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Technology

We are adopting advanced machine learning (ML) in order to scrutinize driver's subconscious behavioural patterns manifested both outside and inside the vehicle. By discovering unique trends, uncovering involuntary reactions and examining aware cognitive processes, we can accomplish unattainable otherwise standards of accident avoidance (94% effectiveness). Our methodology was both verified during scientific research and validated in relevant environment.

Methodology of prediction

Outside of the vehicle, TCR App monitors user’s everyday life [1] and (on the basis of machine learning technology) transforms registered data into personalized model of his/her susceptibility to abrupt attention falls under the plethora of possible future circumstances. After entering the vehicle, TCR App analyzes multiple parameters associated both with driver's aware decisions and with subconscious reactions manifested by his/her organism [2]. By applying advanced AI, our system can subsequently combine the knowledge obtained beforehand with anticipated progress of the further journey.


As a consequence, we know what are the underlying circumstances of a given journey. We understand in what way the driver's organism behaves when the abrupt attention fall is approaching. Hence, we are capable of predicting the future thanks to our trailblazing algorithms. And as we are aware of what will happen, we can alter it through TCR App’s personalized sound alerts designed to directly impact the driver’s very own brain exactly when it is needed - what ensures that his/her level of attention will never drop to dangerously low magnitudes, ultimately attaining 94% effectiveness within less than 3 months.


[1] It encompasses variety of key information about user’s background: e.g. sleep time/quality, level of health/fitness, work/leisure environment, physical activity, nutrition, triggering external factors, etc.

[2] It is reflected in vehicle trajectory fluctuations, acceleration changes, biometrical parameters and behavioural patterns. Every quantity in both models ([1] and [2]) is being measured fully automatically.

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