NEXYAD will be present at Autonomous Vehicle World Expo 2018 in Stuttgart, June 5 – 6 – 7
Come to visit Nexyad on Groupement ADASbooth 2015/hall C
Discover our 4 Artificial Intelligence Algorythms for ADAS, Autonomous Driving and Telematics
SafetyNex : Real Time Driving Risk Assessment
Fusion of Digital Map with Sensors, combinable with RoadNex, ObstaNex and VisiNex
Artificial Intelligence giving Safety to your Driver Assistant or your Autonomous Driving Systems
VisiNex : Measurement and Score of Visibility
Detection of Lack of Visibility, Fog, Heavy Rains, etc… on front of Vehicle
Artificial Mono Vision Algorythm for Autonomous Driving & ADAS
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.
* * * * *
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
* * * * *
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.
* * * * *
« 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).
* * * * *
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.
« 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).
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.
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.
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