CANBERRA, Australia, Sept. 11, 2018 /PRNewswire/ — Seeing Machines Limited (AIM: SEE) (“Seeing Machines” or the “Company”), the advanced computer vision technology company that designs AI-powered operator monitoring systems to improve transport safety, has launched a retrofit driver monitoring system for autonomous vehicles in response to clear demand from the broadening deployment of semi-autonomous and autonomous research vehicles.
The Guardian Backup-driver Monitoring System (Guardian BdMS) is designed to ensure that the backup-driver in a self-driving research vehicle is alert, aware and ready to take control of the driving task whenever necessary.
Seeing Machines has signed an agreement with one customer and is in advanced discussions with a number of companies at the forefront of autonomous vehicle development.
A growing number of technology companies, automakers and fleet operators are developing semi-autonomous and autonomous research fleets. Testing automated vehicles on public roads is critical for effective research and development, but testing brings clear risks to the general public. In most cases human backup-drivers are employed during the testing phase to help assure safe operation of the vehicle at all times.
The US National Transportation Safety Board recommended in 2017 that driver monitoring systems should be installed when testing autonomous and semi-autonomous vehicles in order to enhance safety and minimise accidents related to fatigue, dwindling attention or distraction on the part of the backup-driver.
The Guardian BdMS leverages Seeing Machines’ automotive-grade FOVIO driver monitoring technology in a convenient retrofit system for SAE (Society of Automotive Engineers) Level 3 (“eyes off”) to Level 5 (“driver off”) test vehicle fleets.
The primary function of the camera-based Guardian BdMS system is to track the backup-driver’s face and eyes during on-road automated or semi-automated vehicle testing, report driver state information (e.g. on-road, or off-road attention state), and identify distraction events of increasing severity (e.g. insufficient driver attention to the road scene).
Nick DiFiore, Seeing Machines’ Senior Vice President and Automotive General Manager, said:
“Automated driving will make driving easier, more comfortable, and eventually, safer. Entrusting the driving task to a computer will become a reality, but the journey will be gradual, complex, and potentially dangerous. Automated vehicles need to operate safely and reliably in real-world conditions.
“Drawing on our 8+ years’ experience in delivering driver monitoring safety solutions for commercial and industrial fleets, as well as in passenger car applications, we have developed a product designed specifically to meet the requirements of automated driving research fleet owners and operators, with the goal of safe innovation and on-road testing of automated driving technology.”
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Seeing Machines (LSE: SEE), a global company headquartered in Australia, is an industry leader in computer vision technologies which enable machines to see, understand and assist people. The Company’s machine learning vision platform has the know-how to deliver real-time understanding of drivers through Artificial Intelligence (AI) analysis of heads, faces and eyes. This insight enables Driver Monitoring Systems (DMS), which monitor driver/operator attention and can identify drowsiness and distraction across multiple transport sectors.
Seeing Machines develops DMS for the Automotive, Commercial Fleet, Aviation, Rail and Off-Road markets. The Company has offices in Australia, USA, Europe and Asia, and delivers multi-platform solutions ranging from embedded software and processors to aftermarket system and service solutions to industry leaders in each vertical.
DMS is now considered a core safety technology for the Automotive industry, particularly with the development of semi-autonomous and self-driving cars. DMS is also increasingly seen to be an integral safety feature across the Commercial Transport & Logistics industry globally.www.seeingmachines.com