Jason Wible Frenchcreek Talks About How AI Helps Fleet Managers to Improve Safety and Increase Efficiency

Jason Wible Frenchcreek Talks About How AI Helps Fleet Managers to Improve Safety and Increase Efficiency

Artificial Intelligence (AI) in fleet management has considerably improved how transportation businesses operate. This technology helps in the creation of safer roads, modernizes the trucking industry and improves safety culture. Industry experts like Jason Wible Frenchcreek mention that the data provided by AI solutions aid fleet managers in making streamlined decisions for improving driver safety while tracking expenses.

Jason Wible Frenchcreek briefly underlines how AI is helpful in improving safety in fleet management

Fleets can detect risky driving behavior and common compliance violations like driver fatigue, distracted driving and speeding, if there is an AI-powered fleet safety system in place. This data provides fleet managers with an improved insight into the performance of the driver, enabling them to identify where the driver has to improve. Examples of risky driving behavior that can be recognized by AI enabled video safety systems include hard braking, speeding, following distance to other vehicles and distracted driving.

Without a dependable safety system providing them with real-time insights and data, fleet managers will not be able to assess the skills of the drivers properly. An AI enabled safety system, however, learns to detect risky driving behaviors and alerts the drivers so that they can make necessary corrections in real time. For instance, in case the driver is following too close to a vehicle, they would be notified by the AI system immediately so that they can promptly correct their driving. This helps create a proper safety culture, and does away with the need for a fleet manager to review the video later and call the driver into their office.

When using AI enabled systems, fleet managers can easily access dependable data whenever required for supporting drivers when an accident occurs, combating insurance claims and maintaining driver compliance. Machine learning is additionally used to sort through expansive quantities of data, so that the managers and drivers know the cause and impact of an event. ML based dash cams learn from the behavior of the driver and predict risk around the vehicle by identifying and monitoring pedestrians, following distance, road signs and other outside influences.

Artificial intelligence based technology makes use of data for gaining insights and making predictions. Such data driven predictions aid in identifying potential wear and tear on fleet vehicles, along with other things. Armed with the ability to access this data quickly, fleet managers can effectively determine and correct driving behaviors that are likely to cause long term negative impacts on the vehicles. Many contemporary fleet vehicles feature a variety of electronic parts and sensors that are meant to gather information about a number of things, including vehicle utilization, idle timings, and fuel consumption. AI has the capability to competently predict issues and identify over exertion points on a vehicle. AI enabled camera systems monitoring tire tread through miles driven are a good example of this use. Jason Wible Frenchcreek underlines that technologies like predictive maintenance, data analytics and the Internet of Things (IoT) are widely used for improving efficiency in vehicle care, and can also be useful in managing maintenance expenses.