Senior Product Manager - Connected Mobility
Safety is a top concern for fleet managers. Aside from being the ethical obligation of a business to keep their employees safe, risky driving behaviour can lead to accidents, injuries, and higher costs such as repairs, higher premiums, medical expenses, and traffic violations. Accidents alone cost the EU economy around €130 billion yearly. As a result, it can hurt employee morale, impact a company’s reputation, or worse, lead to a decline of customer trust.
That’s why more and more fleet managers are using Artificial Intelligence (AI) to run a safer, greener, and more cost-efficient fleet. From 2022-2027, the fleet management industry is expected to grow more than 15% per year on average, not least because of the growth in AI-driven technology that ensures that goods are delivered on time and drivers safe. As the technologies evolve, using smart algorithms to analyse fleet data in real-time has the potential to revolutionise safe driving practices like never before.
Let’s look at key technologies that use AI to improve fleet safety and understand how they work:
Telematics: This technology collects real-time data from different sensors on vehicles, such as location (via GPS), speed (accelerometer) and vehicle health (engine sensors) to give insights about the performance of the vehicle as well as the driver’s behaviour. Companies who are looking to improve fleet safety are often overwhelmed with the amount of vehicle data that’s available to them. That’s when AI comes in. AI, in the form of algorithms, turns these large data sets into actionable insights and alerts the fleet manager of potential issues making telematics systems a good starting point for any business to improve fleet safety, efficiency, and productivity. A study found that telematics systems can help reduce fleet accidents by as much as 30%.
Machine Learning: It’s pretty much what it sounds like: ‘Feeding’ telematics data to machines who learn from it and build models around them. This technology allows data analytics systems to process large amounts of information and analyse known events – without having to be programmed – to predict outcomes for events that haven’t even happened yet. By automating these processes, businesses can gain a competitive edge. For example, if vehicles are used a lot, maintenance is most likely needed. Based on historical data and a set of automated ‘eyes and ears’, predictions can be made which help prevent accidents keeping employees safe and costs under control.
Computer Vision: You’ve probably come across different tech terms like “Lane Departure Warning”, “Blind Spot Detection”, “Video Telematics”. These technologies use cameras, sensors, LiDAR (=Light Detection and Ranging) and image processing algorithms to monitor and analyse visual information such as road conditions, objects surrounding the vehicle, and driver alertness to improve overall fleet safety.
Natural Language Processing: NLP technology enables AI systems to understand, interpret and respond to human language, which is helpful for analysing voice-based communication between drivers and fleet managers. Relevant commands include making phone calls, assessing traffic information, or suggesting alternative routes. Alternatively, fleet managers can provide direct feedback to drivers via text-to-speech if risky driving is detected. After all, AI-enabled hands-free interaction helps drivers focus on the road and reduce accidents.
Cloud Computing: Making sense of millions of fleet data points requires computing power and storage capacity that can process large amounts of data in real-time which is necessary for any AI-driven fleet safety system to operate effectively. Cloud computing also enables predictive analytics by leveraging historical data sets to make informed decisions, identify risks in real-time and prevent accidents or breakdowns before they occur.
AI analyses historical and real-time data to identify potential vehicle failures in advance, reducing the risk of unexpected breakdowns.
AI analyses driving behaviour to detect fatigue, speeding or distraction. Alerts drivers to promote safer driving or complete trainings.
Combined with sensor data, AI detects objects and potential collisions. Issues alerts or automatically brakes or steers to avoid accidents.
Traffic, weather & road conditions, and taken into consideration by AI to determine the safest and most efficient routes for drivers.
AI uses real-time data to support features like lane keeping, adaptive cruise control etc., to improve reaction times and reduce stress.
Around the globe, our network reaches over 184 countries.
We provide the underlying transport network, the virtual overlay, and the platform to prioritise everything.
We have been recognised by industry analysts as leading network providers.
