Tag Archive: risk

Apr
19

Newsletter #22 is now available

NEXYAD Automotive & Transportation Newsletter #22, April 17th, 2018

 


4 disruptive AI algorithms for automotive mobility by NEXYAD


Headlines :

– SafetyNex episode 4 : Driving Risk Assessment for Automotive (Driving Assistant, ADAS, Autonomous Driving)
– CNEJITA Seminar on Artificial Intelligence: who will be responsible ?
– SafetyNex : driving robot maybe will mitigate human errors, but first they have to imitate good drivers
– “Theory of Water Flush” and Impact on the Prevention of Accidents for Autonomous Vehicles
– 4 disruptive AI algorithms for automotive mobility

Go to the Nexyad Automotive & Transportation Newsletter #22

Apr
17

4 disruptive AI algorithms for automotive mobility by NEXYAD

NEXYAD Automotive & Transportation Newsletter #22, April 17th, 2018

 


4 disruptive AI algorithms for automotive mobility by NEXYAD

Headlines :
– SafetyNex episode 4 : Driving Risk Assessment for Automotive (Driving Assistant, ADAS, Autonomous Driving)

– CNEJITA Seminar on Artificial Intelligence: who will be responsible ?

– SafetyNex : driving robot maybe will mitigate human errors, but first they have to imitate good drivers

– “Theory of Water Flush” and Impact on the Prevention of Accidents for Autonomous Vehicles

– 4 disruptive AI algorithms for automotive mobility

* * * * *



SafetyNex episode 4:
Driving Risk Assessment for Automotive
(Driving Assistant, ADAS, Autonomous Driving)

 
The new video on SafetyNex, on board driving risk assessment in real time.



* * * * *



CNEJITA Seminar on Artificial Intelligence:
who will be responsible ?

 
April 10th, CNEJITA (National Company of Legal Experts on Computer Science and Associated Techniques) organized a Seminar, whose objective is to determine the responsibility in terms of artificial intelligence through the understanding of technology and the dialogue with the actors of the sector.
It is therefore around this theme of topicality and future which is the artificial intelligence that the best experts in terms of computing met at the Commercial Court of Paris.

AI: concepts, technological breakthroughs and new risks
– Understanding the Concepts and Landscape of AI – Jean-Claude HEUDIN (Artificial-Creature.com – Teacher Researcher in AI)
– IA: state of play and perspectives – Jean-Philippe DESBIOLLES (IBM head of France IA WATSON)

Roundtable – Which Expertise fo AI ? was animated by Serge MIGAYRON (Honorary President of CNEJITA)
– The acceptability and limits of IA – JA CAUSSE (CNEJITA Expert)
– The Autonomous Vehicle and Traceability of IA – Jean-Louis LEQUEUX (Former President of VeDeCoM Tech)
– Auditability and risk control in the design of an IA – Gérard YAHIAOUI (NEXYAD)
– Evolution of the world of insurance, towards an objective responsibility – Nicolas HELENON (Co-manager Firm NEO TECH Assurances)

Roundtable – The Legal Challenges of AI. Animation – A MEILLASSOUX (ATM Lawyers – President of AFDIT)

– Introduction to Classical and New AI Concepts by Law: Applicable Regime and Evidence – L SZUSKIN (BAKER McKENZIE Lawyer)
– Tort liability in the face of AI: adaptation of traditional categories or creation of a responsibility specific to AI? – P GLASER (Lawyer TAYLOR WESSING)
– Contractual liability in the face of the IA: risk management during the contractualization of an IA system – FP LANI (DERRIENNIC Associate Lawyer)
– Synthesis on the current legal landscape – G de MONTEYNARD (Attorney General at the Court of Cassation)

Colloque CNEJITA
Gérard YAHIAOUI, CEO of NEXYAD

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SafetyNex : driving robot maybe will mitigate human errors,
but first they have to imitate good drivers

 
BEWARE with the statistics : “94% of severe personal damage accidents are due to human errors” doesn’t mean that you’ll save 94% of severe accident with autonomous driving : drivers do not only make mistakes they also drive well (1 accident every 70 000 km, 3 dead every billion km – OCDE) … It is important to study also good driving and near misses (when driver has the right behaviour to avoid accident or to mitigate severity)… That’s what NEXYAD did during 15 years of research programs on road safety ^^ (that led to SafetyNex). See image (if you do not provide the “green” features, you will lose lives more than you gain with your driverless car. Our AI algorithm SafetyNex was made for this.

Example of risk tree

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“Theory of Water Flush” and Impact on the Prevention of Accidents for Autonomous Vehicles

 
“THEORY OF WATER FLUSH” AND IMPACT ON THE PREVENTION OF ACCIDENTS
FOR AUTONOMOUS VEHICLES

by NEXYAD

INTRODUCTION
Let’s suppose that the flush does not exist in our toilets, and then let’s suppose that engineers able to create complex systems or even “systems of systems” are consulted to invent it, and that they apply exactly the same method than they do in the field of ADAS and Autonomous Vehicles.

Water Flush Vs Automotive Engineers

METHOD OF SCENARIOS
We propose to apply the method of scenarios, which consists in crossing all the factors that can modify the situation, then in each case of the complete combination, propose a solution. For this, it is necessary to note the number of possible shapes for the tank, the possible volumes, all the possible locations for the water supply entry, the possible diameters of the inlet pipe, the flow rates and possible pressures of water, the possible residual water levels before filling. We can generate the combinatorial of these factors, which allows us to generate all the possible scenarios of the “flush” problem. In each case, it is possible to give a solution, namely, the duration of filling of the tank (opening and closing of the water tap).

This approach is fully compatible with deep learning, which will also interpolate between two reference cases (quality of interpolation/generalization to be controlled, of course) if characteristics had to drift over time. Of course, the tank must integrate a system of sensors to evaluate the configuration (diameter of pipe, pressure of water, position pipe, capacity of the tank, etc …). We can use a camera, lasers, ultrasounds, etc. So that this recognition of situation is as accurate as possible. For such an approach, automation/control engineers talk about open-loop (feed forward) control because the data flow is as follows:

Open Loop

COST AND ROBUSTNESS OF THE SCENARIOS METHOD
It is easy to understand that the flush thus designed will be perfectly functional (there is no reason for it does not work), but for a high cost due to the sensors to integrate. Similarly, the robustness of the system to a measurement error or to a bad situation recognition is not guaranteed : we can very good to fill too much or not enough. The accuracy of the configuration case recognition is very important.


SOLUTION OF WATER FLUSH IN THE REAL WORLD
If you have the curiosity to disassemble your flush, you will notice that it is much simpler than the system described above: A float indicates when the water supply valve should be closed. The figure is as follows:

Closed Loop

Automation engineers call this a closed loop control (servo control). The feed forward “open” control is reduced to “open the tap thoroughly without worrying about the flow of water, the volume of the tank, and turn off the tap as soon as the float asks for it “. Note that this method works regardless of the configuration of the flush : we do not even need to know the volume of the tank that can be modified (for example: by filling half of the tank with glass beads) without affecting the operation of the flush. It is a robust and cheap system.


TRANSCRIPT OF THESE REMARKS IN THE FIELD OF ADAS AND AUTONOMOUS VEHICLES:
SERVO CONTROL IN DECISION

The information processing chain of the autonomous vehicle follows the general feed forward form :
NEXYAD has developed the SafetyNex system which dynamically estimates in real time the risk that the driver (human or artificial) takes. However, the autonomous vehicle may be functionally specified as follows:

AD feeding

“transport someone from point A to point B as quickly as possible, and safely.”

The “quickly” aspect is the historical business of the automobile. The “safely” notion integrates intrinsic safety of the system (its dependability: it should not explode, sensors or power supply may not be disabled, etc.), and since it is a vehicle, its ability to move with a good road safety, that is to say by “not taking too much risk in driving”. Since SafetyNex estimates this driving risk dynamically and in real time, it can be said that SafetyNex is a dynamic indicator of “SOTIF” (Safety Of The Intended Function). SafetyNex acts as a “driving risk float” : when the risk arrives at the maximum accepted level (like the float of the flush) we stop the action that raised the risk (example: we stop accelerating or we slow down). Thus, the response of an autonomous driving system is made adaptive (at the decision level) : even if the feed forward open loop is not perfect, it can correct itself to take into account, among other things, the instruction and the measure of driving risk. This system is completely independent of the automatic driving system in terms of information processing, so it represents redundancy of processing.

SafetyNex uses to estimate risk :

. risk due to inadequacy of driving behaviour to the difficulties of the infrastructure : navigation map, GPS, accelerometers

. risk due to inadequacy of driving behaviour to the presence of other road users (cars, pedestrians, …) : data extracted from the sensors (camera, lidar, radar, etc) such as “time to collision”, “inter distance (in seconds)”, number of vulnerables around, etc.

. risk due to inadequacy of driving behaviour to weather conditions: in particular to atmospheric visibility (fog, rain, snow, sand, penumbra). Knowing that when visibility is low, vehicle must pay more attention (and slow down) even if this autonomous vehicle is not impacted by the decrease in visibility (if it only uses a lidar for example) because the avoidance of an accident is done at the same time by the two protagonists : if one of them (pedestrian, human driver), does not see the autonomous vehicle, then it finds itself only to be able to avoid the accident, which doubles the probabilities of a potential accident.

. other

The use of SafetyNex allows to make adaptive an artificial intelligence of autonomous driving, on the following diagram :

Adapt Closed loop AI

If you have a lean computer, then you only apply one loop between t and (t+1) as it is shown on the figure. If you have a powerful computer, you can then even simulate a big number of decisions and take the less risky one (like automaticians do with predictive control systems). Of course, SafetyNex is only ONE way to close the loop (on a crucial notion : driving risk). This figure may be extanded to other variables of contol that make sense for an autonomous vehicle. More complex adaptation rules may switch from a decision to another if risk simulation shows that finally it is less risky (ex : slow down or turn wheel ?).


CONCLUSION
SafetyNex uses the map in addition to sensors (same sensors as the driving system or parallel tracks) and does not need to accurately identify the situation but instead to estimate a risk (this is a different task). SafetyNex is a knowledge-based AI system (knowledge extracted from human experts in road safety, from 19 countries – Europe Japan USA – who validated the system over 50 million km. Total research program duration : 15 years). This technology is still being improved, of course, but it can already be integrated into autonomous vehicles and avoid a large number of accidents by its ability to make the system adaptive to unknown situations. In particular, in the case of autonomous urban vehicles (autonomous shuttles, robot taxis), the adaptation of driving behaviour to complexity of infrastructure is made possible by SafetyNex, which decodes this complexity by reading the navigation map in front of the vehicle. SafetyNex makes the autonomous vehicle anticipate more by following “rules of safety” : with SafetyNex emergency situations (that still will need emergency braking and other emergency actions) become much more rare. Autonomous vehicle acts like an experienced cautious driver. Note : if you modulate Maximum Accepted Risk, then you modulate aggressiveness of the autonomous vehicle. This might make sense not to let the autonomous vehicle trapped in complex human driving situations (where the autonomous vehicle would stopped indefinitly).

* * * * *



4 disruptive AI algorithms for automotive mobility

 

. ObstaNex detects obstacles with a simple cam (a la Mobileye).
What is disruptive ?
ObstaNex runs in real time on a regular smartphone… it means it doesn’t need a big computing power to run. It can be trained/re trained on a “small database” using the methodology A.G.E.N.D.A. (Approche Générale des Etudes Neuronales pour le Développement d’Applications or General Approach of Neuronal Studies for Application Development) – important is you improve your cam !
 



. RoadNex detects drivable part of the lane borders and free space.
What is disruptive ?
RoadNex works even in the Streets of old cities as Paris, London or Roma, and it runs in real time on a regular smartphone. it means it doesn’t need a big computing power to run.
 



. VisiNex detects lacks of visibility (fog, heavy rain, snow, sand storm …).
What is disruptive ?
VisiNex is an artificial vision tool which is correlated with human perception. If there is something to see, VisiNex is able to give a score of visibility. Except Daimler, we haven’t seen such a military background-based detection elsewhere.



. SafetyNex is the only fusion Artificial Intelligence algorithm (sensor + map fusion) that estimates driving risk dynamically and in real time.
What is disruptive?
SafetyNex allows to have an explicit value of driving risk. It is a total revolution for car insurers, fleet managers, and autonomous driving engineers. These algorithms are already under integration into products for telematics /connected car, ADAS, Autonomous Vehicle.


Apr
11

CNEJITA Seminar on Artificial Intelligence: who will be responsible ?

April 10th, CNEJITA (National Company of Legal Experts on Computer Science and Associated Techniques) organized a Seminar, whose objective is to determine the responsibility in terms of artificial intelligence through the understanding of technology and the dialogue with the actors of the sector.
It is therefore around this theme of topicality and future which is the artificial intelligence that the best experts in terms of computing met at the Commercial Court of Paris.

AI: concepts, technological breakthroughs and new risks
– Understanding the Concepts and Landscape of AI – Jean-Claude HEUDIN (Artificial-Creature.com – Teacher Researcher in AI)
– IA: state of play and perspectives – Jean-Philippe DESBIOLLES (IBM head of France IA WATSON)

Roundtable – Which Expertise fo AI ? was animated by Serge MIGAYRON (Honorary President of CNEJITA)
– The acceptability and limits of IA – JA CAUSSE (CNEJITA Expert)
– The Autonomous Vehicle and Traceability of IA – Jean-Louis LEQUEUX (Former President of VeDeCoM Tech)
– Auditability and risk control in the design of an IA – Gérard YAHIAOUI (NEXYAD)
– Evolution of the world of insurance, towards an objective responsibility – Nicolas HELENON (Co-manager Firm NEO TECH Assurances)

Roundtable – The Legal Challenges of AI. Animation – A MEILLASSOUX (ATM Lawyers – President of AFDIT)

– Introduction to Classical and New AI Concepts by Law: Applicable Regime and Evidence – L SZUSKIN (BAKER McKENZIE Lawyer)
– Tort liability in the face of AI: adaptation of traditional categories or creation of a responsibility specific to AI? – P GLASER (Lawyer TAYLOR WESSING)
– Contractual liability in the face of the IA: risk management during the contractualization of an IA system – FP LANI (DERRIENNIC Associate Lawyer)
– Synthesis on the current legal landscape – G de MONTEYNARD (Attorney General at the Court of Cassation)

Colloque CNEJITA
Gérard YAHIAOUI, CEO of NEXYAD

Sep
27

Newsletter #18 is now available

Nexyad at the forefront
of ADAS for road safety

Headlines :

– Road accident and driving risk are two different notions
– Nexyad at AutoSens Brussels 2017
– Enova Symposium Paris 2017
– Connected & Self-driving Car Meetup #9
– Academic chair at MOV’EO with INSA Rouen
– Sensor fusion and data fusion with SafetyNex
– SafetyNex : Understanding the Concept of Risk
– NEXYAD : the story
– Navigation Based ADAS : use SafetyNex to build ACC and anticipation (predictive) brake systems

Go to the Nexyad Automotive & Transportation Newsletter #18

Sep
26

Nexyad at the forefront
of ADAS for road safety

NEXYAD Automotive & Transportation Newsletter #18, September 26th, 2017

 


Nexyad at the forefront of ADAS for road safety

Headlines :

– Road accident and driving risk are two different notions

– Nexyad at AutoSens Brussels 2017

– Enova Symposium Paris 2017

– Connected & Self-driving Car Meetup #9

– Academic chair : effective collaboration between MOV’EO groupement ADAS and INSA Rouen

– Sensor fusion and data fusion with SafetyNex

– SafetyNex : Understanding the Concept of Risk

– NEXYAD : the story

– Navigation Based ADAS : use SafetyNex to build ACC and anticipation (predictive) brake systems

* * * * *



Road accident and driving risk are two different notions

A lot of professionals that must cope with road safety observe accident through statistics : it seems to be normal to think that safety is low where there are a lot of accidents and that safety is high where there are few accidents.
This reality tends to make people confuse the two notions : risk and accident.
And since you stay at the statistic level, then it works : if 99% of people that played russian roulette more than 50 times have died (accident), then you can say that russian roulette is risky (risk).
Insurance companies, fleet managers, have taken into account those statistics, in order to estimate their future costs, and compute their pricing.
But now, digital connected devices are available at the very individual and local level : telematics (professional devices installed into cars), smartphones, connected car, can estimate the driving behaviour in real time and they know exactly where you drive.
Then, what this new technology brings to risk assessment ? and can you still apply at the individual level the ideas that was set at a population (statistics) level ?
That question was studied in 1969 by an American University Professor that was also a researcher for the company “Insurance of North America”, Frank E. BIRD, and a key notion was then used : the “incident” or “near miss accident” or “quasi accident”. It was shown that the risk you take does not lead to accident but to “quasi-accident”. Indeed, even in very risky situations, accident can be avoided most of the time at the very last second ! Frank E. BIRD worked on what was called “The Triangle of Risk” or “Safety Triangle”
Sometimes, you do not have luck … and then you have an accident instead of having a quasi-accident.

Pyramid of Risk Example of Statitical Relationship in Risk Assessment : from Behaviour to Fatalities


Then accident is the confluence of “risk you take” and “bad luck”. It is interesting to notice that, if you do not study the individual and local (in space and time) level, so if you consider a large population of drivers during a long duration, then “bad luck” automatically disappears… and so risk can be measured by observation of accident. But at the individual and local levels, risk cannot be measure by observing accident.
It is interesting to read about Safety Triangle and then have a clear idea of links between RISK, ACCIDENT, SEVERE PERSONAL INJURIES.
In road Safety concerns, researchers and experts have been working during 50 years on this concept of quasi-accident and they accumulated data and knowledge about this key notion. Let us resume the russian roulette comparison : pulling the trigger is the quasi-accident … and sometimes you die (accident). But even before playing such a “game” you KNOW that it is risky.
The knowledge of risk is represented by a collection of cause-effect relationships.
There is a knowledge-based artificial intelligence system that gathered the knowledge from road safety experts and researchers (that work mainly on road infrastructure) and that is now available in order to assess driving risk in real time : SafetyNex developed by the company NEXYAD. SafetyNex is the “thermometer” of driving risk and it alerts the driver BEFORE the dangerous situation, letting time to slow down and then potentially to avoid accident, to reduce severity (less personal severe injuries), …
Obviously, markets are :
. car insurance (prevention, severity, UBI)
. fleet management (prevention, reduction of costs, fit in regulations and laws)
But even the automotive can take benefit of suche a real time driving risk assessment module :
. intelligent navigation with risk vocal alerts
. automatic triggering of braking for ADAS (if risk too high then slow down)
. driverless cars : giving to the artificial intelligence that drives the car the perception of the risk it takes…
SafetyNex opens the door to a new generation of onboard applications for every field of automotive sector that is concerned with risk and safety.
Read more

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Nexyad at AutoSens Brussels 2017

AutoSens took place at the AutoWorld Museum in Brussels September 19-21.
To answer the issues of Connected Cars and Autonomous Cars, engineers need first to give eyes, ears and A.I. to future vehicles. Sensors will play this crucial and difficult role of replacing the human senses.
Engineers and sensors providers met for three days of conferences and workshops.
Groupement ADAS was present with Leddartech the lidars canadian company that rose 100 M$ funding, New Imaging Technologies with their unique high dynamic range camera sensors, Intempora that provide famous RT-Maps, and of course Nexyad presented his three camera-based software modules for Road Detection – RoadNex, for Obstacles Detection – ObstaNex, for Visibility Measurement – VisiNex and SafetyNex – the Road Safety system with sensors fusion and data fusion (digital map, accelerometers, GPS, cameras, lidars, radars, ultrasounds, weather data, traffic data, etc.).
New players appeared as Crowdflower or Mighty Ai, they are plateforms that help you process your data or images very quickly by dividing the workload with very many people registered online.
Autosens

* * * * *



Enova Symposium Paris 2017

Round Table at ENOVA symposium in Paris, on the subject of self-driving car
(see from left to right)
. Vincent ABADIE, Vice-President Expert Leader Autonomous Vehicle and ADAS, PSA Group
. Jochen LANGHEIM, Vice-President advanced systems R&D programs, ST MICROELECTRONICS
. Jean-François SENCERIN, Autonomous Driving NFI/PFA Program Director
. Gérard YAHIAOUI CEO, NEXYAD
. Guillaume DEVAUCHELLE, Vice-President Innovation, VALEO
. Alain PIPERNO, Expert Safety & Autonomous Vehicle, UTAC
The audience could listen to this experts panel and ask questions about connected and driverless cars.
Journalist : Laurent MEILLAUD
ENOVA, Paris, Porte de Versailles

Enova

3 days of exchanges and conviviality at the service of Innovation + Business Meetings driven by THE NEW NEEDS OF CONNECTIVITY.
Read more about the Event

* * * * *



Connected & Self-driving Car Meetup #9

Nexyad was invited to the Connected & Self-driving Car Meetup #9 at Le Square (Renault’s innovation lab in Paris), on september 13.
Thanks to the perfect organisation of Laurent Dunys and Bruno Moncorge.
A large audience listened presentation about vehicles and data security with Nabil Bouzerna of IRT SystemX. Finally, Jean-François Menier, lawyer at Elyos Avocats gave a very interesting wrap-up about the potential responsibility of a driver in the case of a connected / self-driving car accident and of course about driver and passengers safety with SafetyNex App : real time driving risk assessment.

Meetup

* * * * *



Academic chair : effective collaboration between MOV’EO groupement ADAS and INSA Rouen

MOV’EO Groupement ADAS built an academic chair with INSA ROUEN (option Intelligent Transportation) on ADAS and driverless cars.
The first course was given by Gérard YAHIAOUI, CEO of NEXYAD, the 13th of September 2017 in Rouen : presentation of key notions (near missed accident, driving risk), and presentation of SafetyNex (real time driving risk assessment) and applications to car insurance, fleet management, ADAS, and driverless cars.

Chaire pédagogique

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Sensor fusion and data fusion with SafetyNex

SafetyNex is a real time driving risk assessment system. Of course, Driving Risk makes everyone think of car insurance and fleet management. And it is a natural application (deployment has already started). But it is important to note that Driving Risk is also a key notion for ADAS and Driverless car.

SafetyNex Data Fusion System

Indeed, Driving Risk happens when there is no adequation between Driving Behaviour and Driving Context. ADAS and Driverless act on Driving Behaviour :
. ADAS modifies Driving Behaviour : braking when the human driver did not, etc …
. Driverless car creates Driving Behaviour : there is still a driver called “artificial intelligence”.

Driving context is measured :
. Map Electronic Horizon
. GPS
. Accelerometers
. Times to collision (front and rear)
. Number of vulnerables around (even on sidewalks)
. Atmospheric visibility / weather condition (fog, pouring rain, etc.)
. X2Car Data Streams (accident, weather alert, construction area, etc.

So you can now imagine that if you have the opportunity to ESTIMATE adequation between Driving Behaviour and Driving Context, then you can build much more relevant ADAS and Driverless Artificial Intelligence (adequation or inadequation).

You may notice that Driving Context is measured through heterogenous sensors and data streams. It brings no difficulty to SafetyNex that uses Fuzzy Sets and Possibility Theory to estimate adequation, givin a Driving output called Driving Risk (that you should want to minimize under constraints of mobility efficiency).

Then SafetyNex is actually a sensor and data fusion system (high level fusion), much more efficient than every fusion systems that you ever developed, because it generates a variable (Driving Risk) that is a KEY NOTION for driving and is EASY TO UNDERSTAND AND USE.

NEXYAD implemented a low cost version with only the first 3 inputs (more than 5,000 road safety rules to cope with the infrastructure dangers …) and is now implementing simple rules to take into account mobile context. Example: “the shorter the time to collision, the higher the risk”. And that’s it ! The knowledge based artificial intelligence of SafetyNex automatically does the fusion with the 5,000 rules. There is no need to “weight” the rules, as possibility theroy allows a fusion with every rule competing with the others … Elegant applied maths to a problem that most engineers describe in a so complicated way that it becomes impossible to solve.

We really encourage ADAS and Driverless engineers to come to us and simply integrates SafetyNex (low CPU consumption, easy real time, etc.) and then get NOW a proven sensor fusion and data fusion system that works. This gives ONE dimension of Driving Systemic Analysis items: Driving Risk, in real time.

Of course, if you do the systemic analysis you will find other dimensions of interest (we let you do that, we’ve done it for ourselves, trace of the military research past of NEXYAD founders).

SafetyNex is now under implementation by big ADAS OEM companies. Series deployment will start in 2018. We will be glad to help you being a part of it.

* * * * *



SafetyNex : Understanding the Concept of Risk

SafetyNex App is a real time driving risk assessment. We present below 3 videos to explain as simply as possible the concept of driving risk.

Luck doesn’t change the risk that the driver takes. It means that risk taken by the « lucky risky driver » is exactly the same than risk taken by the « unlucky risky driver ». It is possible then to detect risky drivers before they have accident (anticipation of costs). Once detected, it is possible to train them (prevention program).

Because SafetyNex driving risk assessment is done in real time, it is possible to alert the driver (when risk is higher than an acceptable value), and if driver slows down, then risk never rises at the red level. It is an onboard prevention system (ADAS).

Observation of accidents on a short period of time (3 months for instance) may not show any difference between « cautious driver » and « lucky risky driver » (both of them may not have accident). It is a big problem for UBI, and SafetyNex brings the solution as it anticipates accident (sooner or later the « lucky risky driver » will have a severe accident).

The Risky & Lucky Driver



The Risky & Unlucky Driver



The Cautious Driver



More info : http://www.safetynex.net

* * * * *



NEXYAD : the story

All you have to know about the french High Tech company.

NEXYAD is a “fake startup” (much older than it seems ^^) set up in 1995 by maths research engineers (Pierre DA SILVA DIAS & Gérard YAHIAOUI + other shareholder : researchers, finance executives, engineers). Founders came from military research (anti-tank missiles), with a strong culture of computer vision, machine Learning, artificial intelligence, signal theoy and processing, stats and data analysis.
During years we were the extended maths team of famous big firms in many fields : defence, automotive, banking, insurance, energy, cosmetics, agri-food, glass, railways, … Customers were research departments first, and then product and marketing departments, sales departments, industrial labs, manufactures, actuaries, etc.
In the automotive sector, we’ve been involved in a lot of different high level maths works such as :
. production/manufacture (work for PSA Group, Charleville),
. industrial Lab (work for PSA Group – Belchamp,TOYOTA Europe – Brussels, Robert BOSCH GmbH, Bühlertal, VALEO, La verrière – SAINT GOBAIN, … etc.)
. research & development for PSA Group, RENAULT, NISSAN, IEE, DAIMLER, FAURECIA, LE LAB (PSA RENAULT), on perception by camera, radar, capacitive sensors, ultrasound, infrared, etc … , vehicle dynamics (active control), sensorial analysis (touch smell perception of texture by vision), human factor analysis, detection of passengers on seats, odor gas sensors, artificial intelligence, epidemiologic analysis (pollution, particles, …), etc.

Read the entire article.

* * * * *



Navigation Based ADAS : use SafetyNex to build ACC and anticipation (predictive) brake systems

Nexyad SafetyNex

Imagine a robotized car that would slow down automatically when approaching a tiny curve, or an intersection or a priority, of a stop sign, etc … if needed (i.e. if and only if the current speed and acceleration of the car is not appropriate to the driving context). Sounds interesting ?
It would be then “smooth anticipation braking” (from 0.1 to 0.3 g) instead of “emergency braking” (so easier to do and not that disturbing for driver and passengers comfort in the car). Doing this, the car dramatically decreases the probability to be kept in a dangerous situation and it let much more margin to emergency brake if still needed.
Finally, it would mean that the car follows road traffic code plus safety rules (anticipation).
This is easy to achieve using NEXYAD real time driving risk assessment module SafetyNex : SafetyNex reads “Electronic Horizon” (reading POIs and decoding shape and dimensions of the infrastructure ahead), “GPS“, “accelerometers“, and can accept additional inputs such as “time to collision“, “size of free space“, “position in the lane“, “atmospheric visibility“, alert data streams (weather, accident, traffic, …). All those heterogenous data are used (data fusion) to estimate driving risk in real time : Driving Risk (t)
Then everytime that Driving Risk(t) comes higher than an acceptable threshold value, the robotized car slightly slows down … and that’s it !
SafetyNex is the result of 15 years of collaborative research and it works.
Markets : Car insurance and fleet managers (for real time alert and risk profiles recording), ADAS (for automatic predictive/anticipation brake), and Driverless car (Automated car that follows Road Traffic Code).
SafetyNex is Under deployment, please feel free to try it and put it into your own products (available as an API).
Keywords : Adaptive Cruise Control, ACC, Intelligent ACC, Intelligent Cruise Control, navigation-based, navigation-based ADAS, NB ADAS, ADAS, Advances Driver Assistance Systems, Anticipation brake, Predictive Brake, SafetyNex, Risk, Driving Risk, Real time driving risk assessment, road traffic code, SafetyNex, electronic horizon, GPS, accelerometers, time to collision, free space, size of free space, position in the lane, lane departure, visibility, atmospheric visibility, data stream, weather, accident, traffic, data fusion,…

Sep
25

Road accident and driving risk are two different notions

A lot of professionals that must cope with road safety observe accident through statistics : it seems to be normal to think that safety is low where there are a lot of accidents and that safety is high where there are few accidents.
This reality tends to make people confuse the two notions : risk and accident.
And since you stay at the statistic level, then it works : if 99% of people that played russian roulette more than 50 times have died (accident), then you can say that russian roulette is risky (risk).
Insurance companies, fleet managers, have taken into account those statistics, in order to estimate their future costs, and compute their pricing.
But now, digital connected devices are available at the very individual and local level : telematics (professional devices installed into cars), smartphones, connected car, can estimate the driving behaviour in real time and they know exactly where you drive.
Then, what this new technology brings to risk assessment ? and can you still apply at the individual level the ideas that was set at a population (statistics) level ?
That question was studied in 1969 by an American University Professor that was also a researcher for the company “Insurance of North America”, Frank E. BIRD, and a key notion was then used : the “incident” or “near miss accident” or “quasi accident”. It was shown that the risk you take does not lead to accident but to “quasi-accident”. Indeed, even in very risky situations, accident can be avoided most of the time at the very last second ! Frank E. BIRD worked on what was called “The Triangle of Risk” or “Safety Triangle”
Sometimes, you do not have luck … and then you have an accident instead of having a quasi-accident.

Pyramid of Risk Example of Statitical Relationship in Risk Assessment : from Behaviour to Fatalities


Then accident is the confluence of “risk you take” and “bad luck”. It is interesting to notice that, if you do not study the individual and local (in space and time) level, so if you consider a large population of drivers during a long duration, then “bad luck” automatically disappears… and so risk can be measured by observation of accident. But at the individual and local levels, risk cannot be measure by observing accident.
It is interesting to read about Safety Triangle and then have a clear idea of links between RISK, ACCIDENT, SEVERE PERSONAL INJURIES.
In road Safety concerns, researchers and experts have been working during 50 years on this concept of quasi-accident and they accumulated data and knowledge about this key notion. Let us resume the russian roulette comparison : pulling the trigger is the quasi-accident … and sometimes you die (accident). But even before playing such a “game” you KNOW that it is risky.
The knowledge of risk is represented by a collection of cause-effect relationships.
There is a knowledge-based artificial intelligence system that gathered the knowledge from road safety experts and researchers (that work mainly on road infrastructure) and that is now available in order to assess driving risk in real time : SafetyNex developed by the company NEXYAD. SafetyNex is the “thermometer” of driving risk and it alerts the driver BEFORE the dangerous situation, letting time to slow down and then potentially to avoid accident, to reduce severity (less personal severe injuries), …
Obviously, markets are :
. car insurance (prevention, severity, UBI)
. fleet management (prevention, reduction of costs, fit in regulations and laws)
But even the automotive can take benefit of suche a real time driving risk assessment module :
. intelligent navigation with risk vocal alerts
. automatic triggering of braking for ADAS (if risk too high then slow down)
. driverless cars : giving to the artificial intelligence that drives the car the perception of the risk it takes…
SafetyNex opens the door to a new generation of onboard applications for every field of automotive sector that is concerned with risk and safety.
Read more

Sep
08

Sensor fusion and data fusion with SafetyNex

SafetyNex is a real time driving risk assessment system. Of course, Driving Risk makes everyone think of car insurance and fleet management. And it is a natural application (deployment has already started). But it is important to note that Driving Risk is also a key notion for ADAS and Driverless car.

SafetyNex Data Fusion System

Indeed, Driving Risk happens when there is no adequation between Driving Behaviour and Driving Context. ADAS and Driverless act on Driving Behaviour :
. ADAS modifies Driving Behaviour : braking when the human driver did not, etc …
. Driverless car creates Driving Behaviour : there is still a driver called “artificial intelligence”.

Driving context is measured :
. Map Electronic Horizon
. GPS
. Accelerometers
. Times to collision (front and rear)
. Number of vulnerables around (even on sidewalks)
. Atmospheric visibility / weather condition (fog, pouring rain, etc.)
. X2Car Data Streams (accident, weather alert, construction area, etc.

So you can now imagine that if you have the opportunity to ESTIMATE adequation between Driving Behaviour and Driving Context, then you can build much more relevant ADAS and Driverless Artificial Intelligence (adequation or inadequation).

You may notice that Driving Context is measured through heterogenous sensors and data streams. It brings no difficulty to SafetyNex that uses Fuzzy Sets and Possibility Theory to estimate adequation, givin a Driving output called Driving Risk (that you should want to minimize under constraints of mobility efficiency).

Then SafetyNex is actually a sensor and data fusion system (high level fusion), much more efficient than every fusion systems that you ever developed, because it generates a variable (Driving Risk) that is a KEY NOTION for driving and is EASY TO UNDERSTAND AND USE.

NEXYAD implemented a low cost version with only the first 3 inputs (more than 5,000 road safety rules to cope with the infrastructure dangers …) and is now implementing simple rules to take into account mobile context. Example: “the shorter the time to collision, the higher the risk”. And that’s it ! The knowledge based artificial intelligence of SafetyNex automatically does the fusion with the 5,000 rules. There is no need to “weight” the rules, as possibility theroy allows a fusion with every rule competing with the others … Elegant applied maths to a problem that most engineers describe in a so complicated way that it becomes impossible to solve.

We really encourage ADAS and Driverless engineers to come to us and simply integrates SafetyNex (low CPU consumption, easy real time, etc.) and then get NOW a proven sensor fusion and data fusion system that works. This gives ONE dimension of Driving Systemic Analysis items: Driving Risk, in real time.

Of course, if you do the systemic analysis you will find other dimensions of interest (we let you do that, we’ve done it for ourselves, trace of the military research past of NEXYAD founders).

SafetyNex is now under implementation by big ADAS OEM companies. Series deployment will start in 2018. We will be glad to help you being a part of it.

Aug
21

SafetyNex : Understanding the Concept of Risk

SafetyNex App is a real time driving risk assessment. We present below 3 videos to explain as simply as possible the concept of driving risk.

Luck doesn’t change the risk that the driver takes. It means that risk taken by the « lucky risky driver » is exactly the same than risk taken by the « unlucky risky driver ». It is possible then to detect risky drivers before they have accident (anticipation of costs). Once detected, it is possible to train them (prevention program).

Because SafetyNex driving risk assessment is done in real time, it is possible to alert the driver (when risk is higher than an acceptable value), and if driver slows down, then risk never rises at the red level. It is an onboard prevention system (ADAS).

Observation of accidents on a short period of time (3 months for instance) may not show any difference between « cautious driver » and « lucky risky driver » (both of them may not have accident). It is a big problem for UBI, and SafetyNex brings the solution as it anticipates accident (sooner or later the « lucky risky driver » will have a severe accident).

The Risky & Lucky Driver



The Risky & Unlucky Driver



The Cautious Driver



More info : http://www.safetynex.net

May
10

Interpretation of Risk Profiles with SafetyNex
A new paper by Nexyad

ESTIMATION OF DRIVER’S EXPERTISE, OF RISK TAKEN CONSCIOUSLY BY THE DRIVER,
AND OF THE LACK OF ANTICIPATION AND MISUNDERSTANDING OF THE ROAD



By NEXYAD

Lire la version Française


I – INTRODUCTION
NEXYAD has been developing the smartphonte application SafetyNex which estimates the risk of driving in real time [1]. SafetyNex is both a driver assistance system (ADAS), which alerts the driver (vocal alert) before danger (When the risk increases too much), and a telematics system that records risk profiles and usage profiles.
Warning before the danger gives the driver time to slow down and avoid the accident. Road Safety studies show that SafetyNex can reduce the number of accidents by 20% [2]. This simple functionality is of interest of car insurers, fleet managers, and to car manufacturers.
SafetyNex also rewards the driver with cups (gold, silver, bronze) that can be transformed into money incentive (vouchers, etc.) so that the safe drivers stil have a daily interest to go on using SafetyNex. Indeed, tools that are not used over a period of time rarely have a real effect on the accidentology. SafetyNex is therefore distinguished from other products, on the one hand by its real time and driving assistance, but also for its “reward” side. SafetyNex informs the driver In real time when the risk exceeds a threshold of danger, than one can say that SafetyNex gives the risk in the hands of the driver first. The driver is in control of his/her risk.
Then SafetyNex distinguishes from all telematics products that ultimately provide information to the insurer or fleet manager, but not to the driver who feels rightly spied on.
Risk and usage profiles [3] are forwarded to managers who have an interest in minimizing risk and the number of accidents. This paper presents a simple way to interpret the risk profiles constructed by SafetyNex.

II – SafetyNex RISK PROFILES
SafetyNex estimates risk of driving at every instant.
Since SafetyNex also measures usages, it measures among other things the durations and the number of traveled kilometers.
One can then construct the curve Risk (t) which is the risk at each moment, and also the curve Risk (km) which is the risk at each point of the route.

Risk Distance

Risk Distance rated

Let’s consider one or the other of those curves, it is easy then to cut the risk into slots:

Risk 03

It is therefore possible to calculate the total duration spent [resp the total number of km carried out] with a risk
between 10% and 20%, for example (or between 50% and 60%). The graph of these durations [resp number of km] for each range of risk (0% -10%, 10% -20%, etc.), looks like :

Graph 4

It can be seen that this graph can be seperated into three parts:

Graph 05

. A very high bar of near zero risk
. A shape of « bell curve » comparable to a gaussian
. Rising at the very end towards high risks
NEXYAD has run over 3,500 testers since June 2016, and has been able to interpret the shapes of these curves.

III – INTERPRETATION OF SafetyNex RISK PROFILES : CONSENTED RISK, EXPERTISE OF DRIVING, LACK OF ANTICIPATION OF DRIVER
The large quasi-zero risk bar simply expresses the fact that overall the car is a safe mode of transportation.
The part that draws a bell curve has a more or less strong spread : we have noticed that experienced drivers have a narrow curve (repeatability of their driving style is high) while beginners have a huge spread (they can’t drive always the same way).
The centering of the bell curve (maximum likelihood) corresponds to the way in which the driver takes a controlled Risk : cautious beginners have a low maximum likelihood (they try to take as less risk as they can) while experienced drivers have a higher maximum likelihood : they know what risk level thay can cope with.
Finally, the values that go up to the right (tail of distribution of the curve in bell) correspond to the vocal alerts, that is to saycases where the driver has not fully understood that the risk is high. In other words, it is the lack of anticipation and misunderstanding of road.

IV – CONCLUSION
SafetyNex’s risk profiles make it possible to understand the kind of driver you have :
. Cautious / not cautious (maximum likelihood of the bell curve position)
. Experienced / beginner (spreading of the bell curve)
. Lack of anticipation / very good anticipation (queues of distribution of the curve in bell)
Fleet insurers and managers therefore have all the information that they need to help the driver.
For example, within the framework of prevention plans, offering training adapted to each type of risky driving.
We validated this information by driving 3,500 testers, including beginners, experienced drivers, and also pilots (who in take risks appearance, but in reality have a very safe driving). This allowed us to give these interpretations of SafetyNex’s risk profiles.
With the deployment of SafetyNex to reduce the number of accidents, professionals structurally gain margin, and can use this margin to analyze profiles, segment them, and find the segments where it may be interesting to develop UBI (Usage Based Insurance) and real time pricing fleet.
This multi-functionality of SafetyNex makes it a unique and effective tool for managing driving risks.

V – REFERENCES
[1] : THE ULTIMATE SOLUTION FOR INSURANCE COMPANIES THAT NEED ONBOARD RISK ASSESSMENT : SafetyNex

[2] : SMARTPHONE APP SafetyNex COULD REDUCE ACCIDENT RATE BY 20%

[3] : EXAMPLE OF SafetyNex RISK PROFILES

Feb
23

SafetyNex Video Demo Onboard

Voir la version française de la démo

SafetyNex is a Smartphone APP that measures Risk taken by the Driver in Real Time and warns him before dangerous areas.

SafetyNex is available throuth an API (Android, iOS, Windows, Linux)

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