Digital Safety Services: Ecology and Fleet Under Control

Digitalizing Safety: From Fleet Control to Predictive Analytics

Transport risk management traditionally remains one of the most complex tasks for HSE departments. The probability of severe traffic accidents in large corporate fleets is statistically high, and the consequences range from reputational losses to complete enterprise bankruptcy. During the webinar, Yuri Visnevsky, founder of Sky (an MTS ecosystem company), explains how the transition from passive monitoring to active systems using artificial intelligence is changing the approach to road safety.

The Accident Pyramid and Driver Scoring

Using corporate fleets as an example, the speaker shows that reducing the number of severe accidents is impossible without addressing the base of the "accident pyramid" — minor violations and dangerous driving behaviors. To solve this problem, a telematics and personal scoring system is implemented.

Driving style assessment is based on data regarding speeding, harsh braking, and lane changes. An important element of the system is the sound alert (buzzer) in the cabin: the driver receives a signal at the moment of the violation, allowing them to correct their behavior "here and now." However, technical control works effectively only in conjunction with management decisions. The presentation details the mechanism of corrective feedback, where violation data is transmitted to line managers for discussions and integration into the personnel motivation system.

AI Video Analytics: Real-Time Accident Prevention

The next stage in the development of safety systems is smart dashcams with onboard neural network analytics. Unlike classic cameras used only to analyze incidents that have already occurred, AI video analytics works proactively.

• Driver condition control: the system recognizes signs of falling asleep, distraction by a smartphone, or smoking.
• Road situation monitoring: the camera records dangerous proximity, lane violations, or the risk of hitting a pedestrian.
• Instant response: upon detecting a critical risk (e.g., microsleep), the system wakes the driver with a voice warning and immediately sends a video clip to the dispatcher to take action.

The practical cases presented in the report demonstrate a multiple reduction in the frequency of dangerous actions and the prevention of hundreds of potential accidents thanks to timely automatic intervention.

Analytics Automation and the Role of Eco-Monitoring

Collecting a massive array of data requires adequate processing tools. The speaker analyzes the dispatching outsourcing model, where data aggregation from different systems is consolidated into unified dashboards. To reduce the burden on managers, AI-based Telegram bots are used to automatically generate personalized reports and recommendations for drivers.

In the second part of the webinar (presented by Olesya Sukhanos, Head of Eco-Monitoring), the focus shifts to digital solutions for environmental safety, highlighting a comprehensive approach to managing operational risks.

What you will learn from this webinar:

• How to justify fleet safety investments to top management using real financial and reputational risks as examples?
• How does the two-level driver influence system work (buzzer + feedback from the manager)?
• What is the fundamental difference between AI video analytics and standard GPS monitoring?
• How to automate routine work with problem drivers using neural networks and chatbots?
• What results does the implementation of predictive safety systems yield, using large logistics and industrial companies as examples?