Autonomous/ADAS reference architecture captures, manages sensor data
The the unique architecture, says the company, is designed to address the specific needs of every stage of ADAS/AD development by enhancing the acquisition, movement, storage, and curation of the data necessary to develop autonomous vehicle software.
“Although still relatively nascent, organizations developing autonomous vehicles are at a crossroads,” says Jamie Lerner, president and CEO, Quantum. “The volume of data being captured is increasing exponentially, presenting an urgent need for speed, capacity and cost-efficiency in the data management lifecycle. As the experts in unstructured data capture, storage, management, and enrichment, we are leading the way in delivering a complete portfolio of end-to-end solutions and lab-proven technology that delivers the industry’s best performance, capacity and scalability – all requirements for ADAS/AD solutions – at a fraction of the cost. This new reference architecture empowers ADAS developers to build the self-driving vehicles of tomorrow.”
The vast amount of data generated during autonomous vehicle development illustrates the scale of challenges faced by AV manufacturers, says the company. Test vehicles typically capture terabytes of sensor data per hour generated by multiple video cameras, LiDARs, and radars. ADAS/AD development systems rely on collecting and processing these large amounts of unstructured data to build sophisticated machine learning (ML) models and algorithms, requiring intelligent and efficient data management.
The data processing in an AV development system starts with capturing data in a test vehicle. The Quantum R6000 is an ultra-fast automotive and mil-spec edge storage device explicitly developed for high-speed data capture in challenging, rugged environments including car, truck, airplane, and other moving vehicles. It provides a large data storage capacity necessary for the in-vehicle logger to store the collected sensor data for an extended period of time, all in a small form factor that makes it well suited for self-driving test vehicles.
The R6000 is designed to withstand the demands of a rugged environment and is purpose-built for high availability and reliability. Once data is captured, the R6000 removable storage canister enables quick data offload and on-the-road replacement, allowing cars to stay in service and reduce vehicle downtime.
Data is then uploaded to the Quantum StorNext File System for processing. StorNext, says the company, has demonstrated the fastest overall response times for video data using independent benchmark testing, and has the ability to process thousands of concurrent streams at high throughput. Further, StorNext software includes a policy engine with options to place and manage data on NVMe, HDD, object storage, cloud, and tape. This unique data management capability enables full and efficient use of the analytics infrastructure across multiple tiers.
With Quantum, once the ML model training and verification is complete and new models developed and deployed, the massive data sets required for future ML development can be retained on low-cost storage providing the right balance between the highest performance and best economics.
“Autonomous vehicle manufacturers are capturing massive amounts of roadway data,” says Graham Cousens, ADAS/Autonomous Vehicle Solutions practice lead, Quantum, “and then using that data to design, develop, and validate algorithms that can power self-driving cars. The challenge they’re grappling with is how to effectively extract insights, integrate with other pieces of their architecture, and retain that data for longer periods of time. These are challenges that Quantum has been solving for over 40 years in other sectors. Based on solutions that have been proven to outperform the competition in lab testing – driven by the powerful StorNext File System and our ultra-fast automotive & mil-spec R6000 in-vehicle data storage device – this new reference architecture is set to streamline and power the future of autonomous vehicles development.”