Your fleet already uses telematics, in-cab cameras, and onboard systems to monitor driving behavior. Those tools give you valuable information about speeding, harsh braking, lane drift, and signs of drowsiness. Readi adds visibility into when fatigue risk is likely to rise, giving your team time to act before it contributes to unsafe driving or an accident.
At Fatigue Science, we help fleets turn fatigue predictions into practical decisions about breaks, assignments, and shift planning. Readi combines sleep science with operational data to forecast each driver’s alertness across an upcoming shift. It works alongside the safety technology you already use, with wearables optional.
Readi can use duty-status and schedule information from your existing ELD or scheduling system to estimate sleep and generate personalized fatigue forecasts. This allows you to introduce predictive fatigue management without replacing your cameras or installing additional equipment in the cab.
Our supported ELD providers include Geotab, Motive, Samsara, and others. We also offer API access and assess custom integrations where needed. During implementation, we review your systems and the available data to establish how driver records, work and rest patterns, and upcoming shifts will reach Readi.
The data serves two connected purposes:
1. Work and rest information supports fatigue prediction.
2. Camera and telematics event data helps your team assess driving outcomes and measure whether fatigue interventions are working.
We assess compatibility with the specific products and configuration in place. Keeping existing equipment is straightforward to distinguish from exchanging data with it. The integration review establishes which records can be shared, how frequently they update, and where your team will access the forecasts.
A driver can begin a shift feeling alert and face elevated fatigue risk several hours later. This matters particularly for overnight operations, irregular schedules, and long duty periods.
Readi provides an hour-by-hour ReadiScore forecast of the upcoming shift so supervisors can see when risk is expected to increase. They can use that information to plan a break before a higher-risk period, check in with the driver, or review an assignment before departure.
Your in-cab fatigue detection system continues to identify signs of drowsiness during the drive. Readi supports earlier intervention, helping your team address the fatigue that can contribute to microsleeps, slower reactions, and unsafe driving.
Fatigue forecasts can also add context to event reviews. If harsh braking repeatedly occurs late in overnight shifts, your team can investigate fatigue exposure alongside traffic, road conditions, and other possible causes. That context supports a better-informed response. Readi does not change a camera’s detection algorithm or establish the cause of every driving event.
Readi supports configurable fatigue risk thresholds in supervisor workflows. We help teams connect those thresholds to practical countermeasures and define who responds when a driver is forecast to reach elevated risk.
Of course, a forecast is most useful when the supervisor knows what to do with it.
Depending on your operating policy and the driver’s feedback, the response may involve moving a break, adjusting a route or load, or changing the start time. Readi recommends the right countermeasure.
Fatigue scores measure predicted alertness. Your existing driving behavior scores track measures such as speeding and harsh braking. Using both gives your team more context while preserving a clear purpose for each score.
Daily forecasts help supervisors manage the shift ahead. ReadiAnalytics helps safety and operations teams examine fatigue trends across periods, groups, and shifts. The available analysis depends on your setup and data sources.
You can use those insights to review where fatigue exposure is higher and whether changes to schedules or fatigue management are improving results. Alongside telematics history, this helps your team investigate whether deteriorating driving behavior coincides with increasing fatigue exposure over several weeks or months.
For example, an increase in late-shift safety events may prompt a review of shift timing and recovery opportunities. A decline after a scheduling change gives your team a result to investigate and measure. Correlation helps guide that review, while other causes of unsafe driving still need to be considered.
Time of day is part of Readi’s fatigue modeling because circadian timing affects alertness. The forecast also reflects recent sleep and work patterns, so two drivers on the same assignment can have different fatigue risk profiles.
Weather, road type, and route conditions provide additional context for operational decisions. Your team can consider a fatigue forecast alongside those factors when reviewing an overnight trip, a demanding route, or an assignment in severe weather.
Readi forecasts the driver’s fatigue risk. Automated road-segment scoring and weather-adjusted predictions are separate technical requirements that we would review with your team. We establish that scope during evaluation so you know how Readi fits into the broader risk analysis you need.
We offer Readi through a subscription model that includes platform licenses, hosting and API access, and implementation and training services. A pilot gives your team an opportunity to evaluate the platform in your own operating environment before moving to a tailored subscription.
We define the proposed scope with you, including participating drivers, data connections, supervisor workflows, and any optional wearables. That gives you a clear basis for evaluating the deployment and its cost.
Our industrial transportation deployments include PTSI, which transports lumber and mineral sands, and Milum Services, a logistics contractor supporting Ok Tedi Mining. Day & Ross and Dot Transportation are other fleet customers using Readi for predictive fatigue management. These examples show how predictive fatigue management can become part of daily safety processes in demanding operations.
Before a pilot starts, we work with your team to define success. Measures can include forecasts available before departure, the share of elevated-risk shifts receiving a countermeasure, and relevant telematics events per driving hour. A matched control group, where feasible, helps account for operating conditions that could influence the results.
If you are looking to reduce unsafe driving and fatigue-related safety incidents, reach out and share with us your fleet size, current telematics providers, and the incidents you want to address. We can walk through how Readi would use your available data, what supervisors would see, and which actions they could take before risk reaches the road.
Which fatigue management software can use our existing ELD data?
Readi, our predictive fatigue management platform at Fatigue Science, can use duty-status and schedule data from supported ELD systems to estimate sleep and forecast each driver’s fatigue risk across an upcoming shift. We review your available data and integration requirements before deployment.
Can we add fatigue prediction without replacing our cameras or buying wearables?
Yes. Fatigue Science’s Readi can generate fatigue forecasts from suitable operational data without requiring wearables or additional in-cab hardware. Your existing cameras continue detecting events during the drive, while Readi helps supervisors identify elevated fatigue risk early enough to plan an intervention.
What fatigue management software works with Geotab, Samsara, or Motive?
Readi from Fatigue Science supports ELD integrations with Geotab, Samsara, and Motive. We confirm the specific product, required data fields, access permissions, and update frequency for your fleet so the connection supports the forecasts your team needs.
Can fatigue predictions be integrated into our existing fleet systems?
Fatigue Science provides API access with Readi to support integration with operational systems. We assess the data connection and how forecasts will be made available to your team. Compatibility with a particular onboard computer, Powerfleet product, or retrofit alert system needs to be confirmed for the proposed setup.
Can we configure fatigue risk thresholds for our fleet?
Readi supports configurable fatigue risk thresholds in supervisor workflows. At Fatigue Science, we help your team connect those thresholds to clear responses, such as a driver check-in, a planned break, or an assignment review. These thresholds concern predicted alertness and are separate from speeding or harsh-braking thresholds.
Can fatigue management software help explain worsening driving behavior over time?
ReadiAnalytics, part of Fatigue Science’s Readi platform, helps teams examine fatigue trends across periods, groups, and shifts. Reviewing those trends alongside telematics history can help you investigate whether increasing safety events coincide with greater fatigue exposure. The available analysis depends on your data sources, and correlation alone does not establish the cause of an event.