Tag Archive: Methodology Agenda

Nov
29

Newsletter #19 is now available

Nexyad at CES 2018 January 8-12
in Las Vegas

Headlines :

– 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

Go to the Nexyad Automotive & Transportation Newsletter #19

Nov
27

Nexyad at CES 2018 January 8-12
in Las Vegas

NEXYAD Automotive & Transportation Newsletter #19, November 29th, 2017

 


Nexyad at CES 2018 January 8-12 – Las Vegas

Headlines :

– 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

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Come to meet Nexyad at CES 2018 in Las Vegas (Jan 8-12)

CES 2018

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

CES 2018 East LVCC - Central Plaza Map

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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

Nexyad CEO at Cercle Lab

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 of Kap-Code
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.

Unesco AI Congress

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 :

Example of driving 1
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.

Click to read the entire article

See here 3 more use cases

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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 & 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.



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Nexyad present with Groupement ADAS at Equip’Auto 2017

Equip'Auto_Groupement ADAS

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

Etiquetage

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.

Click to read the entire article

References
Methodology for ADAS Validation: The 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

* * * * *



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.

Click to read the entire article


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SafetyNex can bring Artificial Intelligence into Autopilots
in respect of ASIL ISO 26262



SafetyNex integration in the ASIL 26262.
SafetyNex Asil 26262
© NEXYAD 2017

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

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.

Thierry Ernst
Thierry Ernst, CEO of Yogoko


Nov
14

Ground Reality with Nexyad Validation DataBase

EtiquetageFree space ground reality (for RoadNex) and obstacles ground reality (for ObstaNex)

NEXYAD has been developing a methodology called “AGENDA” (published in the 90’s by Gerard 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.


Oct
11

Validation of ADAS Nexyad Database

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 :
Validation of ADAS Nexyad database

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.

References
Methodology for ADAS Validation: The 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

Apr
03

Conference on Machine Learning organized by SNCF

Very interesting conference on Machine Learning and Deep Learning.
SNCF RESEAUX welcomed a large audience last tuesday 28th march in Saint Denis.

French experts of A.I. field were introduced by Claude Solard SNCF réseau Chief Operating Officer and Jean-Jacques Thomas Director of Innovation.
Christophe Garcia, Full professor at INSA Lyon, deputy director of the LIRIS laboratory, gave an introduction to Neural Networks and Artificial Intelligence and answered to the audience’s questions.
Then, Gérard Yahiaoui, CEO of Nexyad, Vice President of the MOV’EO Cluster, explained the Criteria for use and Methodology for implementation: the methodology AGENDA.

SNCF Conference Gerard Yahiaoui
Gérard Yahiaoui CEO of Nexyad


From CEA List, Jean-Marc Philippe presented “Tools of the Network to the Optimized System”. The conference continued with a roundtable about the SNCF Cafeine Project or how to go from the industrial need to the applied solutions in SNCF Réseau; animated by Alain Rivero, SNCF Réseau Director of Cafeine Project; Xavier Roy, Head of Innovation of ITNovem; Michel Paindavoine, co-founder of Global Sensing Technologies and professor at University of Bourgogne; and Jean-Jacques Thomas Director of innovation of SNCF Réseau.

The CAFEINE Project proposes to use the Artificial Neural Networks to detect the defects of pantographs on moving trains, and also to read the number of each wagons, oars and containers.

Another roundtable was organized about Startups and A.I. with Kunthirvy Collin-Dy, Head of relationships with Startups of SNCF Réseau Robot Lab; and Yannick Gérard, Head of Project of DAVI.
Patrick Bastard, Director of Driving Assistance Systems engineering of Renault, ended the conference with a presentation on an opening to other fields of application : vehicles electrification, ADAS and autonomous driving.

Conference SNCF 3 speakers
From the left : Jean-Marc Philippe, Patrick Bastard et Christophe Garcia

Dec
21

Autonomous Vehicle TEST & DEVELOPMENT Symposium 2016

Autonomous Vehicle TEST & DEVELOPMENT Symposium 2016
31 may – 2 june 2016 Stuttgart, Germany.

Test & Development Symposium Stuttgart 2016

2016 Preliminary Conference Programme

Wesnesday 1st June

09:15 – 15:45 – Test and Validation Strategies for Autonomous Vehicles
Room B

09:45 – Building a relevant validation database for camera-based ADAS
Gérard Yahiaoui, President and CEO, Nexyad, France
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.

http://www.autonomousvehiclesymposium.com/conf_overview.php

Sep
02

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.