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/.

恶劣的天气条件对自动驾驶汽车（AVs）的广泛应用构成了重大挑战，因为它影响了激光雷达和摄像头等传感器。尽管协作感知（CP）在恶劣条件下改善了自动驾驶汽车的认识能力，但现有的CP数据集却缺少恶劣天气条件。为了解决这一问题，我们推出了Adver-City，这是首个专注于恶劣天气条件的开源合成CP数据集。该数据集在CARLA平台上，利用OpenCDA进行模拟，包含超过24,000帧图像、超过890,000个标注以及110种独特的场景，涵盖了六种不同的天气状况：晴朗天气、细雨、大雨、雾、浓雾大雨，以及首次在合成CP数据集中出现的眩光。它包含六个对象类别，包括行人和骑行者，并使用来自车辆和路边单元的数据，这些数据包括激光雷达、RGB和语义分割摄像头、GNSS和IMU。其场景基于真实的交通事故报告，描绘了恶劣天气和能见度差条件下最相关的道路配置，对象密度各异，既有密集场景也有稀疏场景，为CP模型的创新测试条件提供了可能。在数据集上运行的基准测试表明，天气条件为感知模型创造了挑战性的环境，降低了多模态目标检测性能高达19%，而对象密度则影响了基于激光雷达的检测，使其性能下降高达29%。代码和文档可在https://labs.cs.queensu.ca/quarrg/datasets/adver-city/找到。

搜集汇总

背景与挑战

背景概述

Adver-City是首个专注于恶劣天气条件的开源协同感知合成数据集，包含24,000多帧图像和890,000多个标注，涵盖6种天气条件。该数据集基于真实事故报告模拟了恶劣天气下的道路场景，为自动驾驶车辆的感知模型测试提供了新颖的挑战性环境。