Quantum Accelerates Data-Intensive Autonomous Vehicle Research
At Mississippi State University
This is a Press Release edited by StorageNewsletter.com on November 28, 2022 at 2:00 pmQuantum Corporation announces its role in accelerating all-terrain autonomous vehicle research at the Center for Advanced Vehicular Systems (CAVS) at Mississippi State University (MSU), one of the premier university automotive research centers in the world.
CAVS collects vast amounts of unstructured data using Quantum R-Series Edge Storage, a ruggedized solution for capturing massive data volumes in edge environments. The data is generated by vehicles and used for further analysis and ML model development in the CAVS data center.
Storage and processing needs for autonomous vehicle (AVs) development are growing. Mobility Foresights research estimates that 20% of new cars sold globally will have at least Level 3 autonomous driving capability by 2030. An estimated 90 million connected and autonomous vehicles will each generate up to 10TB of data per day or 1ZB per day across the industry. The automotive industry increasingly requires storage solutions that are flexible, scalable, easy-to-manage, and reliable to address this
At the CAVS facility, featuring a 55-acre off-road proving ground, test vehicles equipped with a variety of sensors collect an array of data about the outdoor terrain. This data is then used to create a digital twin of the environment for running driving simulations. These simulations are leveraged to create navigation software that guides AVs through the outdoor terrain.
Creating a digital twin of the environment requires high-quality data collected in the field. The CAVS team needed vehicle onboard storage systems that could flawlessly collect field data and enable engineers to transfer that data to the large-scale centralized data center storage for simulations.
“We needed storage that could reliably collect critical sensor data as vehicles traverse rough trails and other challenging terrain, which R-series Edge Storage provides for us,” said Daniel Carruth, associate director for advanced vehicle systems, CAVS. “With Quantum, we can move data from a vehicle to the data center quickly and easily. We have an end-to-end data management workflow that lets us stay focused on the insights that all of this data can deliver.“
Integrating the R-Series Edge Storage into a single, shareable storage platform enables CAVS engineers to make data readily available to multiple development organizations. To offload the collected vehicle data, technicians can simply remove the storage magazine from the in-car storage device and slide it into a data center chassis or use the 10GbE network port for data offloading.
The autonomous systems developed at CAVS will be vital for the military and organizations in agriculture, energy, construction, forestry, and more.
“Using the information collected in our test vehicles, we are building a comprehensive data set that will be valuable to several other teams at MSU and beyond,” says Clay Walden, executive director, CAVS. “We’re eager to see how this data will fuel breakthrough R&D in a variety of fields.“
“Data is a critical component in supporting the continued growth and success of the autonomous vehicle market,” said Plamen Minev, technical director, AI and cloud, Quantum. “Working with the CAVS team is a wonderful opportunity for us to provide a data management solution that makes storing, moving, and analyzing this critical field data simpler and more streamlined for the CAVS engineering team.“
“The researchers at CAVS are capturing massive amounts of data in demanding off-road environments and then using that data to design, develop, and validate algorithms that can power self-driving military vehicles. The R-Series Edge Storage enables them to store, quickly offload, and analyze the data for simulations and further research,” said Graham Cousens, ADAS/autonomous vehicle solutions practice lead, Quantum. “We are thrilled to partner with such a cutting-edge research facility to power the future of autonomous vehicles.“