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The future of safe driving: How AI is transforming fleet management

14 Sep 2023
David Brown

David Brown

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.

What are the technologies behind AI-enabled fleet safety?

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.

How can AI improve fleet safety and optimise operations?

AI plays an important role in improving fleet safety through various applications. Here are five examples of the most common use cases:

Predictive Maintenance

AI analyses historical and real-time data to identify potential vehicle failures in advance, reducing the risk of unexpected breakdowns.

Driver Monitoring

AI analyses driving behaviour to detect fatigue, speeding or distraction. Alerts drivers to promote safer driving or complete trainings.

Collision Avoidance

Combined with sensor data, AI detects objects and potential collisions. Issues alerts or automatically brakes or steers to avoid accidents.

Route Optimisation

Traffic, weather & road conditions, and taken into consideration by AI to determine the safest and most efficient routes for drivers.

Driver Assistance

AI uses real-time data to support features like lane keeping, adaptive cruise control etc., to improve reaction times and reduce stress.

What happens if we don't apply AI in our fleets?

If AI is not applied in fleet safety, there may be several negative consequences for a business:

More Accidents

Without AI, fleets are faced with an increased risk of accidents due to distraction, poor driving behaviour, and unforeseen road conditions.

Higher Costs

Accidents and sudden breakdowns can lead to unplanned costs and missed opportunities. Maintenance tends to run on a fixed schedule instead of analysing data in real-time resulting in more frequent repairs and increased downtime.

Reduced Productivity

Not utilising AI means you're not automating fleet analytics to the full potential. As a consequence, process and tasks are not streamlined and can lead to higher workload and lower productivity.

Lower Efficiency

Fleets managers who don't use AI typically rely on manual processes instead of predictive analytics. This is time-consuming, error-prone and can lead to operational inefficiencies.

How can businesses promote fleet safety effectively? Here’s a list of steps you can take:

  1. Develop a safety culture: At Vodafone, ‘Drive Safely’ is one of our ‘Absolute Rules’. It’s a cultural codex that puts employee safety above everything. It’s the foundation of fleet safety to promote a workplace culture that emphasises safe driving practices. It’s essential to have everyone onboard and set clear guidelines to create a shared commitment that goes beyond the workplace.

  2. Invest in vehicle safety tech: Don’t restrict yourself to the technology you’re currently using. Instead, see what innovations the market has to offer and integrate into your fleet operations. Lane departure warning, blind spot detection and adaptive cruise control are just a few Advanced Driver Assistance Systems (ADAS) that will reduce the risk of getting into an accident significantly.

  3. Check your drivers’ behaviour: Without driving data, it’s just an opinion. You can change this by using telematics systems that monitor driving behaviour (along with location and vehicle data) such as speed, braking, cornering or even driver distraction. The goal here is to gain more insights about your fleet, identify areas for improvement and reward those who drive safely.​

  4. Offer ongoing driver trainings: The more you repeat, the more likely you’ll remember. The same goes for fleet safety trainings. If you want to promote safe driving practices ongoing trainings or refreshers are essential. Individual driver scores help assess which topics should be covered such as eco-driving, vehicle handling, hazard awareness and distracted driving.

Want to improve fleet safety but don’t know where to start?

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