CAR DETECTION WITH OBSTANEX ON A REGULAR COMPUTER ARCHITECTURE : LOW DEPLOYMENT COST, LOW ENERGY CONSUMPTION, LESS HEAT, etc.
Here is a snapshot of car detection using RoadNex.
The big differenciation of ObstaNex is that is runs on a regular computer architecture (on a smartphone for instance) : no need for a heavy computing system. It is then much cheaper for mass volume deployment (new cars and aftermarket), because heavy computing architectures bring computing speed, but also high deployment cost, Energy consumption, heat, etc … not that good for onboard systems.
Watch this 2 minutes video showing 5 concrete examples of driving situations where the eyes of the driver and the ADAS sensors of the vehicle are not sufficient to prevent accident.
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
The new video on SafetyNex, on board driving risk assessment in real time.
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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)
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.
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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.
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:
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:
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:
« 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 :
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).
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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.
NEXYAD Automotive & Transportation Newsletter #21, March 19th, 2018
Artificial Intelligence for Automotive with SafetyNex
Headlines :
– CAC Conference 2018 on Connected Car in Paris
– NEXYAD SafetyNex in Media
– The value of driving risk notion for Telematics, ADAS and Autonomous Driving
– SafetyNex and the compliance Package of CONNECTED VEHICLES AND PERSONAL DATA
The Connected Automotive Conference, held March 13, 2018 in Paris, is the French reference in conference on the connected vehicle.
Several themes were discussed around selected guests:
– What is the innovation « Made in France »? Decryption of the latest advances and ongoing pilot projects that will bring major changes in the field of mobility.
– New expectations of the French. Analysis of the latest studies conducted with citizens and put in perspective with the results around the world.
Which ADAS will integrate the automobile tomorrow? After smart parking and cameras, what driving assistants will be used in tomorrow’s vehicles and for what use?
– How will AI change the lives of motorists? From GAFA to start-ups, everyone dreams of designing the intelligent assistant of the motorist. The relationship with the brand will be transformed.
Then, followed interview, key-note, startup contest and experts workshops, all day long.
Gérard YAHIAOUI, CEO of Nexyad, was invited to participate at the conference as an expert in Articicial Inteligence, Advanced Driver Assistance Systems and Highly Automated Driving.
Here is a news in French press that talks about the Academic Chair that the cluster of startups and SMEs « MOVEO Groupement ADAS » organized with INSA Rouen.
NEXYAD is part of this cluster of high-tech startups and SMIs (on ADAS, connected car, and autonomous driving) and is quoted in this article of Journal du Net (French spoken), they interviewed Mr Aziz Benrshair, director of the « Autonomous and Connected Vehicle » Academic Chair launched by INSA Rouen : Comment ces partenaires contribuent-ils concrètement ?
« Ils assurent environ 50% de l’enseignement. Des experts de ces entreprises viennent enseigner sous forme de TP ou de TD. Ils transmettent leur savoir faire et expliquent les projets sur lesquels ils travaillent. La société Sherpa Engineering est par exemple intervenue sur les questions d’actionneurs, de prise de décision et de commande automatique. Nexyad a abordé l’analyse du comportement du conducteur, proposée par sa solution d’évaluation des risques d’accidents SafetyNex. Des partenaires historiques extérieurs à cette chaire, comme Valeo ou Vedecom, sont également intervenus. »
The value of driving risk notion for Telematics, ADAS and Autonomous Driving
The value of driving risk notion for Telematics, ADAS and Autonomous Driving.
by NEXYAD
Every year, more than 25.000 persons die on roads in Europe which has the safest infrastructures anyway. Brasil, Russia, USA, have more fatalities and the situation is worst in development countries. Everywhere people are aware by these risk for their health or life. Driving can be dangerous for drivers and passengers, however most of people accept these risk fairly minimal (in average three dead by billion km in OECD countries) for all advantages of fast point to point terrestrial mobility. But by the way, what is exactly what people use to call driving risk?
Let’s take an example, if someone plays Russian roulette: probability to die is one on six when one pulls the trigger. If one decides finally not to play, probability to die with a bullet in the head disappears completely. If you pull the trigger, risk to die is 100% (although probability is 1/6).
Another example: if a car is static parked into garage, then driving risk is zero. On the opposite, if a car passes a stop sign at 20km/h, driving risk taken by the driver is equal to 100%: driver takes the full risk). Probability depends on the traffic at the intersection.
More generally, driving risk taken by driver (and we talk about “the risk you take” a priori) will goes from 0 to 100% depending on the adequate of driving behaviour to driving context. This driving context has several dimensions: complexity of infrastructure, traffic of other road users, weather conditions, etc. Inadequate of driving behaviour to complexity of infrastructure can predict 75% of accident.
SafetyNex and the compliance Package of CONNECTED VEHICLES AND PERSONAL DATA
In march 2018, french CNIL will publish the final version of the Compliance Package for the Connected Vehicles and Personal Data.
NEXYAD appears in the list of Bodies consulted by the CNIL (p.3). An interesting article about the collected data shows that SafetyNex is fully compliant with french law and recommendations for European Union (p.25).
Extract :
DATA COLLECTED
The data control shall only collect personal data that are strictly necessary for the processing. In the case of a contract for the provision of services, the only data that can be collected are those that are essential for the provision of service.
Concerning data relating to criminal offences:
For purpose 1 (model optimisation and product improvement) and 3 (commercial use of the vehicle’s data): except in the case of specific legal provision, data that are likely to reveal criminal offences shall not be processed by legal persons who do not administer a public service,
except to defend their rights in court. However, that data can be processed locally, directly in the vehicle, in accordance with scenario No. 1, in order to give the user control over that particularly sensitive data and limit as much as possible the consequences on privacy.
strong caracters are made by Nexyad
Reminder: onboard systems, telematics devices or smartphone Apps, which collect speed and location data, allow easily to make reconstruction of criminal offences.
In march 2018, french CNIL will publish the final version of the Compliance Package for the Connected Vehicles and Personal Data.
NEXYAD appears in the list of Bodies consulted by the CNIL (p.3). An interesting article about the collected data shows that SafetyNex is fully compliant with french law and recommendations for European Union (p.25).
Extract :
DATA COLLECTED
The data control shall only collect personal data that are strictly necessary for the processing. In the case of a contract for the provision of services, the only data that can be collected are those that are essential for the provision of service.
Concerning data relating to criminal offences:
For purpose 1 (model optimisation and product improvement) and 3 (commercial use of the vehicle’s data): except in the case of specific legal provision, data that are likely to reveal criminal offences shall not be processed by legal persons who do not administer a public service,
except to defend their rights in court. However, that data can be processed locally, directly in the vehicle, in accordance with scenario No. 1, in order to give the user control over that particularly sensitive data and limit as much as possible the consequences on privacy.
strong caracters are made by Nexyad
Reminder: onboard systems, telematics devices or smartphone Apps, which collect speed and location data, allow easily to make reconstruction of criminal offences.
– Come to meet Nexyad at CES 2018 in Las Vegas – January 8-12
– NEXYAD giving the award of the best Insurtech startup «prix coup de cœur des assureurs 2017»,
organized by Cercle LAB (Laboratoire Banque Assurance) at Allianz Tower in Paris La Défense
– Nexyad invited speaker at UNESCO Conference on Artificial Intelligence : use case of autonomous vehicle
– SafetyNex driving risk assessment (20 times per second while driving): anticipation of danger
– INTEMPORA and NEXYAD, members of MOVEO Groupement ADAS interviewed on BFM Business (Major French TV)
– Bitumen Free Space Detection by Nexyad RoadNex module in real time on a Smartphone
– Nexyad on Groupement ADAS booth at Equip’Auto 2017
– Validation of ADAS Nexyad Database with ground reality
– Individual driving risk assessment and car insurance : what applications ? what business models ?
– SafetyNex can bring Artificial Intelligence into Autopilots in respect of ASIL ISO 26262
– Welcome to YOGOKO, new member of “MOV’EO” Groupement ADAS cluster
Come to meet Nexyad at CES 2018 in Las Vegas (Jan 8-12)
Nexyad invite you to visit us at the Leddar Ecosystem Pavillion at East LVCC – Central Plaza (booth CP-23)
If you want to reserve a meeting slot, please contact us at nexyadCES2018@nexyad.net
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NEXYAD giving the award of the best Insurtech startup « prix coup de cœur des assureurs 2017», organized by Cercle LAB (Laboratoire Banque Assurance) at Allianz Tower in Paris La Défense
Gerard Yahiaoui at Cercle Lab
Last year Nexyad won the special prize « Coup de Cœur » by french insurers of Cercle Lab with SafetyNex the driving risk assessment App in real time. For this, Gerard Yahiaoui CEO of Nexyad handed the 2017 new prize to the winner KAP-Code represented by Adel Mebarki. Kap-Code is dedicated to improve the care of chronic diseases and the detection of drug safety signals on social networks thruth 3 solutions : helping patients and health advisors with connected objects, Digital Health that allows profesionals to provide care for their patients and harnessing Big Data for science.
Adel Mebarki, head of innovation of Kap-Code
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Nexyad invited speaker at UNESCO Conference on Artificial Intelligence : use case of autonomous vehicle
Nexyad CEO Gérard Yahiaoui A.I. expert was invited to speak (invited paper) in a conference in Paris on Artificial Intelligence (JNI/IESF, Under the patronage of UNESCO, 2017 Oc 19). He talked about Artificial Intelligence for Autonomous Vehicle : « Intelligence Artificielle pour le Véhicule Autonome, et exemple de réalisation : SafetyNex ». Very interesting papers during this day showing a broad range of AI applications (automotive, fashion, HR management, Legal, …). More than 400 people in the audience.
Gerard Yahiaoui explaning difference between A.I. and complex automation for autonomous driving
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SafetyNex driving risk assessment (20 times per second while driving): anticipation of danger
SafetyNex estimates driving risk 20 times per second, during driving (real time).
On the following figure, you can see risk rising when approaching a stop sign with an inappropriate car speed :
Speed of the car is quite high before the STOP sign and Risk goes to the maximum with a vocal alarm to the driver which have time to slow down or brake to stop.
This estimation is computed INSIDE the local device (inside the car). Current implementation is on smartphones (IOS and Android), then computing of risk is completely done INSIDE the smartphone : that makes SafetyNex compliant with all driver’s privacy regulations and laws in Europe.
INTEMPORA and NEXYAD, members of MOVEO Groupement ADAS on BFM Business (Major French TV)
1st Nov 2017, the Tech & Co tv show on the subject : will self-driving car come sooner than expected ?
Nicolas du Lac & Gerard Yahiaoui
Nicolas du LAC, INTEMPORA, and Gerard YAHIAOUI, NEXYAD, presented their innovations and explained how the MOVEO Groupement ADAS helps to be stronger for their innovative startups.
Nicolas talked about RT-MAPS that is a software tool for R&D, making easy the task of developing applications with multiple sensors (cameras, lidar, radar, …) that of course are not synchronized and that must collaborate through algorithms of sensor fusion in order to get good objects detection and recognition.
Gerard talked about SafetyNex that is an onboard real time module that is the only module in the world that can estimate driving risk 20 times per second. Self-driving car can then know the risk it takes with and it simplifies the development of autopilot (example : « if risk too high then slow down »).
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Bitumen Free Space Detection by Nexyad RoadNex on Smartphone
RoadNex detects free space on road with negative detection of obstacles as vehicle on the video below.
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Nexyad present with Groupement ADAS at Equip’Auto 2017
Philippe Orvain, CEO of Nomadic Solutions
Nexyad was present with Groupement ADAS at Equip’Auto Congress in Paris. Groupement ADAS is a SME’s cluster : 10 companies with expertise in the field of Advanced Driver Assistance Systems, Connected car and Autonomous vehicle. Philippe Orvain CEO of Nomadic Solutions and competitiveness cluster MOV’EO Vice President has responsed to journalist Laurent Meillaud on Congress TV channel.
Watch Philippe Orvain interview on the congress channel with SafetyNex video demo :
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Validation of ADAS Nexyad Database with ground reality
Free space ground reality (for RoadNex) and obstacles ground reality (for ObstaNex)
The NEXYAD company is currently developing the construction of a database for the validation of systems of driver assistance and driving delegation, (ADAS and Autonomous car) using the AGENDA methodology published in the 1990s by Gérard Yahiaoui in the field of machine learning and artificial neural networks applications. Here is an example of ground reality : ground reality is needed in order to automate performance / KPIs measurement when you modify the perception system.(methodology initially intended to handle, among other things, the construction of learning databases and tests for the implementation of neural networks).
This database has two essential characteristics:
1) Real-life situations
Indeed, the AGENDA methodology recommends describing the possible variations of signals and input images as factors of variability and their crosses.
Example, for obstacle detection:
. weather (dry weather, sunny weather, rain, fog) . overall brightness (low, medium, high)
. vehicle speed (low, moderate, high)
. type of road (motorway, road with marking, road without marking, …)
. coating (bitumen 1, bitumen 2, …, pavers)
. day / night (car headlights and infrastructure lighting)
. season (spring, summer, autumn, winter)
. etc.
NEXYAD’s ambition is to propagate its methodological expertise and to enable everyone to evaluate the performance of vision systems for ADAS, be they systems developed by NEXYAD or others.
Individual driving risk assessment and car insurance : what applications ? what business models ?
As many people know now, NEXYAD has been developing the first real time driving risk assessment system called SafetyNex.
SafetyNex is currently available in B2B :
. as a smartphone App (Android and IOS)
. as a real time driving risk assessment API that OEMs and Insurers may integrate into their own smartphone App or into their own telematics or ADAS device (Android, iOS, Linux, Windows).
This real time driving risk assessment module has been validated on 50 million km, and applies proven methods for risk assessment, using, for instance, the Frank E. BIRD « safety triangle » concept, and running in real time a knowledge-based system AI that has been built by NEXYAD since 2001. It took 15 years to extract thousands of road safety knowledge atoms from experts of 19 countries. Some of this knowledge is directly operational, some is deep knowledge on detection theory (a mix of Information Theory and Knowledge on Human Brain abilities). And of course, SafetyNex also applies fundamental knowledge on mechanics (braking abilities, …) including complex issues such as grip for example.
Complex Automation MUST be ASIL ISO 26262.
Artificial Intelligence CANNOT BE ASIL ISO 26262 (by definition) and acts only on parameters of Complex Automation doing ++/– – variations, never skipping « reflexes actions » (emergency braking, etc), but allowing anticipation speed adaptation to reduce frequency of emergency situations (and then give more margin to reflexes actions and also improve comfort). Maximum acceptable Driving Risk can be changed depending on driving situation in order to set « aggressivity level» of HAV.
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Welcome to YOGOKO, new member of « MOV’EO » Groupement ADAS cluster
In november, cluster Groupement ADAS, from Mobility and Automotive R&D competitiveness national cluster Mov’eo, welcomed YOGOKO as new member. It makes eleven players like a football team, and we hope to score goals in the Automotive market competition.
YoGoKo is a startup company founded in 2014 by employees from three research institutes : Mines ParisTech, Telecom Bretagne and Inria. YoGoKo makes use of software developed in teams specialized in Internet technologies (RSM at Telecom Bretagne) and robotics (CAOR at Mines ParisTech and RITS at Inria). These research teams have been working together since 2006 on innovative communication solutions applied to Intelligent Transportation Systems. They contributed to several collaborative R&D projects related to ITS (CVIS, ITSSv6, GeoNet, DriveC2X, SCORE@F, …).
In 2012, these laboratories engaged together into the development of a common demonstration platform which comprises connected vehicles (fleet of conventional vehicles from Mines ParisTech and fleet of autonomous vehicles from Inria), roadside equipments and cloud-based services.
YoGoKo demonstration platform was finally revealed on Feb. 11 th 2014 during the Mobilité 2.0 event organized by the French Ministry of Transport. This successful demonstation and the extremely warmfull feedack gained at this occasion triggered the launch of YoGoKo as a company.
SafetyNex estimates driving risk 20 times per second, during driving (real time).
On the following figure, you can see risk rising when approaching a stop sign with an inappropriate car speed :
Speed of the car is quite high before the STOP sign and Risk goes to the maximum with a vocal alarm to the driver which have time to slow down or brake to stop.
Now with a slower speed profile :
Speed of the car is lesser here but still too high approaching the STOP sign, there is a vocal alarm again but for lesser time.
And finally the « good » safe speed profile :
There is a good anticipation here before the STOP sign, speed is adequate and risk is very low, then null.
This estimation is computed INSIDE the local device (inside the car). Current implementation is on smartphones (IOS and Android), then computing of risk is completely done INSIDE the smartphone : that makes SafetyNex compliant with all driver’s privacu regulations and laws in Europe.
Applications :
. insurance and fleets : vocal alert when risk is too high. As you can see, it lets time to slow down and avoid potential accident. This leads to a reduction of accident rate and a reduction of personal injuries. Of course, risk values can be recorded to do stats (risk profiles) for UBI (Usage Based Insurance) applications.
. Automotive : same application is navigation with risk alerts. But with a robotized braking system (ADAS), it is possible to trigger braking with risk value. In such a case, it is not emergency braking but rather anticipation braking.
In addition, it is possible, in the case of HAD (highly Automated Driving) car, to control speed (longitudinal part of auto pilot) very easily : « if risk too high, then slow down », « if risk is low, then accelerate until risk becomes the max value you accept or until you’re at the speed limit ». You write your autopilot with very few lines of code !
As many people know now, NEXYAD has been developing the first real time driving risk assessment system called SafetyNex.
SafetyNex is currently available in B2B :
. as a smartphone App (Android and IOS)
. as a real time driving risk assessment API that OEMs and Insurers may integrate into their own smartphone App or into their own telematics or ADAS device (Android, iOS, Linux, Windows).
This real time driving risk assessment module has been validated on 50 million km, and applies proven methods for risk assessment, using, for instance, the Frank E. BIRD « safety triangle » concept, and running in real time a knowledge-based system AI that has been built by NEXYAD since 2001. It took 15 years to extract thousands of road safety knowledge atoms from experts of 19 countries. Some of this knowledge is directly operational, some is deep knowledge on detection theory (a mix of Information Theory and Knowledge on Human Brain abilities). And of course, SafetyNex also applies fundamental knowledge on mechanics (braking abilities, …) including complex issues such as grip for example.
A tough work was to be made in order to build such a knowledge-based system that could cope with « gradual truth » and could run a complex knowledge-based model in real time. Implementation uses fuzzy sets both for facts and knowledge representation, and competition between knowledge atoms uses deep Possibility Theory concepts. Of course, SafetyNex deals with dynamic problems (road accident, with distances, speed, …) and had to find a proper representation of TIME. NEXYAD was not very quick to develop all those solutions and to integrate them into a product called SafetyNex : SafetyNex is proposed for sale since Jan 2017 (first R&D on SafetyNex : 2001). But the result is efficient : you can run SafetyNex on your smartphone while driving your car and see how it helps you to drive much safer without alerting for nothing.
This unique technology places now NEXYAD in the center of the worldwide telematics/adas/data collecting race for car insurers.
So let’s see what are the main applications of SafetyNex for car insurers :
. PREVENTION : SafetyNex alerts (vocal alert) driver BEFORE a dangerous situation, letting time to slow down and avoid accident. It is the best prevention tool ever : studies showed that 20% of accident can be avoided (then also 20% of personal injuries and fatalities !!!). SafetyNex is the ONLY tool that runs in real time inside the smartphone or inside the telematics/adas devices and then is the ONLY module that can alert driver (all the other systems apply scoring methods on the cloud …). This is a major differenciation for SafetyNex : avoid accident ! Observation of drivers using SafetyNex showed that they change their way of driving in order to mimnimize the number of alerts, then they change their driving behaviour in the long term.
. COMMUNICATION : SafetyNex gives driver a score of Safe Driving after every trip. This is a positive way of presenting risk and driver appreciate being given a gold cup for his/her high safe driving score. You can imagine many ways to communicate for car insurers. Example : every year, sort all drivers (customers) of a country, find the best (highest SafetyNex safe driving score) and give a huge incentive (offer a brand new car, for instance). Such an incentive tranforms the car insurer’s customers set into a community that tries to drive better, and you can build of course a social network for experience share and local challenges … etc. This is a simple and very concrete use of digital technology both for getting better profits and for saving lives. Marketing executives call this ‘the Nudge Theory ». SafetyNex is a natural candidate for applying this Nudge Theory.
. KNOWLEDGE : car insurers only know about risk by accident observation. Although the theory of Frank E. BIRD (safety triangle) shows that the proper variable to observe would be what is called « near miss » or « quasi accident ». SafetyNex alerts every time that driver’s behaviour leads to a quasi accident situation. Then car insurers can use SafetyNex to build this new knowledge and sort and cross all their variables with this new notion. Actuaries will quickly find clever ways to use those new sets of data and you will be surprised to see how productive data scientists can be when they have a direct access to new qualified huge set of data.
. USAGE BASED INSURANCE : Of course, SafetyNex is THE perfect tool for UBI. At the end of every trip, SafetyNex sends data (statistics) : usage (urban, road, highway, time, duration, km, postal code, etc …) and risk (risk profiles). Then it is very easy to propose Usage Based princings (‘as you drive » or « how you drive » etc).
It is important to notice that real time abilities of SafetyNex also simplify business models :
. Anyway car insurer does effective prevention with SafetyNex : it is only needed to avoid few personal injuries and fatalities to « pay » the deployment of SafetyNex. That’s the key of SafetyNex deployment : SafetyNex alerts driver WHILE DRIVING and BEFORE danger, then many accident are avoided … then many personal injuries are avoided. If you know the price of a personal injury in you car insurance, then you see that SafetyNex price was made in order to let you get a fair ROI on this issue.
. Communication (incentive) is then already paid by the number of avoided personal injuries !
. and in addition, car insurer gets qualified data : for knowledge acquisition and UBI opportunities study.
As a conclusion, we can remember main facts :
. SafetyNex real time abilities make it VERY DIFFERENT from any other risk assessment solutions (using stats and data science in the cloud) because then SafetyNex ACTS on driving behaviour BEFORE accident and then lets avoid many accidents.
. Prevention effect of SafetyNex finances it deployment cost.
. Knowledge extraction and UBI opportunities business models can be done without stress as SafetyNex already gives a fair ROI to car indurer.
Some OEMs and insurers already started to integrate SafetyNex API into their smartphone App or into their telematics/adas device.
It is now time to move forward to the 21st century with digital insurance.
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.
Let’s consider one or the other of those curves, it is easy then to cut the risk into slots:
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 :
It can be seen that this graph can be seperated into three parts:
. 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.
Nexyad Automotive & Transportation Newsletter #15 is Available
2017 Year of mass deployment for Nexyad Products
After three years of Nexyad products development, 2017 will see the start of mass deployment of its products. An Automotive EOM has integrated RoadNex & ObstaNex for road detection and obstacles detection on special vehicles and an Infrastructure EOM has integrated Visinex for visibility measurement on highways. Beside, we’ll soon announce our first customers for SafetyNex.
Headlines :
– Nexyad Review after the CES 2017 in Las Vegas
– Example of SafetyNex Risk Profiles Data
– SafetyNex App Risk Profiles Analysis
– Nexyad invited to the 4th “Les rencontres du Fleet Management”
– NEXYAD new award: Champion SME (PME championne) of the French cluster MOV’EO
– Interview of Mov’eo and Nexyad by AutoK7
– New Update for Nexyad Modules RoadNex (Road detection) & ObstaNex (Obstacles Detection)
NEXYAD Automotive & Transportation Newsletter #15, February 13th, 2017
2017 Year of mass deployment for Nexyad Products
After three years of Nexyad products development, 2017 will see the start of mass deployment of its products. An Automotive EOM has integrated RoadNex & ObstaNex for road detection and obstacles detection on special vehicles and an Infrastructure EOM has integrated Visinex for visibility measurement on highways. Beside, we’ll soon announce our first customers for SafetyNex.
Nexyad was part of the Mission and took the opportunity to meet its partners, customers and future customers in Las Vegas. The show is bigger than everybody has told us before. There is thousands of boothes in several places in Las Vegas. It is impossible to see everything in four days long. This year, French Tech was well represented in the Venetian Hotel and at the Convention Center near the Westgate Hotel. Thursday January 5th, Nexyad was invited to a networking reception sponsored by Orange with the presence of Mr Stéphane Richard, chairman and CEO of Orange. The event took place at the Air Bar on the top of the Strastosphere Tower in Las Vegas.
Special interests seen about ADAS & Autonomous Driving at CES in Las Vegas
Abeeway already present at CES 2015 presented this year its new solution for autonomous geolocalisation with great duration (one year).
Giant Alphabet announced process of finalizing its autonomous car business through spin-off Waymo. Is Google will be car manufacturers or just embedded technology platforms and in the cloud technology for manufacturers ?
The GENIVI industrial alliance provides an open source system initiative for automotive manufacturers and their suppliers in connected car field. It collaborates with the Open Connectivity Foundation for connectivity between vehicles and smart homes. The alliance includes manufacturers such as PSA60, Renault Nissan, BMW and Daimler.
HERE Indoor Positioning brings precision to the industrial IoT.
HERE Indoor Positionning demo
The NEXYAD team present in Las Vegas was invited by officials on the HERE booth at Convention Center Central Plazza.
When we entered the booth, we saw a table with mini robotic forklifts in a warehouse, all built with real technology. The robots moved around a simulated environment picking up and delivering containers based on high-accuracy positioning, bringing the real-world applications to life.
It was one of the numerous technologies presented by HERE at CES this year. More about HERE here
LeddarTech has showcased three innovative 2D and 3D high-resolution LIDAR « solid state » solutions for autonomous driving applications based on next-generation LeddarCore ICs and developed with the collaboration of leading-edge suppliers and partners from the newly-established Leddar Ecosystem.
Nexyad hat a « summit » meeting with Leddartech (top of the Westgate Hotel on the booth of the canadian company). Both companies spoke about various future projects within the Groupement ADAS.
Gérard Yahiaoui CEO of Nexyad and Pier-Olivier Hamel Product leader at Leddartech.
Quanergy Systems rised $90m in 2016 to built Autonomous cars sensors with US Partner Sensata Technologies.
REVA2 participated for the first time to CES and present a vehicle that is both autonomous and traditional. It is in fact a complete automotive system that ambition to create the French startup.
robotTUNER (An automated vehicle that looks like the french one NAVYA).
Twinswheel has presented its autonomous droid for parcel delivery services in urban area.
New ADAS demos by VALEO
Valeo onboard demos area at Golden Lot was completely crowed during the four days of the Convention. But we were lucky happy few to test Valeo XtraVue and Valeo 360AEB Nearshield.
Valeo XtraVue is a system based on a set of connected cameras that eliminates visual obstacles. It takes two vehicles equipped with the system. The driver of the vehicle B, which follows the vehicle A, can see on its control screen what happens in front of the vehicle A by seeing through it.
VALEO XtraVue demo car
Valeo 360AEB Nearshield (Autonomous Emergency Braking) is an innovative technology to protect nearby pedestrians when vehicles perform low-speed maneuvers. With a full 360 degrees system of cameras and ultrasonic sensors, this ADAS helps the driver to avoid accident due to blind spots around large vehicles such as SUV and pick-up trucks. Read more about Valeo at CES
Verizon (fixed and mobile telecom) was updating 2.0 of its Go90 video streaming application for iOS and Android which now supports Apple TV and Google Chromecast. It will allow the sharing of video links with other users via social networks. Verizon Digital Media acquired Volicon, which manages video distribution back-ends and analytics with its Media Intelligence Platform. Another acquisition is Fleetmatics for $ 2.4B. It is a fleet tracking service via GPS, a deal to get into the automotive sector.
The sugar on the top was Yahoo !, acquired for only $ 4.8B. the top is its billion unique monthly users including 60% on mobiles, and its advertising network. This complements the acquisition of AOL, Yahoo being intended to integrate it.
Visteon Demonstrates Augmented Reality Driving Experience and Latest Head-Up Display Technology at CES® 2017.
Complementing the vehicle’s HUD, embedded front-view and driver monitoring cameras trigger “smart alerts” in the form of lights and sounds when the driver is not paying attention to the road, if the vehicle strays from its lane, or if the vehicle is at risk of potentially hitting an object. For example, when a pedestrian or bicycle is present on the side of the road, an LED light projects onto the windshield within a 90-degree angle of the driver’s line of sight, giving a visual alert without the driver needing to turn his or her head.
Two onboard cameras look at the front of the car (a video for the demo),
detect obstacles and alerts the driver via a HUD.
* * * * *
Example of SafetyNex Risk Profiles Data
See below an example of Risk Profile Data Nexyad is able to give to their customers. SafetyNex provides Eco Driving profiles data, and usages profiles (Kms, date, hours, kind of roads, etc…) all of these can be crossed with Risk Profiles.
Risk profiles estimated by SafetyNex: Analysis of profiles, and possible use to detect fatigue and hypovigilance of driver.
SafetyNex is a nomadic real-time risk estimation system. The system has been described in detail in previous publications [1] and uses the key concept of « near-accident » or « quasi-accident », and is a result of 15 years of collaborative research with road safety experts and researchers.
The main competitive advantage of SafetyNex is that it allows, since the risk is estimated in real time,
to warn the driver (vocal alert), and thus to allow driver to avoid accident. Studies show that SafetyNex can reduce accident rate by 20% [2], which represents for insurers and fleet managers a consequent increase in margin [3].
But of course, SafetyNex also records usage and risk profiles. These profiles provide the behavior of the driver, or more precisely, his/her ability to regulate driving task consistently with danger. No need to record large volumes of data (accelerations, etc…) which in reality are not data (these are signals) for a possible back-office analysis, SafetyNex provides exactly the interesting data [4].
Below are examples of usage profiles and driver risk profiles.
Nexyad invited to the 4th « Les rencontres du Fleet Management »
Last thursday 26th of january 2017, Gérard Yahiaoui CEO of Nexyad participated to a panel debate in Paris near Les Champs Elysées. He was invited as road safety telematics expert. Marie-Amélie Fenoll journalist at Decision Achats animated the discussion between other speakers : Jean-Yves Marie Rose of Ademe, Julien Honnart of Wayzup, Didier Blocus of ALD Automotive France, Edwin Colella and Alain Sinquin of Octo Telematics.
Fleet Management faces numerous problems and stakes in terms of safety, monitoring of drivers, training and loss costs. Gérard Yahiaoui presented SafetyNex App risk assessment in real time, that warns the driver before danger and gives individual and global risk profiles and usage profiles. An API of SafetyNex will be available very soon to integrate in any device. SafetyNex use allows to reduce accident rate by 20%.
from the left Marie-Amélie Fenoll, Edwin Collela and Gérard Yahiaoui
* * * * *
NEXYAD new award :
Champion SME (PME championne) of the French cluster MOV’EO
NEXYAD is proud to announce that MOV’EO selected NEXYAD to be one of their CHAMPION high tech SMEs
(PME championne du pôle de compétitivité MOV’EO).
« We are very proud to get this award from MOV’EO that works hard for high tech SMEs development » said Gerard YAHIAOUI, CEO of NEXYAD. « We participated to the MOV’EO mission at CES 2017 in LasVegas, with Business France, and for us, it is already a success ». « This new award will give us more exposure and for a High-Tech SME it is always a good thing ».
« Nous sommes très fiers d’obtenir cette récompense de MOV’EO qui œuvre pour le développement des PME de hautes technologies dans le secteur de la mobilité. Nous avons participé à la la mission CES 2017 à Las Vegas, avec Business France, et pour Nexyad, c’est déjà un succès. Cette nouvelle récompense nous apporte plus d’exposition et pour une PME de High-Tech c’est toujours une bonne chose. » a déclaré Gérard Yahiaoui le P-DG de Nexyad.
* * * * *
Interview of Mov’eo and Nexyad by AutoK7
Nicolas Dattez from french competitivity cluster MOVEO and Gérard Yahiaoui CEO of Nexyad talk about their experience of CES in Las Vegas with Christophe Bourroux for RadioK7 « Voice of Mobility »
« We are stronger at ten to push heavy doors ! »
(Click to play)
* * * * *
New Update for Nexyad Modules RoadNex (Road detection) & ObstaNex (Obstacles Detection)
Nexyad development team work hard to give updates and new functionnalities to its ADAS modules. See Below an example of road detection with the last update on RoadNex:
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from the left Marie-Amélie Fenoll, Edwin Collela and Gérard Yahiaoui
