rFPro’s Data Framing Cuts Hardware Costs
UK-based driving simulation company, rFpro, has developed a means to slash the hardware costs associated with large-scale simulation. The ground-breaking development has the potential to remove the industry’s dependence on manual annotation of test data that is created frame-by-frame, which is both time-consuming and error-prone.
“Currently, many players in the autonomous vehicle field employ an army of people to manually annotate each frame of a video, LiDAR point or radar return to identify objects in the scene (such as other vehicles, pedestrians, road markings and traffic signals) to create training data,” said Matt Daley, rFpro Managing Director. “This new approach from rFpro provides a digital, cost-effective way of creating the same data completely error-free and 10,000 times quicker compared to manual annotation, which takes around 30 minutes per frame with a 10% error rate. This step-change will enable deep learning to fulfil its potential because it significantly reduces the cost and time of generating useful training data.”
rFpro calls the new approach Data Farming and compares it to Render Farming, which has revolutionised the economics of popular animation. It enables customers to build complete datasets that cover the full vehicle system where every sensor is simulated at the same time. The data is synchronised across all sensors, even with the most complex hardware designs. This is essential where customers are employing sensor fusion to bring together data, for example from multiple 8K HDR stereo cameras, LiDAR and radar sensors at the same time.”
Data Farming is already being utilised by existing customers, including global Tier1 supplier, DENSO ADAS Engineering Services. “Through rFpro’s Data Farming we can create an extensive number of driving scenarios, allowing the generation of very large variations in scenes, all through the investment in a single platform,” said Francisco Eslava-Medina, Project Manager at DENSO ADAS. “This allows us to quickly and cost-effectively generate the vast quantity of quality training data that is essential for certain product development phases of computer vision technologies, especially for neural networks for our autonomous vehicle technologies.”
The new approach permits customers to start with even a single PC to perform a complex simulation involving multiple sensors. “Simulations don’t have to be run in real-time, offering flexibility to the user around the computing power required,” added Daley. “For engineers, this puts it within a typical departmental budget, rather than requiring senior approval. High quality training and test data is now far more accessible.” Data Farming is fully scalable, allowing customers to expand across multiple hardware resources when they are ready to accelerate their data production.
Another customer that has successfully employed Data Farming includes Ambarella, a leading autonomous vehicle technology provider. “The software presents a radical shift in creating training data and is already accelerating the development of our autonomous vehicle systems,” said Alberto Broggi, General Manager of Ambarella’s division in Italy. “Deep learning and AI are critical to the successful adoption of autonomous vehicles. It may not be reasonably possible to get to the standard required only through the use of manually annotated data sets. Data Farming will transform the way the industry develops autonomous vehicles”.
SOS Lab Funded
In April 2020, SOS LAB, Korea’s ‘LiDAR’ sensor startup company, secured series A+ investment of a total of USD 8 Million starting from a lead investment from Korea Development Bank (KDB), bringing the company’s total capital raised so far to USD 14 Million. It continued to attract investments after securing seed funding from ‘Future Play’, an accelerator for tech startups. In addition, the participation of the IPO team of Yuanta Securities in the investment confirmed the value of the existence of SOS LAB as an emerging tech startup. Other investment companies such as A ventures, Emford Equity Partners, Ulmus Investment, KDB Capital, Shinhan Capital and Shinhan Financial Group also participated in the A+ round investment. Despite a decline in general economic activities due to COVID-19, SOS LAB successfully attracted the investment and it has now unveiled its plans for the commercialization of LiDAR.
Panasonic Invests in Blue Yonder
Panasonic Corporation announced that it is making an equity investment in Blue Yonder — the leading end-to-end supply chain software provider. The investment via a secondary sale of shares values Blue Yonder at an enterprise value of $5.5 billion. It builds upon a strong strategic relationship between the two companies, including a joint venture in Japan announced in April 2019. Panasonic will have a 20% minority ownership stake and one seat on the Board of Directors of Blue Yonder.
The expanded partnership between Panasonic and Blue Yonder will accelerate the promise of the Autonomous Supply ChainTM. Harnessing the edge via the Internet of Things (IoT), Blue Yonder’s platform utilizes machine learning to drive faster, more context-aware business decisions — all to deliver autonomous outcomes.
Blue Yonder and Panasonic will combine resources and work closely with partner companies in Japan to fuel growth by selling Blue Yonder’s Luminate™ solutions and bringing forth new, jointly-developed solutions on the Blue Yonder Luminate Platform that enhance customers’ capabilities for supply and demand forecasting, inventory and labor optimization, and streamlining business operations.