How Vehicle Connectivity, Data, and AI are Transforming Fleet Management

In recent years, vehicle connectivity has become closely associated with technologies such as vehicle-to-everything (V2X) communication, which implies vehicles interacting with a wide range of ICT devices, including other vehicles, infrastructure, pedestrians, and external networks.

As organisations work to future-proof their fleets, connected vehicle technologies are expected to become the standard for fleet management. Although full V2X implementation may still be some years away, many fleets are already utilising connectivity, data, and AI to enhance operations. In this article, we’ll explore how.

Increasing driver safety

Significant advancements are expected in camera video monitoring. While standard dashcams will remain important for real-time incident detection and avoidance, integrating AI with these systems will greatly support fleet managers’ competencies to proactively stop accidents from occurring.

Driver-facing AI cameras, in particular, are capable of detecting high-risk behaviours like fatigue or distraction, simultaneously alerting both the driver and fleet manager. By analysing facial cues and behaviours, these AI-powered systems can flag dangerous behaviours that could lead to incidents, offering a new level of driver safety.

Improving driver training

Telematics devices can collect detailed data on discouraged driving habits, such as over-acceleration, harsh braking, sharp cornering, and engine idling. Consequently, these insights give fleet managers a comprehensive view of each driver’s behaviour on the road.

By identifying areas for improvement, fleet managers can offer targeted driver training tailored to address certain habits, providing them with personalised guidance and support. This focused approach not only refines individual performance, but also achieves considerable benefits for fleets as a whole in terms of safety and efficiency.

Ensuring regulatory compliance

As previously mentioned, dashcams can be integrated into fleet management systems to establish effective incident management. However, this is particularly advantageous for construction fleets, where adherence to regulations is paramount.

Dashcams equipped with equal audio and video capabilities enable fleet managers to precisely review accidents like injuries, collisions, or instances of aggression. This strengthens compliance with lone worker safety protocols, while also solidifying the protection of drivers operating specialised vehicles. Namely, the real-time data helps ensure prompt responses to incidents and provides valuable records for regulatory and safety audits.

Optimising routes

AI is increasingly being used for route optimisation in fleet management. This is because AI-driven fleet management software can analyse vast amounts of data, identify patterns, and create routes which have taken into account factors such as time, distance, and potential cost.

In addition, AI continuously tracks variables like traffic, weather, and road conditions to adjust routes for maximum efficiency. The benefits are substantial in this context, encompassing better fleet cost control, lowered carbon emissions for greater sustainability, and faster job completion times - especially vital in last-mile deliveries.

Enhancing communication

Natural language processing technology empowers AI-based fleet management systems to understand, interpret, and respond to human language. In turn, supplementing interactions between drivers and fleet managers.

The key functionality refers to facilitating communications regarding alternative route suggestions, job instructions, and vehicle diagnostics. Moreover, fleet managers can send immediate feedback to drivers through text-to-speech technology when risky driving behaviours are detected. Ultimately, such AI-enabled interaction allows drivers to focus on the road while keeping both them and their manager informed.

Streamlining vehicle maintenance

Effective fleet data management requires substantial computing power and storage capacity to process large volumes of information in real time. So much so, that this capability is essential for any AI-driven fleet management system to function optimally.

Cloud computing plays a distinct part here by using predictive analytics to generate historical data sets which can inform decision-making and prevent breakdowns before they happen. Specifically, AI analyses both historical and current data to spot vehicle failures in advance, allowing fleet managers to schedule predictive maintenance and keep on top of service intervals, thereby decreasing the likelihood of unexpected breakdowns and minimising costly repairs.

The role of OEMs

Vehicle connectivity, data, and AI are each transforming fleet management by making operators more influential than ever before. The ability of AI to analyse vast amounts of data from embedded and interconnected OEM hardware devices equips fleet managers with actionable insights to maximise operational efficiency, cost reduction, and productivity.

From real-time route optimisation and communication to driver safety and vehicle maintenance, modern technology is fundamentally changing how fleets are managed. Furthermore, as AI collects more data through OEM processing via the cloud, its predictive proficiency exponentially improves, inevitably resulting in more intuitive fleet management for the future.

At MICHELIN Connected Fleet, we are at the forefront of vehicle connectivity, data, and AI innovation. Likewise, our strong OEM partnerships guarantee seamless integration of our fleet management solutions. If you’re interested in reaping the benefits we've discussed to give your fleet a competitive edge, then be sure to make an enquiry into our services today.

Written by MICHELIN Connected Fleet

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