Enhance fleet safety
with Active Fatigue and Distraction Detection technology
Reducing Risk in a Hazardous Work Environment
In the high-risk environment of the oil and gas industry, ensuring the safety and wellbeing of workers is paramount. One significant challenge workers face is the risk of driver drowsiness and distraction, which can compromise their ability to perform tasks safely and efficiently.
“Falling asleep behind the wheel or being distracted while driving are amongst the leading causes of road accidents worldwide. Our road transport fleets have begun deploying devices that detect signs of microsleeps, fatigue and distraction, and respond by warning drivers so they can take action to stay alert.” Shell1
Land transportation safety practice
The International Association of Oil & Gas Producers (IOGP) is a global association representing the oil and gas industry, focusing on issues like safety, environmental protection, and technical standards.
In their report 365 – Land transportation safety practice, IOGP recommends the use of vehicle camera systems and Active Fatigue and Distraction Detection (AFDD) devices.
“External research shows that 94% of all motor vehicle crashes are caused by human error… Distractions are shown to compromise the safety of the driver, passengers, pedestrians, and people in other vehicles… Fatigue negatively affects a driver’s physical, cognitive, psychomotor, and sensory processing capabilities, which are needed for safe driving.” IOGP2
Active Fatigue and Distraction Detection
AFDD technology helps manage driver drowsiness and distraction by keeping drivers alert and focused while they're on the road. The driver monitoring system is designed to detect signs of fatigue or distraction, like drowsiness or being unfocused, and alert the user to prevent accidents.
Watch the video to learn how AFDD devices can help oil and gas companies manage fatigue and distraction.
The basics
The device is a fleet safety technology that helps keep drivers alert and focused while they're on the road. It uses sensors to detect signs of driver drowsiness or distraction, like if their eyes are closing or if they are not paying attention to the road. When the system notices these signs, it alerts the driver to help prevent accidents.
AFDD devices typically use sensors to track various signals, such as eye movements and facial expressions. The system then analyses these signals to detect signs of fatigue, drowsiness, or distraction, such as eyelid drooping, head nodding, or repeated off-road glances.
When driver drowsiness or distraction is detected, the device may alert the driver through visual, auditory, or haptic signals to prompt them to take a break or regain focus.
The primary benefit of an AFDD device is that the system alerts the driver in real time when a distraction or fatigue event (such as a microsleep) occurs, helping to prevent potentially fatal and costly accidents.
Some devices, like Guardian, also alert fleet operations of an event as it happens, so they can intervene at the right time where necessary. In fact, the human intervention aspect of our solution has been scientifically proven to significantly enhance the effect of in-cabin alerts. In-cabin alerts can reduce risky driving events by around 66%, which is great, but this jumps to 90%+ with real-time intervention and driver fatigue management.
Plus, data from AFDD devices can support driver coaching and operational improvements across the fleet.
The IOGP recommends devices should:
- use artificial intelligence (AI) camera-based technology,
- be tested for their accuracy in a variety of scenarios, and
- comply with local data privacy requirements and laws.
Specific requirements include the detection of fatigue and distraction events, real-time warning alerts and automated event recording. More information can be found in their report: 365 – Land transportation safety practice.
Seeing Machines work with global oil and gas companies to enhance fleet safety and operations with Guardian, our industry-leading safety solution which combines distraction warning technology with a driver drowsiness detection system.
Contact us to discover how Guardian can help reduce the risks of fatigue and distraction to improve driver safety across your company and comply with the IOGP recommendations.
Protecting your fleet
Guardian is a preventative fleet safety solution that uses computer vision and machine learning to mitigate the risks associated with fatigue and distraction. Using in-cab sensors to monitor the driver’s levels of drowsiness and distraction in real time, combined with 24/7 monitoring and analytics services, Guardian is a proven AFDD solution for vehicles in the oil and gas industry.
Detection
Guardian is setting a new standard in driver distraction and drowsiness detection; meticulously tracking the eye gaze to provide a significantly more accurate indication of the driver’s focus.
Alerts
The automotive-grade in-vehicle system monitors drivers for signs of fatigue or distraction, intervening in real time through a set of audio, visual, and haptic alerts.
Intervention
What makes the Guardian AFDD solution unique is combining our proven fleet safety technology, with monitoring and intervention by real humans.
Data
Real-time data offers valuable data and rich insights used to understand situations as they evolve, and support compliance and reporting requirements.
Demonstration
We’re supporting global oil and gas companies to enhance fleet safety and operations with Guardian. Submit the form to request a demonstration of our industry-leading AFDD technology.
Learn more
AFDD in the oil and gas industry
A brief introduction to Active Fatigue and Distraction Detection technology and why it is important in the high-risk environment of the oil and gas industry.
AFDD and other safety systems
Discover the difference between Active Fatigue and Distraction Detection devices, Advanced Driver Assistance Systems (ADAS) and In-Vehicle Monitoring Systems (IVMS).
Guardian: protecting the oil and gas industry
Guardian is a preventative AFDD solution that uses computer vision and machine learning to mitigate the risks associated with drowsy and distracted driving.