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
Headlines :
– Road accident and driving risk are two different notions
– Nexyad at AutoSens Brussels 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
– Navigation Based ADAS : use SafetyNex to build ACC and anticipation (predictive) brake systems
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.
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
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.
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
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
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.
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.
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.
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 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).
More info : http://www.safetynex.net
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.
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,…
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.
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 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).
More info : http://www.safetynex.net