CAS & FATIGUE
to these challenges and illustrates how sensor-based safety architectures are evolving.
At its core, PDS4.0 combines active RFID technology with advanced zone-based detection logic. Vehicles automatically generate configurable detection zones that are visualised to the operator through the onboard Mining Information System( OMI). These zones form graded safety layers ranging from early recognition to automatic braking. By structuring risk areas in this way, the system supports situational awareness and helps operators assess hazards before they escalate.
A key aspect of the system design is the interaction between vehicle and personnel equipment. Workers carry personal TAGs that continuously communicate with nearby PDS-equipped machines. When a vehicle enters detection range, TAGs provide immediate feedback via visual signals, enabling personnel to react even in noisy or visually restricted environments. This mutual awareness is a defining feature of modern proximity detection concepts.
Another component is the system’ s ability to respond autonomously. Depending on the zone reached, PDS4.0 issues warnings or initiates controlled deceleration up to a complete emergency stop. These interventions are designed to integrate with existing vehicle dynamics and operational workflows, ensuring that safety measures remain effective without compromising overall process efficiency.
The flexibility of PDS4.0 is reflected in its modular hardware kits tailored to different vehicle classes- from light-duty transport vehicles to heavy and super-heavy equipment. The associated antennas and transceivers differ in coverage and radiation patterns, allowing operators to match system characteristics to their specific fleet and underground geometry.
While the system’ s primary purpose is collision avoidance, its structure also supports broader operational objectives. By logging detection events, movements, and interactions between vehicles and personnel, PDS4.0 can contribute to datadriven safety analyses and continuous improvement initiatives. As mining environments become more digitalised, such integrated datasets offer increasing value.
Overall, Becker argues that PDS4.0 illustrates how proximity detection has matured from isolated warning devices to comprehensive safety subsystems.“ Through a combination of configurable detection logic, two-way awareness between personnel and machinery, and automatic intervention capabilities, the system addresses both regulatory expectations and practical mining realities. As mines continue to adopt higher automation levels, such technologies will play a decisive role in safeguarding workers and maintaining operational continuity.”
A new approach to mining fatigue risk management
Mining safety is a complex topic – but there is an increasing realisation of the benefits of spotting risks early, instead of waiting for something to go wrong and relying only on lagging indicators like incidents, alarms, or near misses. Instead, many mines are starting to use leading indicators that show risk before it happens.
Fatigue has always been a risk in mining – but it is no longer seen as a personal problem for the operator, rather something that can be measured and managed as part of an overall safety strategy.
As part of this shift, many mines are now using tools that can predict fatigue risk ahead of time. AI models can show when workers might become fatigued, even hours before a shift starts. This helps leaders plan safer staffing, choose the right tasks for workers, and keep critical controls strong. Fatigue Science’ s Readi platform is one tool being used in mining, which combines sleep science, schedule modelling, and AI to show where and when fatigue risk may appear. Reactive tools, like in-cab cameras, remain still critical to safety, but they only spot fatigue after a worker is already at risk – so they are lagging indicators which cannot prevent risk on their own. Fatigue Science argues that proactive fatigue insights are becoming something mining leaders expect, not an added extra.
IM spoke to Bryden Waggott, VP Product at Fatigue Science. He stated:“ Most investment in mining has been in reactive fatigue tools like in-cab camera systems used in haulage and people transportation. These systems are important, but they can be sensitive and often set off false alarms. The bigger issue, though, is that you don’ t want workers entering high-risk areas fatigued in the first place. That’ s the problem with relying only on reactive detection.”
As mines move toward more proactive safety controls, fatigue is increasingly being treated as a‘ fit for work’ and‘ fit for task’ issue- similar to equipment readiness. Waggott adds that mining companies understand that workers often can’ t accurately self-assess fatigue. By the time someone feels tired, their performance may already be affected.
He adds:“ Predictive fatigue modelling helps remove guesswork by using science instead of personal judgment. It looks at sleep patterns over several days, matches them with shift schedules, and factors in the body’ s natural clock to predict when fatigue is likely. This gives supervisors the leading indicators they need to act before a worker enters high-risk work, before alarms go off, and before a near miss happens.”
In the past, this kind of modelling required wearables, but those came with challenges. Mines have tough environments that can break devices. Big workforces also make it hard to get everyone to use them- workers may forget them, not want to wear them, or accidentally damage them. Wearables can still be useful in some cases, but many mines now want solutions that are easier to use and can scale across the whole site. Mining safety supervisors are trying to reduce friction for the workforce. Their goal is to support safety practices, not burden their crews who already operate in demanding environments.
Fatigue Science argues that it has been able to solve this problem by creating a non-wearable way to understand sleep patterns using proven questionnaires and AI. Waggott explains:“ We learned that you can model fatigue risk by looking at a worker’ s sleep over several days, without needing to track their sleep with a wearable device. We compare these sleep patterns to millions of anonymous profiles in our database. It’ s very accurate, and it also makes things easier for workers.”
He says this move toward non-wearable safety tools fits where Fatigue Science believes mining safety is heading- solutions that can scale, don’ t intrude on workers, and are backed by science.“ Predictive fatigue insights also work well with reactive camera systems. They help cut down on false alarms and make sure workers start their shifts in a safer state. Together, these tools create a new safety standard- a layered approach where proactive insight helps prevent risk, and reactive systems act as a last line of defence.”
In many mines, he says this approach is changing fatigue management from a simple compliance task into a true leadership practice.“ It gives supervisors the same kind of early warning and foresight they already expect from geotechnical, mechanical, or environmental systems.”
Fatigue Science says it is already helping large mining and fleet companies in North America and around the world use nonwearable fatigue tools, as many workers say they prefer not being monitored by hardware.“ There has been really positive feedback from the operator’ s perspective as it nurtures a safety culture without operators thinking they are in trouble because they triggered a reactive alarm.”
While Waggott accepts that it will take time for many mines to move beyond traditional wearables, but that as more industries utilise non-wearable predictive tools and the value is proven, adoption in mining will grow.“ Mining has always improved its safety practices when new
72 International Mining | JANUARY 2026