Driver distraction represents one of the biggest influencing factors in road collisions, insurance and liability exposure, and overall fleet performance. With National Highway Traffic Safety Administration’s (NHTSA) latest figures claiming that 3,208 people were killed and 315,167 injured in 2024 in motor vehicle crashes involving distracted drivers, the issue remains a safety priority for all Commercial Motor Vehicle (CMV) fleets.
“For CMVs, research suggests that 41 percent of collisions are caused by driver error, of which, 70 percent are a result of some form of inattention or distraction, with a further 17 percent down to fatigue,” says Stuart Davis, senior product manager at Brigade Electronics. “In terms of the severity, looking at or interacting with a screen or electronic device has been shown to increase the response time of a driver by up to 57 percent. When driving, especially at higher speeds, it does not take much of a distraction to cause a collision, so even a small increase in driver response can significantly increase risk levels.”
Distraction is generally categorized into three types: visual (eyes off the road), manual (hands off the wheel), and cognitive (mind off driving).
“The most critical distractions are those that combine multiple types at the same time, says Felipe Lima, international sales and business development director of Queclink Wireless Solutions. “The clearest example is handheld cellphone usage, which typically involves all three simultaneously.”
However, risk is not defined only by the type of distraction, but also by the context and the operation. Risks are often intensified by demanding working conditions and schedules where drivers are expected to remain responsive to changing requirements and live job updates while behind the wheel. This creates an environment where attention is frequently divided, heightening distraction risk.
“Many work truck operations will tell drivers to stay focused while simultaneously requiring them to interact with dispatch, compliance, route changes, customer updates and proof-of-service tools in real time. In other words, part of the distraction burden is created by the fleet’s own operating model,” says Lima.
MOBILE PHONE DISTRACTION
Cellphone distraction represents a huge and growing risk to work truck fleets. Federal government research estimates that 6.4 percent of drivers use a handheld or handsfree cellphone during any moment of the day. Meanwhile, a survey by the Insurance Institute for Highway Safety (IIHS) found that around 20 percent of drivers admitted engaging in at least one smartphone-based distraction, such as messaging, video calling, watching content or using social media, on most or all journeys.
Most states have now passed some sort of statewide law against distracted driving and cellphone usage, which outlines when and how a driver can or cannot use a device behind the wheel. To date, over 30 states have banned handheld calls and texting for all drivers – with 20 specifically banning all drivers from holding electronic devices – while a further 17 states only ban texting. For CMV fleets, FMCSA regulations also prohibit drivers from reaching for or holding mobile devices while driving, with violations potentially impacting CSA scores and DOT compliance ratings.
Fleets are also being urged to adopt clear, consistently enforced policy frameworks including strict prohibition on handheld cellphone use while driving. However, tougher laws and corporate policy alone will not be sufficient to change attitudes towards smartphone devices and tackle this dangerous behavior. Work truck fleets will need to consider ongoing engagement, structured training, and clear communication of real-world consequences, underpinned by incident data and near-miss reporting. We will also see more fleets adopting emerging innovations, which are expected to become increasingly central to risk reduction.
One such development is mobile device blocking that prevents unauthorized access to a smartphone while driving, which is responsible for up to 40 percent of all collisions according to unofficial estimates.
“Using an app installed on a driver’s smartphone linked to a telematics unit or dashcam, without the need for any additional hardware, the blocking technology restricts access to non-essential functions while driving, says Mark Hadley, CEO of Blackout Technologies. “This includes messaging apps, social media, streaming services, Internet browsing and camera use, as well as suppression of incoming notifications. It also includes a two-minute delay to prevent drivers using their device when in stationary traffic.”
TELEMATICS AND AI CAMERAS
Telematics has become a cornerstone of modern fleet management, providing real-time visibility into driving behavior. Metrics such as harsh braking, rapid acceleration, cornering forces, idling, and inconsistent speed patterns can help identify early signs of distraction or reduced concentration. For work truck fleets, this capability enables a shift from reactive incident response to proactive risk prevention. Instead of waiting for collisions or near misses, operators can monitor trends across teams and identify emerging risks before they escalate.
The emergence of AI-powered driver-facing cameras further strengthens this telematics-based approach. Using in-cab video and machine learning, these devices can detect signs of distraction in real-time, including cellphone use, prolonged eye-off-road time, fatigue indicators such as yawning or head nodding, and other behaviors such as eating or smoking that may reduce control or attention. When risk is detected, immediate in-cab alerts can prompt drivers to refocus, creating a real-time feedback loop that helps prevent incidents. This type of in-cab coaching is claimed to be highly effective, especially amongst higher-risk drivers.
“Targeting the bottom 10 percent of drivers in terms of safety certainly has huge potential for securing improvements. One fleet found that their worst performing drivers were generating 17 times more risk events than the best drivers,” says Sam Footer, partnership director at SureCam. “Within four months of adopting AI dashcams with a coaching system, the high-risk drivers had cut events by 56 percent and were only generating four times the number when compared to their better performing peers.”
Meanwhile, Advanced Driver Assistance Systems (ADAS) are now becoming standard across modern CMV fleets. Features such as lane departure warnings, blind-spot monitoring and forward collision alerts are intended to support a driver’s situational awareness and help maintain focus on the road, providing an additional layer of protection. However, system design and usability remain critical, as overly complex, poorly integrated or excessively intrusive ADAS could increase cognitive load and contribute to fatigue or confusion.
“While on its own, ADAS does not measure distraction, they highlight when the driver fails to respond appropriately, which often indicates inattention. Not only does this offer a real-time driving aid, it also can offer added insight when viewed alongside other video, vehicle and operational data,” says Lima.
FUTURE TECHNOLOGY DIRECTION
Fleet technology is evolving rapidly, so the marketplace will see further advancements in hardware and software in terms of further aiding drivers and fleets.
“The direction of innovation is clear, and it will all be around enhanced connectivity and AI. Multi-camera contextualization will offer a better understanding of what is occurring around the vehicle at the exact moment of driver distraction, while advances in edge-based AI will allow greater on-device processing, faster feedback and more selective uploads,” says Lima.
“We will also see a deeper integration of fleet and video telematics, moving away from a camera system in isolation towards an all-encompassing risk engine, which analyses and reports on a wide range of data sources. For example, when you combine layers of data, you move from asking: Did the driver look away? to asking: Was the driver distracted, during a safety-critical moment, while the vehicle was in a rising-risk state?”
Davis agrees that an integrated approach, pulling data from multiple systems and devices, but he also suggests there is a time-based element to consider moving forward.
“Currently, a fleet can take a long-term statistical approach to identify trends and areas of improvement using driver behavior video and data to reduce driver and vehicle risk. With AI driver-facing cameras and ADAS, real-time alerting it is also possible for high-risk events that need immediate attention, but next we want to be able to understand what combination of actions and circumstances are most likely to result in a collision,” says Davis.
“With a multi-input system, we could use AI to analyze a wide range of data such as harsh driving, speed, tailgating, lane departure, distraction, fatigue, time of day and weather conditions to detect approaching risk in advance based on an accumulation of events. Once this is possible, a driver could be given a 30-minute warning and an action to address the situation, whether taking a break, reducing speed or even changing route.”
OVERCOMING DRIVER CONCERNS
The effectiveness of these systems will always depend heavily on implementation. Without clear communication, they may be seen as intrusive and result in privacy concerns amongst drivers. Therefore, Work truck operators need to position them as safety tools, not surveillance measures, supported by transparency around data use and monitoring.
Footer points out that it is important to convince drivers that the purpose of technology is non-punitive and primarily aimed at making them safer.
“Any measures should always be more carrot than stick. You need to explain the reasons behind it being adopted and why it is a benefit to them, with ongoing communication that gives them an opportunity to contribute to the entire process and express any worries they have,” says Footer.
“The balance between safety monitoring with driver privacy can be achieved through data minimization, edge processing and controlled data governance. Modern hardware uses AI models that detect distraction or fatigue in real time without continuously transmitting or storing raw video. Be transparent and explain that the system is designed to identify risk, not constantly monitor driver and in-cab behavior, while defining clear policies for retention, access control, and data usage,” says Lima.
Reducing driver distraction in work truck fleets requires a joined-up approach combining clear policy enforcement, continuous driver education, intelligent use of technology and realistic operational planning. Those that take a proactive stance not only improve safety outcomes but also strengthen service reliability, reduce operational disruption and support driver wellbeing in some of the most demanding fleet environments.
about the author
Jonathan Symons is a communications consultant and freelance journalist with over 25 years of experience in the telematics sector and working with several major suppliers of fleet technology in the transportation sector.
