发表机构
Ain Shams University; Royal Military College of Canada（艾因夏姆斯大学; 加拿大皇家军事学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文总结自动驾驶多传感器平台开发中的机械、校准、电源与同步挑战，提出GNSS/NTP同步等缓解策略，以提升测试可重复性与鲁棒性。
AI 中文摘要
本文报告了在开发和部署用于自动驾驶研究的灵活多传感器平台过程中遇到的挑战及吸取的经验教训，旨在为开发新型多传感器系统的研究人员提供参考。随着对可靠、多样化数据集需求的增加，新颖的环境和传感配置对于应对现实世界的运营挑战至关重要。因此，许多研究团队构建自定义的多感官数据采集平台，在此过程中，无论传感器类型如何，都会出现诸如机械设计、传感器校准、电源管理和时间同步等基本问题。我们反思了这些挑战，并分享了关键见解，以指导未来的平台设计，提高自动驾驶车辆测试的可重复性和鲁棒性。我们还总结了所采用的缓解策略，例如，使用GNSS授时和NTP协议进行全系统时间同步，针对不同传感器配置的自定义校准程序，以及旨在提高现场部署期间可靠性和数据完整性的设计实践。
英文摘要
This paper reports on the challenges encountered and lessons learned during the development and deployment of a flexible multi-sensor platform for autonomous driving research. It aims to serve as a reference for researchers developing new multi-sensor systems. As the need for reliable, diverse datasets increases, novel environments and sensing configurations are essential to tackle real-world operational challenges. Consequently, many research groups create custom multi-sensory data collection platforms, where fundamental issues, such as mechanical design, sensor calibration, power management, and time synchronization, arise regardless of sensor types. We reflect on these challenges and share key insights to guide future platform designs, enhancing reproducibility and robustness in autonomous vehicle testing. We also summarize the mitigation strategies we adopted, for instance, system-wide time synchronization using GNSS timing and NTP protocols, custom calibration routines for different sensor configurations, and design practices to improve reliability and data integrity during field deployments.
Comments6 pages, 5 figures
