– 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
* * * * *
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
* * * * *
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
* * * * *
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”).
* * * * *
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.
* * * * *
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 :
* * * * *
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.
* * * * *
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.
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 (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.
Type of obstacle: – static obstacle
. linked to the infrastructure: works terminals, tolls, …
. related to users: tire on the roadway, package dropped from a truck, motorcyclist lying on the road following an accident, vehicle broken down stopped on the roadway, pedestrian stationary on the edge of the roadway (visible / hidden) – moving obstacle
. truck, car, vulnerable (pedestrian, bike, motorcycle) with the typical trajectories (longitudinal in the direction of travel, longitudinal in the opposite direction of travel, lateral) and the position (opposite, right, left) .
. Etc.
Real-life situations examples :
We see that if we combine these factors, we can find quite quickly a very large number of cases. Now, the development of ADAS systems is complex, and it is necessary to proceed by successive iterations, from simple situations to complicated situations.
Our database allows this, since all records are described such as cross-referring of modalities of the factors of variability. We thus know exactly in which cases the system was tested or not.
The formalism of ‘cross-referring of modalities of the factors of variability’ makes it possible to use experimental designs, and in particular orthogonal fractional plans, to greatly reduce the number of cases to be tested while guaranteeing maximum coverage of real-life situations. In this context, we can develop an ADAS on an orthogonal fractional plane and test it for other orthogonal fractional planes, for example.
2) Ground reality
It is a question of define the obstacles and elements of the infrastructure (markings, road edges, etc.) on the images so as to constitute a reference allow measuring the performance of the system.
1.1, summer, cloudy, unmarked road, moderate speed, tire on roadway, dry weather
1.2, summer, cloudy, unmarked road, moderate speed, package on roadway, dry weather
2.1, summer, cloudy, unmarked road, moderate speed, visible static pedestrians at the edges of the roadway, time
2.2, summer, cloudy, unmarked road, moderate speed, human lying on roadway, dry weather
etc.
It is not certain that one can meet these few cases, even by driving 1 million km on open road!
Target of this database :
NEXYAD starts its collection of images and data:
. video (towards the front of the vehicle) color
. accelerometers
. gyros
The files are synchronized by the RT-MAPS tool of the company INTEMPORA.
The files are saved in RT-MAPS format and directly replayable on this tool.
NEXYAD is currently looking for contributors on this internal project. Contributors co fund and have in return free access to the database, unlimited in time. This contribution will accelerate the work of collection and labeling.
NEXYAD wishes to make this database available soon, free of charge to give material to the ADAS community and the autonomous vehicle, in a reduced version of the database, and in a paid way (in the form of subscriptions) for the complete base.
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.
Wenesday 1st of June : Presentation this morning by Gérard YAHIAOUI President & CEO of NEXYAD, France
“BUILDING A RELEVANT VALIDATION DATABASE FOR CAMERA-BASED ADAS”
Validation of camera-based artificial vision systems applied on open world is a very complex issue. An HD colour camera may generate more than 65 000 power 2 000 000 different images (information theory), so it is not possible to test every possible message. We propose a deterministic approach for building a validation database using the AGENDA methodology that was developed and published in the 1990s for neural network database (learn & test) design.
A large audience attended to this conference that questions the way for Autonomous Vehicle ADAS validation.
Gérard on the left, the conference’s audience on the top and the booth of Groupement ADAS from the mezzanine.
NEXYAD Automotive & Transportation Newsletter n°4, le 24 août 2015
Base de données de validation des ADAS utilisant des caméras
La société NEXYAD démarre actuellement la construction d’une base de données pour la validation des systèmes d’aides à la conduite et de délégation de conduite (ADAS et Autonomous car) en utilisant la méthodologie AGENDA publiée dans les années 90 par Gérard Yahiaoui (méthodologie au départ destinée à maîtriser entre autre la construction des bases de données d’apprentissage et des tests pour la mise en œuvre des réseaux de neurones).
Cette base de données a deux caractéristiques essentielles :
1) Situations de vie
En effet, la méthodologie AGENDA préconise de décrire les variations possibles des signaux et images d’entrées en facteurs de la variabilité et leurs croisements.
Exemple, pour de la détection d’obstacles :
. météo (temps sec couvert, temps ensoleillé, pluie, brouillard)
. luminosité globale (faible, moyenne, forte)
. vitesse du véhicule porteur (faible, modérée, grande)
. type de route (autoroute, route avec marquage, route sans marquage, …)
. revêtement (bitume 1, bitume 2, …, pavés)
. jour / nuit (phares et éclairages de l’infrastructure allumés)
. saison (printemps, été, automne, hiver)
. etc …
> type d’obstacle :
– arrêté
. liés à l’infrastructure : bornes de travaux, péages, …
. liés aux usagers : pneu sur la chaussée, colis tombé d’un camion, motard allongé sur la
route suite à un accident, véhicule en panne arrêté sur la chaussée, piéton immobile sur le
bord de la chaussée (embusqué / non embusqué)
– en mouvement
. camion, voiture, vulnérable (piéton, vélo, moto) avec à chaque fois les trajectoires types (longitudinale dans le sens de roulage, longitudinale dans le sens inverse du roulage, latérale) et la position (en face, à froite, à gauche).
. Etc…
On constate que si l’on croise ces facteurs, on trouve assez rapidement un nombre de cas énorme. Or, la mise au point des systèmes ADAS est complexe, et il est nécessaire de procéder par itérations successives, en partant de situations simples pour aller vers les situations compliquées.
Notre base de données permet cela, puisque tous les enregistrements sont décrits en termes de croisements des modalités des facteurs de la variabilité. On sait ainsi exactement dans quels cas on a testé ou pas le système.
Le formalisme de ‘croisement des modalités des facteurs de variabilité’ permet d’utiliser les plans d’expériences, et en particulier les plans fractionnaires orthogonaux pour réduire fortement le nombre de cas à tester tout en garantissant une couverture maximale des situations de vie. On peut dans ce cadre mettre au point un ADAS sur un plan fractionnaire orthogonal et le tester dur d’autres plans fractionnaires orthogonaux par exemple.
2) Réalité terrain
Il s’agit de détourer sur les images les obstacles et éléments de l’infrastructure (marquages, bords de route, etc) de manière à constituer une référence permettant de mesure la performance du système.
. Exemple de situations de vie :
1.1, été, temps couvert, route sans marquage, vitesse modérée, pneu sur la chaussée, temps sec
1.2, été, temps couvert, route sans marquage, vitesse modérée, colis sur la chaussée, temps sec
2.1, été, temps couvert, route sans marquage, vitesse modérée, piétons immobiles non embusqués au bords de la chaussée, temps
2.2, été, temps couvert, route sans marquage, vitesse modérée, humain allongé sur la chaussée, temps sec
etc …
Il n’est pas certain que l’on puisse rencontrer ces quelques cas, même en roulant 1 million de km sur route ouverte !
Objectif
NEXYAD démarre son recueil d’images et de données :
. vidéo (vers l’avant du véhicule) couleur
. accéléromètres
. gyromètres
Les fichiers sont synchronisés par l’outil RT-MAPS de la société INTEMPORA. INTEMPORA.
Les fichiers sont enregistrés au format RT-MAPS et directement rejouables par cet outil.
NEXYAD cherche actuellement des contributeurs sur ce projet interne. Les contributeurs co financent et ont en retour un accès gratuit à la base de données, illimité dans le temps. Cette contribution permettra d’accélérer le travail de recueil et d’étiquetage.
NEXYAD souhaite mettre à disposition cette base avant Juin 2016, de manière gratuite pour donner de la matière à la communauté des ADAS et du véhicule autonome, pour une version réduite de la base, et de manière payante (sous forme d’abonnements) pour la base complète.
L’ambition de NEXYAD est de propager son expertise méthodologique et de permettre à chacun d’évaluer les performances des systèmes de vision pour les ADAS, qu’il s’agisse des systèmes développés par NEXYAD, ou d’autres.
Références
“Methodology for ADAS Validation: Potential Contribution of Other Scientific Fields Which Have Already Answered the Same Questions”, Gérard Yahiaoui, Pierre Da Silva Dias, CESA congress Dec 2014, Paris, proc. Springer Verlag
“Methods and tools for ADAS validation”, Gérard Yahiaoui, Nicolas du Lac, Safetyweek congress, May 2015, Aschaffenburg
Contact
Pour toute question ou pour devenir un contributeur, contactez NEXYAD : +33 139041360
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