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Turn on Safe driving assessment in your mobile app

Apply advanced analytics to your drivers by adding the Telematics SDK to your mobile application. We provide a powerful scoring model that boasts the highest predictive value. It allows you to determine the segment of the most responsible drivers who have lower road accidents and those who have irresponsible driving and increase the risk of road accidents of the least responsible ones. What’s more, those drivers who use telematics are reported to be 47% less loss-making than other drivers.

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driving behavior

Select those parameters that are important to your business and geography

Driving factors

Scoring model factors

Common factors
  • Distracted driving/ Phone usage
  • Rapid acceleration
  • Harsh braking
  • Aggressive cornering
  • Speeding
Additional factors
  • Weather conditions
  • Road conditions
  • Driving area
  • Annual mileage
  • Trip duration
  • Time of driving
  • Other...

telematics technology

driving data

united states

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auto insurance

premium paying

attention posted

speed limits

telematics device

telematics program

insurance rating

text while driving

pay as you drive

motor vehicle

crashes risky

driving behavior

eyes off the road

driver distraction

seat belts

time of day

safe-driving tips

insurance telematics

Driving behavior analysis provides powerful insights into existing driver segments

UBI telematics

Example of driver patterns

  • Rare phone usage
  • A small number of driving incidents
  • Compliance with speed limits
  • Rare use of the vehicle during rush hours
  • Minor speed violation
  • A small number of driving incidents
  • Relatively high annual mileage
  • Driving during night hours
  • Aggressive and “digital” driving style
  • Speeding during rain
  • Phone distraction while speeding at night

Safe drivers have almost 5 times fewer road accidents than an average driver. Even among young drivers.

The way how you drive reflects on the risk of accident frequency. In accordance with the latest research, driving behaviors are manageable. Regular and proper interactions with drivers can improve driver behavior and reduce the risk of accidents. But, not all interactions lead to the same result. Insurance companies and driving schools can use this knowledge to provide drivers with a better experience and new products as UBI (Usage-based insurance) and digital driving coach.

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Behavior modification through nudges
Telematics for young drivers

The product supports different periods of observation - from one month to a constant monitoring

Safe scorecard observation period
  • From the risk assessment point of view, one month is a sufficient period to collect the necessary amount of information and reach steady results in risk driving assessment.
  • The existing product solutions are based on different periods of observation of a client: from one month to a constant period of monitoring.
  • Our analysis shows that approximately in 1 month it is possible to determine a driving style of a client even taking into account his possible intentional updating of the driving style.
  • However, for a more precise determination of the annual mileage of a car, which required a significantly longer period, the stabilization of scoring model sets is 3-4 months.
  • However, driving style changes over time. This can be influenced by a change in social status, location, work, and other factors. So, we recommend our clients to keep monitoring active from time to time
  • From the point of view of obtaining the necessary information, a 3-month monitoring period is sufficient for obtaining steady results.


motor vehicle crashes

auto insurance

premium paying

attention posted

speed limits

drunk driving

What is the information used to calculate drive scorecard

Speeding

  • At each driving moment, Telematics SDK collects the user's speed
  • This data is compared with the existing speed limits provided by our partners (Location platforms: HERE, Google, etc.)
  • We detect in which points there was speeding and then combine detected time intervals into events with a minimum duration of 3 seconds and maximum gap of 5 seconds between them

Distracted driving

  • Phone usage is detected by ML-model
  • We detect at which points there was phone usage and then combine detected time intervals into events with a minimum duration of 3 seconds and a maximum gap of 20

Acceleration

  • Our algorithms continuously adapt phone axes to calculate correct accelerations
  • The resulting accelerations in the direction of movement are transformed into events if the driver has accelerated >3 m/s2.
  • We detect at which points there was acceleration and then combine detected time intervals into events with a minimum duration of 0.6 seconds and a maximum gap of 3 seconds between them

Braking

  • Our algorithms continuously adapt phone axes to calculate correct decelerations
  • The resulting decelerations in the direction of travel are transformed into events if the driver has slowed down >3.2 m/s2.
  • We detect at which points there was deceleration and than combine detected time intervals into events with a minimum duration of 0.6 seconds and maximum gap of 3 seconds between them

Cornering

  • Our algorithms continuously adapt phone axes to calculate correct accelerations
  • The resulting accelerations in the direction of motion are transformed into events if the perpendicular acceleration to the direction of motion >4.2 m/s2.
  • We detect at which points there was acceleration and then combine detected time intervals into events with a minimum duration of 0.6 seconds and a maximum gap of 3 seconds between them

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Damoov
Telematics infrastructure for tracking and safe-driving mobile applications
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