SafetyNex App may reduce road accident rate by 20%



Onboard telematics now can measure behavior of a driver, and therefore, car insurers

have early started this adventure of connected car, more or less successfully.


The simplest applications that have been deployed are:

. locate stolen vehicles

. measure the usage of the driver, and in particular the number of kilometers traveled in order to propose adaptive pricing (vehicle

  that always stays in a garage will never have an accident!)


But the main business of the insurer deals with the concept of risk, and then, we have seen a lot of telematics firms proposing automatic detection

of risky behaviors.

The most common is the so-called detection of "severe braking," which is based on the assumption that severe braking

reveals a lack of anticipation, and thereby a dangerous driving.

We now know that this assumption is totally false [1], but it is still in the mind of some insurers that « want to believe » there is a simple way to
classify human beings behaviours.

However, the lack of results of these deployments has led some German and US insurers to abandon Telematics [2].


The company NEXYAD has demonstrated that it is possible to « measure » in real time the risk of driving, and this stimulus now  keen interest in telematics
among insurers worldwide. This interest was even increased as NEXYAD won the BMW Tech date challenge with their onboard risk assessment App SafetyNex [3]

SafetyNex works where all other systems fail, simply because the problem was treated in a completely new way, without any « science fashion » consideration,
especially about the deep learning (or machine learning).


Indeed, the difficulties of developing  an application of efficient onboard risk estimation are :


. Science and facts : an accident is a rare event and inexplicable (definition :  "happens by chance", a driver has got one accident
  every 70 000 km, on average, most of which are harmless). Observing a driver during 5 years (to make sure an

  accident occured ) is long and ineffective (one accident does little to make individual statistics), and  variability factors of "road life situations" are extremely
  numerous, so it would take millions of drivers during decades before having relevant statistics.


. Ethics : driving behavior in itself has absolutely no direct link with the risk [1] (indeed we conceive  easily that drifting demos on an abandoned airport or just in front
  of a school at noon, corresponds to very different risks although the driving behavior is the same : it obviously needs to be "contextualize"). Contextualization (that is
  not present in the  "severe braking" experiments mentioned before) therefore demand to know, among other things, speed of  the vehicle, and where this speed is practiced.

  But as digital maps have all recoded the maximum authorized speed, then if you record the speed and geolocation in a cloud ... it potentially saves violations of speed limits.
  In many countries, ilcuding France, it is prohibited to record infringement to the law by non accredited organizations (like Insurance Companies). This totally disqualifies

  telematics boxes that record raw data in the cloud !

  However, some European insurers continue to test this kind of solution in the hope (bu in vain) that the "deep learning" and "data scientists" give their risk scores.

  But in any case in France (40 million vehicles market), the violation of the Penal Code is sanctioned and generally pursued by the CNIL [3 bis]. And insurance companies won’t
  have the opportonity to defend themselves saying ‘there is no choice » because SafetyNex estimates risk of driving without recording ANY confidential data !
  And it was shown that SafetyNex delivers every needed data to insurance companies (without any violation of driver’s privacy).


We can see with these two constraints that the solution of " big data statistics in the cloud using machine learning" can not be applied:

. statistics (or deep learning etc): accident is rare soi t won’t work at the individual level

. in the cloud: this is contrary to the laws that protect privacy of people.

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