FROM THE INDUSTRY - VisionTrack

How Ai Analysis Is Key To Any Effective Vehicle Telematics Solution

Artificial Intelligence (AI) is having an ever-greater influence on our everyday lives, so unsurprisingly, it has a growing, and key, role in how fleets approach road safety and reduce risk. AI video telematics – using vehicle cameras connected to a cloudbased platform – has massive potential within the sector.

AI vehicle cameras have been around for several years. They are enabling fleet operators to maintain safety levels for both their drivers and other road users by automatically monitoring hazards on the road as well as high-risk behaviours and distractions. This makes it possible to provide real-time feedback straight to the driver, but there is also the added benefit of gaining valuable insight into fleet risk.

These intelligent vehicle camera systems have proven safety benefits, but they are still limited by the processing capacity of the device, so it is in the cloud where the true value of AI will be realised. The challenge for many fleet operators is simply the volume of video and data that is captured, which makes timely and efficient manual review almost impossible.

G-force settings for cameras are highly sensitive to ensure collisions are picked up. As a result, triggered video can exceed hundreds per day, many of which are false positives caused by speed bumps, potholes and other harsh driving events. Effective analysis is crucial for any video telematics solution, yet a manual process – whether delivered in-house or through a third-party service – rarely provides the accuracy or responsiveness to take full advantage.

With cloud-based AI, it is possible to cut through all the noise, so fleets are presented with the information that requires immediate attention. A 1,200-strong fleet has been able to reduce priority videos needing human validation and intervention from 12,300 to just 15 using our NARA AI post analysis software, making it possible to check in less than five minutes.

The latest advances in computer vision can be used to review huge amounts of data, automatically identifying different types of vehicles, cyclists and pedestrians to achieve accurate incident validation within seconds. This added layer of analysis enables emergency assistance to be quickly summoned in the event of a suspected injury, while targeting a reduction in associated fleet costs.

Meanwhile, the ability to detect, monitor and analyse collisions, near misses and harsh driving events in seconds, without human intervention, supports data-driven safety decision-making and problem-solving. Using insight into driver behaviour, fleets can achieve proactive risk intervention that makes it possible to improve driver performance, reduce collisions and most importantly save lives.

Richard Kent President of Global Sales, VisionTrack