Eye tracking technology trial with Toll

RNS Number : 2435S

Seeing Machines Limited

08 March 2019

 

RNS REACH

8 March 2019

Seeing Machines Limited

The ACE Training Centre and Seeing Machines to launch unique eye tracking technology trial

Toll Helicopters and Seeing Machines (AIM: SEE, "Seeing Machines") have joined forces to launch a unique eye tracking technology trial this month as part of the Toll AW-139 Full Flight Simulator (FFS) at the ACE Training Centre in Sydney.

The trial is based on Seeing Machines' Crew Training System prototype which incorporates modern eye tracking technology. The system supports aviation training instructors, pilots and crewmen with intelligent, evidence based data to provide detailed insights into pilot scanning techniques and situational awareness.

The 2019 trial will build on insights gathered from smaller trials conducted by Toll and Seeing Machines over the past 24 months, which have produced tangible benefits for pilots, aircrewmen and instructors:

·      Instructors - can observe crew instrument and broader cockpit scanning in real-time in the Full Flight Simulator environment, as well as during debriefs.  The unique technology provides instructors an objective assessment of what their trainees are looking at (such as speed tape, height, glide slope, heading, flight mode annunciator), at any given point in time during a flying sequence. The information can be used to quickly identify scan breakdowns, missed information, CRM (crew resource management) attention distribution, and standard operating procedure / Flight Crew Operating Manual (FCOM) adherence or deviation.

·      Trainee pilots - will benefit from objective feedback from their eye tracking replay, as they achieve even higher levels of performance.

Darryl Humphreys, AW139 Standards Manager and Flight Examiner described the tool as a valuable asset for instructors and pilots.

"For instructors, the ability to see in real time where a student's eyes are tracking is remarkable. You can observe, analyse and interpret the pilot's situational awareness. In addition, having the eye tracking auto-recorded for replay during training debriefs offers pilots a comprehensive review tool and the ability to self-remediate areas for improvement."

Toll Helicopters' General Manager and former Chief Pilot, Colin Gunn said Toll's investment in advanced eye tracking technology solidified the company's commitment to innovation and best-in-class aviation training.

"Toll and the ACE Training Centre are committed to continuously improving and innovating in our training environment. The advanced technology used in eye tracking is critical for evidence based training and our partnership with Seeing Machines provides the ultimate tool to equip our pilots, crewman and instructors with the highest levels of excellence in training." said Mr Gunn.

Patrick Nolan, GM of Aviation at Seeing Machines added, "Seeing Machines is delighted to be working with the ACE Training Centre and Toll Helicopters team to trial the Crew Training System in their world class training facility. As we move through the trial and progress to deployment of the technology more broadly, we have every confidence it will be instrumental in maintaining Toll's high standards of safety and be key to enhancing pilot training efficiencies."

The trial is being conducted in the CAE Series 3000 Level D AW139 Full Flight Simulator at the ACE Training Centre in Bankstown, Sydney.

Enquiries:

Seeing Machines Limited www.seeingmachines.com	[phone]
Ken Kroeger, Executive Chairman & CEO Sophie Nicoll, VP Marketing & Communications
Cenkos Securities plc (Nominated Adviser and Joint Broker)	[phone]
Neil McDonald/Beth McKiernan/Pete Lynch
Canaccord Genuity Limited (Joint Broker)	[phone]
Simon Bridges/Richard Andrews
Instinctif Partners	[phone]
Kay Larsen/Chantal Woolcock/Adrian Duffield

About Seeing Machines
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 identification and understanding of drivers through Artificial Intelligence (AI) analysis of heads, faces and eyes. This insight enables Driver Monitoring Systems (DMS), which monitor driver/operator identification and attention and can detect 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 to industry leaders in each vertical.

About Toll Group

Toll Group, part of Japan Post, is the Asia Pacific region's leading provider of logistics services, employing approximately 40,000 people across some 1,200 locations in more than 50 countries. Toll's second-to-none capabilities and global reach help provide customers with logistics solutions capable of serving the most comprehensive global supply chains

About Toll Helicopters

For more than 10 years Toll Helicopters has provided safe and reliable helicopters services for our customers, including emergency medical service, search and rescue operations, passenger transfer and logistics support. Headquartered in Australia, but operating across the globe, the integrated team includes pilots, aircrew, helicopter engineers, safety managers, trainers, and inventory and aviation management specialists who bring extensive civil and military helicopter experience.

About Toll and Seeing Machine's partnership

The March 2019 eye tracking technology trial follows a long history of collaboration between Toll and Seeing Machines. Toll has been installing the Seeing Machines Guardian technology (retrofit fatigue and distraction prevention) across its truck fleet since 2008 with over 1,200 vehicles now fitted with the safety device.

Online: www.acetrainingcentre.com.au

Facebook: ACE Training Centre - facebook.com/tollacetrainingcentre

Twitter: ACE Training Centre - @ACE_training_

 

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