NEXYAD Presentation at SIA CESA 5.0

The 5th and 6th december, International Conference SIA CESA 5.0 took place in Versailles (just near the Château).
The goal of the organisers is to build a bridge between traditional automotive electronics and the new developments in vehicle electrification and digitalization as well as those from the world of consumer electronics and the Internet of Things.
The event has presented a great opportunity to understand how the automotive business will evolve over the next five years, with a focus on products and services that are likely to transition from other markets into use-cases for automotive.

Gérard Yahiaoui, Nexyad CEO presented a new paper: Real Time Driving Risk Assessment for Onboard Accident Prevention :
Application to Vocal Driving Risk Assistant, ADAS, and Autonomous Driving.

Nexyad Conference at SIA CESA
Gérard Yahiaoui on the left

To read more

COMPARE YOUR AUTONOMOUS DRIVING SYSTEM TO BEST HUMAN DRIVERS IN TERMS OF DRIVING RISK TAKEN AT EVERY MOMENT

You want to measure the efficiency of your autonomous Driving system in terms of road safety ? Not easy with the regular validation methods : observing the number of km without accident is NOT the key. Indeed, accident is very rare for human driver anyway : on OCDE countries, 1 accident every 70 000 or 100 000 km (depending on the country), and on average 3 death every billion km !
We bring a way to build a metric between YOUR system and better human drivers … using our real time Driving risk assessment module SafetyNex.
A new solution for you to imagine validation process.

Human Vs Robot

Autonomous Vehicle Test & Development : second day

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.

Conference Stuttgart 2016
Gérard on the left, the conference’s audience on the top and the booth of Groupement ADAS from the mezzanine.

Gérard Yahiaoui, Nexyad CEO speaker at
Autonomous Vehicle – Test & Development Symposium 2016

STUTTGART 31 may – 2 june 2016

Test and Validation Strategies for Autonomous Vehicles Room B

june 1st – 09:30 – 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 and test) design.

ROBUSTNESS OF NEXYAD SOFTWARE MODULE
FOR ROAD DETECTION : RoadNex

ROBUSTNESS OF NEXYAD SOFTWARE MODULE FOR ROAD DETECTION : RoadNex

By NEXYAD

Detection of the road, detection of the lane, in front of the vehicle is now a « must-have »
for Advanced Driver Assistance Systems (ADAS) and of course for Autonomous Cars too.
Every R&D team is able to show cases of good detection. The difference between different
modules is robustness : ability to work in many cases (almost every cases).

For instance, robustness consideration led many big Automotive firms to interger the MOBILEYE
detection system : jus because MOBILEYE is more robust than detection systems developed by
those big firms. And robustness is not a matter of deployment : you won’t get a more robust
module is you put 10 000 developers on the project. You need time, big amount of data, and
« smart ideas ».

Note : This robustness definition leads to question on ADAS validation (« almost » every case is
not that well defined … how could we put some maths on those words). NEXYAD has been
developing an applied maths-based methodology for ADAS validation and is currently
recording a validation data base that will be soon available for free worldwide on the internet.

But let’s go back to road detection modules comparison.

There is another difference between road detection systems : do they need white markings
or are they able to work even without markings ?

NEXYAD founders has been working on road detection since the beginning of the 90’s and never
stopped (*). The NEXYAD team is one of the moste experienced team in the world about road detection.
That actually makes the difference, and RoadNex is a module that would take long to develop by
other teams. RoadNex is currently available on PC (windows, Linux) in the real time framework
RT-MAPS. RoadNex will be soon available :
. on electronic device of an Automotive Tier One Company
. on smartphones (so it works in real time on a smartphone usual processor ! try to compare to other modules)
(*) publication at a scientific congress in France in 1993 :
« Texture-based Image Segmentation for Road Recognition with Neural Networks », G. Yahiaoui, M. de Saint Blancard,
Sixth international conference on neural networks and their industrial & cognitive applications NeuroNîmes93, EC2,
Nîmes, 1993,

In order to have an idea of what robustness means, here are some case used to test RoadNex :
RoadNex01
RoadNex02
https://nexyad.net/Automotive-Transportation/wp-content/uploads/2015/11/RoadNex03.jpg
RoadNex04
RoadNex05
RoadNex06
RoadNex08
RoadNex07
RoadNex09
RoadNex10

How many kilometers should you drive to sample those few road scenes variations ?

For more information : https://nexyad.net/Automotive-Transportation/?page_id=412

Driving Delegation: key elements

Driving Delegation: key elements for an artificial perception system
Publication of September 2, 2015
Authors : Gérard YAHIAOUI & Pierre DA SILVA DIAS

INTRODUCTION
The automotive industry starts offering ADAS, and plans to propose in the near future partial or total driving delegation systems.

Main cases to be processed first may be:
. Highway driving, where the number of events per kilometer is small because the infrastructure has been designed to minimize path irregularities (little or no turns, every car in the same direction, wide track, geometric visibility up to several kilometers, enough little interactions between vehicles, at least when the traffic is flowing).
. The city, where infrastructure complexity is very large, where interactions between the road users are very strong, making detection a difficult tasks, but where speed of the vehicle is low.
In all cases, these future ADAS require developing advanced systems of perception.

ADVANCED PERCEPTION
Perception consist in detecting objects, clustering, and possibly tracking them in their own trajectory, from selected sensors (cameras, radar, lidar, slam, ultrasound, …)

It is usually presented as several phases :
. Detection: we perceive that « something » comes off the background, but we do not know what it this is. The Johnson criteria for detection give a theoretical limit of one period, or a minimum width of two pixels to detect a stationary object.
. segmentation and tracking: when zones are detected as being detached from the background (the landscape for image processing, the cluter for a radar, …), the detection must be agglomerated to track large enough objects that may have a meaning.
. Recognition: Recognition is to be able to say what it is. The Johnson criteria for human vision is about 6 periods (for stationnary objects) which gives 12 pixels.
. identification: identification gives, in the recognized class, the precise name of the object.

Detection is by far the most complex. It is potentially based on several principles:
. breakage hypothesis : we made a number of assumptions about world geography. We choose this hypothesis and make sure they are verified for the landscape (or cluter), and not for the objects to be detected. The non-validation of assumptions corresponds to a detection.
. the confrontation of a knowledge of the landscape or cluter: Comparing the « background » as it is supposed to appear in the absence of additional objects with said background which contains objects lead to detection of those objects.
. the knowledge of the shape of the objects to be detected: in this case the detection and pattern recognition are the same. System detects an object in its environment because it recognizes this object.
Human perception jointly implements the three principles.

Perceptions systems incorporate sensors and methods of processing, and are generally effective in a frame capture conditions, and little or not effective in the other frames. For example, a camera in the visible wavelenghts (and its image processing methods), will generally not be effective at night or in fog because « you can not see anything. »

PERFORMANCE, STRENGTH, RELIABILITY
No detection system can operate in any case when dealing with a real problem in the open world.

Designing a detection system then comprises two important phases:
. extend the maximum possible number of cases where the detection system works.
. have a diagnosis that allows to know when it is or when it is not in a position to that the perception system is effective.

We talk about performance (very efficient detection of all objects of interest), strength (number of cases where the collection system remains effective), and reliability (Situational Awareness in which one is and thus the confidence that can be placed in the collection system).
These three elements, performance, robustness, reliability, should be fully known in order to cooperate collection systems (for example, a camera and a radar).

NEXYAD proposed the Methodology AGENDA for characterizing life situations, using the formalism of orthogonal plans of experiments. The recognition of cases of functioning mode can be based on the description of life situations with this methodology. This gives a theoretical and practical framework for an estimation of robustness and reliability.
Performance is measured with statistical comparison operators: in general, it is considered the output of a detection system is a categorical variable with two categories: « detected » and « not detected ». This variable must be compared to a qualitative variable of reference that also has two modalities: « Presence of an object to
be detected » and « absence of objects to be detected. » The comparison can not be made by calculating a percentage (yet it is often that performance is measured this way), but it must use tools such as contingency table, the Khi2, normalized Khi2, khi2 in the box, etc …
To extend the life situations of the domain where the system detects objects correctly, we use to make cooperate several detection systems which use complementary types of sensors (eg in fog, we will trust in radar or infrared detection, but not detection by conventional camera).
A reliable system is one that is able to answer « I do not know »: in the case of driving delegation a system that could detect all objects so powerful, robust, and reliable in 30% of the time has a great value.
The delegation of driving frees 30% time of the driver, which is a real value proposition.

SAFETY OPERATION
Safety is a discipline that encompasses many issues with the objective of ensuring the proper functioning of the system in all cases.
In particular, we must be vigilant concerning detection systems which require to have several measurement channels, such as stereovision.
If detection works only when you can have both cameras, then safety experts refuse such a system because two cameras means 2 times more likely that one fails.
We then see that perception system must have quite still usable « degraded mode » when simulating glitches sensors. A good design of a perception system for ADAS incorporates all these elements.

SYNTHESIS
The race for performance that interests the engineers is rarely the real issue in industrial systems. A system that allows to delegate the driving in 30% of cases (eg clear overcast day dry) and « knows » when there is a case for which it works or does not work, can delegate driving and release the driver for 30% of the time.
This is a proposal for a very high value for the driver.
A system that works effectively in 99% of cases without knowing precisely when it works is absolutely unusable. No manufacturer will put such a system in operation for road safety applications.
The company NEXYAD has been working on these issues for twenty years, especially on road detection, obstacles detection, measurement of visibility (to describe cases where the detection is reliable, for example), the estimation of road safety (suitability driving style with the infrastructure).

NEXYAD developed:
. efficient and very robust basic bricks: RoadNex, ObstaNex, VisiNex onboard, SafetyNex
. a methodology for characterizing life situations in which it develops and tests an ADAS: AGENDA (Improvement performance, the recognition of cases of good performance, and validation of ADAS).
. know-how in collaboration between multiple perception systems.

Validation database for camera-based ADAS

Version française plus bas

NEXYAD Automotive & Transportation Newsletter #4, the 26th of August 2015



Validation database for camera-based ADAS

The company NEXYAD started building a database for validation of advanced driver assistance systems (ADAS and Autonomous car) using the methodology AGENDA published in the 90 by Gérard Yahiaoui (methodology initially developped for control construction of learning and test databases for the implementation of artificial neural networks).
This database has two essential characteristics:

1) Known life situations
Indeed, the methodology AGENDA proposes to describe potential changes of signals and images came into factors of variability and their crosses.
Example, for obstacle detection :
   . weather (dry overcast, sunny weather, rain, fog)
   . overall brightness (low, medium, high)
   . speed of the carrier vehicle (low, moderate, high)
   . type of road (highway, road with marking, road without marking …)
   . coating (bitumen 1, bitumen 2, …, cobblestones)
   . day / night (headlights and the lights switched infrastructure)
   . season (spring, summer, autumn, winter)
   . etc …

      > type of obstacle :
           – stopped
                      . infrastructure-related: work terminals, tolls, …
                      . related users: tire on the road, parcel felt from a truck lying on the road, biker following a road                       accident, disabled vehicle stopped on the floor, standing pedestrian on roadside edge (dodger /                       no sniper)
           – moving
                      . truck, car, vulnerable (pedestrian, bicycle, motorcycle) each with types trajectories (longitudinal
                      in rolling direction, longitudinally in the opposite direction of rolling side) and position (opposite
                      to right, left).
                      . Etc…

We see that if we cross these factors, we find fairly quickly a huge number of cases. However, the development of ADAS systems is complex, and it is necessary to proceed by successive iterations, starting from simple situations to move to complicated situations.
Our database allows this, since all records are described in terms of crossing the terms of the factors of variability. Thus knows exactly which cases were tested or not by the system.
Formalism ‘crossing of variability factors of the terms’ allows using design of experiments, and in particular orthogonal fractional plans to sharply reduce the number of cases to be tested while ensuring maximum coverage of life situations. One can in this context to develop a fractional ADAS on an orthogonal plan and test other hard fractional orthogonal planes for example.

2) Reality reference
This is to crop images barriers and infrastructure elements (markings, roadsides, etc.) so as to constitute a reference to measure system performance.

. Examples of life situations:
Life Situations


1.1, summer, overcast, unmarked road, moderate speed tire on the floor, dry weather
1.2, summer, overcast, unmarked road, moderate speed, parcels on the floor, dry weather
2.1, summer, overcast , unmarked road, moderate speed, standing pedestrians non ambush at the edges of the floor, dry weather
2.2, summer, overcast, unmarked road, moderate speed, lying on the floor human, dry weather
etc …

Not sure that you would meet those few cases, even with on million kilometers on open roads.



Our Goal

NEXYAD starts his collection of images and data:
      . video (towards the front of the vehicle) Color
      . accelerometers
      . gyros

The files are synchronized by RT-MAPS tool INTEMPORA society.
The files are saved as RT-MAPS format and replayable directly by this tool.

NEXYAD currently looking for contributors on this internal project. Co contributors fund and in return free access to the database, unlimited in time. This contribution will accelerate the work of collecting and labeling.
NEXYAD wishes to provide this basis before June 2016, free way to give the material to the community and the ADAS autonomous vehicle for a smaller version of the database, and pay way (as subscriptions) for complete database.
NEXYAD’s ambition is to spread its methodological expertise and allow everyone to assess the performance of vision systems for ADAS, whether systems developed by NEXYAD, or others.

References
“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
For questions, or if you wish to become a contributor, please contact NEXYAD : +33 139041360

Base de données de validation des ADAS utilisant des caméras

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 :
Life Situations


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

Presentations of NEXYAD at SafetyWeek in Germany

At the safety week symposium in Aschaffenburg (Germany), NEXYAD presented :

. A paper about ADAS validation : methodology and tools
. The products on the booth : RoadNex (road detection), ObstaNex (Obstacles detection),
VisiNex Onboard (visibility measurement), SafetyNex (estimating safety level of driving).





NEXYAD at the Safety Week in Germany

NEXYAD has got a booth at the Safety Week symposium in Aschaffenburg in Germany from may 19th to 21st showing the module RoadNex (road detection), ObstaNex (obstacle detection) running in the real time environment RT-MAPS, and available for customers that want to quickly develop an autonomous vehicle/demo car. Those modules are under shifting to smart phones and electronic devices.

NEXYAD also presents a paper written with the company INTEMPORA, about ADAS validation methodology and tools.

NEXYAD is member of the “Groupement ADAS”.


Proceedings of the 2014 CESA

General Information

Nexyad was at the CESA Congress (organized by sia) on 3 and 4 December 2014.

Our presence was at three levels:
. Participation in the round table on the development of ADAS
. Publication on a methodology for validating adas (ADAS validation)
. Presence on the booth ADAS Mov’eo Group

The congres was full of contributions, and main important points we noticed are:

. Emergence of new business models opportunities for high-tech SMEs technology in the world of ADAS
. Strong awareness of the need for ADAS test and validation methodology
. Emergence of highly structured sensors strategies for classifying

ADAS in terms of features, cost, operating range, robustness, …

. Round table

Participants in the roundtable were:
. Maria Belen Aranda Colas, Robert Bosch
. Ching-Yao Chang, University of Berkeley
. Thierry Lehay, PSA Peugeot Citroën
. Patrice Reilhac Valeo
. Gabriel Toffolo, Renault
. Gérard Yahiaoui Nexyad

Questions focused on the need for standardization of HMI and on the need to create new job profiles and competences to effectively develop ADAS and autonomous vehicle, and also on the opportunity for SMEs to find new business models in this ADAS field. Also mentioned the need for standards methods and tools For ADAS validation. Nexyad exposed the opportunity for SMEs to use the nomadic systems of image acquisition, computing and storage (smart phones). these new devices make it possible for SMEs to imagine B2C applications.

. Publication of Nexyad

Nexyad presented the AGENDA methodology that was originally developed in the 90 to specify learning and test data bases for training and validation of artificial neural networks applications.

It appears that this methodology is completely adapted to the specification of ADAS validation
databases.

Nexyad is also currently integrating this methodology in an upcoming tool that will send host such a database and easily manage all crossings of road scenes variability factors (work in collaboration with the SMEs Intempora and Civitec).

During the same session other organizations (Daimler for instance) also presented very interesting for the validation of ADAS.

The proliferation of interesting ideas on this key issue is rather good news and suggests a significant advance in the near future.

Compte rendu du CESA 2014

Information générale

Nexyad était sur le congrès CESA (organisé par la sia) les 3 et 4 décembre 2014.

Notre présence était sur trois plans :

. participation à la table ronde sur l’évolution des ADAS
. publication sur une méthodologie de validation des ADAS
. présence sur le stand groupé du Groupement ADAS de Mov’eo

Le salon a été riche en contributions, et les points importants que nous avons notés ou communiqués sont:

. l’émergence de nouvelles opportunités de business models pour les PME de haute technologie dans le monde des ADAS

. la prise de conscience forte de la nécessité de disposer de méthodologie de test et de validation des systèmes ADAS et des fonctionnalités de délégation de conduite.

. l’émergence de stratégies capteurs très structurées permettant de les classer en termes de fonctions ADAS, de coûts, de distance de fonctionnement, de robustesse, …

. Table ronde :

Les participants à cette table ronde étaient :
. maria Belen Aranda Colas, Robert Bosch
. Ching-Yao Chang, University of Berkeley
. Thierry Lehay, PSA peugeot Citroën
. Patrice Reilhac, Valeo
. Gabriel Toffolo, Renault
. Gérard Yahiaoui, Nexyad

Les questions ont porté sur la nécessité de standardisation des IHM, sur les nouveaux métiers à créer pour développer efficacement les ADAS et le véhicule autonome, et sur l’opportunité pour les PME de trouver de nouveaux business models dans ce paysage des ADAS. A aussi été mentionné la nécessité de disposer de moyens standards de validation des ADAS. Nexyad a exposé l’opportunité pour les PME que constituent les moyens nomades d’acquisition d’images, de calcul, et de stockage (smart phone). Ces moyens nouveaux permettent en effet aux PME d’imaginer des applications B2C.

. Publication de Nexyad :

Nexyad a présenté la méthodologie AGENDA qui avait initialement été développée dans les années 90 pour spécifier les bases de données d’apprentissage et de test des réseaux de neurones artificiels. Il apparaît que cette méthodologie soit tout à fait adaptée à la spécification des bases de données de validation des ADAS.

Nexyad est d’ailleurs en train d’intégrer cette méthodologie dans un futur outil qui permettra d’adresser une telle base de données et de gérer simplement tous les croisements de facteurs de variabilité des scènes routière (en collaboration entre autre, avec les PME Intempora et Civitec).

La session a permis à d’autres organisations (dont Daimler) de présenter des approches très intéressantes pour la validation des ADAS.

Le foisonnement d’idées complémentaires sur ce sujet clé est plutôt une très bonne nouvelle et laisse présager d’une avancée significative dans un avenir proche.