Building A Multi-Sensor Platform For Autonomous Driving Research: Challenges and Lessons Learned

Abstract: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.

Robotics (cs.RO)

arXiv:2610.05604 [cs.RO]

(or arXiv:2610.05604v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.05604 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.