AI (artificial intelligence) will help improve many aspects of fleet management and maintenance, from reducing unplanned downtime to increasing efficiency throughout the maintenance and repair process and improving fuel economy. Machine learning and AI can provide fleet operators with critical data that can be used to optimize operations, as well as predictive analytics to enable better decisionmaking in the future based on the analysis of past fleet activities.

Drivers too can benefit from IoT (Internet of Things)-enabled data. For instance, realtime data about what’s going on outside on the roadways, such as weather conditions, road conditions, and traffic, can help fleet drivers get where they’re going faster and with fewer hiccups along the way. Fleet managers can similarly use this data to track vehicles and make informed decisions about dispatching, eliminating much of the guesswork involved in fleet logistics. When data makes fleet operations more predictable, operations can become more streamlined and efficient.

Innovative technologies are also helping to keep fleet drivers safe behind the wheel. Lytx, a provider of machine vision and AI-powered video telematics solutions for fleets, recently shared a use case for AI in reducing drowsy driving. Lytx’s customer, transportation service provider Hogan Transportation, says it leverages the company’s Driver Safety Program to gather the data it needs to gain visibility into fleet operations and to better understand what is happening across its fleet of vehicles. With this knowledge, Hogan is taking steps to reduce driver fatigue.

The Driver Safety Program combines video-based coaching, comprehensive reporting, and backend support to help drivers be as safe as possible. A DriveCam Event Recorder, a small LTE (long-term evolution)-enabled device mounted beneath a fleet vehicle’s rear-view mirror, records driving behaviors that may be risky and sends this data to the cloud, where it’s analyzed, prioritized, and sent back to the fleet manager to open doors for analysis, reporting, and coaching. Behind the solution are AI and machine learning algorithms and insights derived from Lytx’s database of 100 billion miles of driving data.

Thanks to realtime, in-cab alerts from the Lytx device, drivers can get instant feedback on their driving behaviors, prompting immediate self-correction. Unsafe driving behaviors include things like using a handheld device, speeding, cornering, and not wearing a seat belt. It also includes drowsy driving. The company’s data reflects a 39% reduction in drowsy driving events among clients between June 2018 and June 2019, as well as 66% reduction in drivers falling asleep behind the wheel.

When it comes to identifying and preventing distracted and drowsy driving, technology can help curb unsafe behaviors in the short-term (i.e., via realtime visible or audible alerts) and the long-term (i.e., via analytics, reports, and coaching). For fleet managers, whose businesses depend on safe driving, the use of AI to recognize patterns that enable data-driven decisionmaking can be extremely valuable. With a window into the cab and fleets as a whole, management becomes a lot easier. With the motivation to do well always, fleet drivers become a lot safer.

Want to tweet about this article? Use hashtags #IoT #M2M #distracteddriving #fleets #fleetmanagement #transportation #AI #artificialintelligence #machinelearning #analytics #data #telematics