眩光（glare）场景。数据集涵盖行人、骑行者等6类目标类别，数据采集自搭载激光雷达（LiDAR）、RGB相机、语义分割相机、全球导航卫星系统（GNSS）以及惯性测量单元（IMU）的车载与路侧单元设备。其场景基于真实交通事故报告构建，还原了恶劣天气与低能见度条件下最具代表性的道路构型，目标对象密度覆盖稠密与稀疏两种场景，可为协同感知模型提供全新的测试工况。基于该数据集开展的基准测试结果表明，天气条件会给感知模型带来显著挑战：多模态目标检测性能最高可下降19%，而目标密度对基于激光雷达的检测性能影响最高可达29%。相关代码与文档可通过以下链接获取：https://labs.cs.queensu.ca/quarrg/datasets/adver-city/

搜集汇总

背景与挑战

背景概述

Adver-City Dataset是首个专注于恶劣天气条件的开源合成协同感知数据集，旨在解决自动驾驶车辆在雨、雾、眩光等复杂环境下的感知挑战。它包含超过24,000帧数据和890,000个注释，覆盖六种天气条件和六种对象类别，数据来自多传感器融合，并基于真实事故报告设计场景，为测试协同感知模型提供了新颖且具有挑战性的环境。