Tag Archive: RT Maps

Apr
25

Nexyad at Active Safety Europe 2016 : ADAS to Autonomous

Actice Safety Europe 2016

Come to visit Nexyad the 17th & 18th may in Munich, Germany, for the Congress & Exhibition Active Safety Europe 2016 : ADAS to Autonomous.
Nexyad will be present with Intempora firm, provider of famous real time framework RT-Maps, Don’t miss the 18th : Software “Embeddibility”, Speaker Nicolas Dulac, CEO, Intempora.
Nexyad and Intempora are members of french cluster Groupement ADAS sponsored by MOVEO.

Apr
18

Many Thanks to all Nexyad booth visitors at Connected Car Insurance

Connected Car Insurance
You have been numerous to visit Nexyad Booth at Connected Car Insurance, London last week.
Our disruptive product SafetyNex benefited a lot of interest !
SafetyNex measures Risk of Accident and decreases Loss Costs. This software is available as a smartphone App and as a device.
More information about the ultimate solution for insurance companies that need onboard risk assessment : SafetyNex Position Paper
Version française

Apr
12

Nexyad & Intempora at Connected Car Insurance Europe

CCI London
Philippe from Intempora and Gérard from Nexyad

The salesforce for Nexyad SafetyNex at Connected Car Insurance, London Hilton Tower Bridge, currently and till wesnesday 13th april.

Mar
17

New Release of RoadNex v2.3 available in RT-MAPS 64bits

The new release of NEXYAD Road Detection RoadNex v2.3 is now available in RT-Maps 64bits (by Intempora).

RoadNex Desert Track
RoadNex detecting track in desert

Feb
03

RoadNex v2.2 in Online Store of Intempora

Nexyad RoadNex v2.2 is the only Road Detection module software operable in framework RT-Maps by Intempora.
It is possible to connect multiple sensors included cameras on RT-Maps, collecting data in real time and time-stampted them. Later play and replay video with all your data.

Intempora-RoadNex

Jan
25

Release v2.0 of Nexyad VisiNex Onboard

NEXYAD VisiNex Onboard v2.0 is now available on RT-Maps (by Intempora).
It comes with local Visual Quality Score (VQS) figured by colors :
Green is high, orange is medium, red is low.
See Demo film below on foggy weather condition :




Sep
15

SafetyNex for Onboard Road Safety Measurement

SafetyNex for Onboard Road Safety Measurement by NEXYAD

INTRODUCTION

Car manufacturers and insurance companies both need a system that would estimate in real time the risk taken by the driver.
Most commercial applications use to consider that a driver that do not accelerate much doesn’t take risk, and that a driver that drives more sporty is dangerous.
However, insurance companies statisticians could notice that there no correlation between the driving style and the accidents.
It is completely obvious : danger comes when the driving style is not adapted to the infrastructure. So driving style doesn’t has no meaning by itself.

NEXYAD company has been working since 1995 on onboard risk estimation, and recently launched their module SafetyNex that estimates a risk which is correlated (by construction) with accidents.

SafetyNex is the result of three collaborative French research programs :
. ARCOS
. SARI
. SERA

SafetyNex measures onboard the adequacy of driving style (and in particular the speed of the vehicle) with the characteristics of the infrastructure : adequacy of the current speed and initiated acceleration to the radii of curvature of bends downstream, to the presence of downstream crossings, or pedestrian crossings, … etc.

It is possible to add to SafetyNex optional inputs such as :
. weather report,
. maximum grip
. atmospheric visibility (rain, fog …)
. distance to obstacles (coming from an ADAS system) and in this case, we use not only infrastructure characteristics but also trafic flow information that describe the way other users move on the same infrastructure.

Similarly, can be integrated into SafetyNex data from characteristics of ADAS in order to measure the adequacy of these driver assistance systems to the situation experienced by the vehicle on the infrastructure.
For example, if the vehicle has radar or camera, the data of the opening angle enable SafetyNex (which read shape of the infrastructure from the onboard navigation map) to compute the distance of geometric visibility, not for the driver, but for embedded artificial perception systems.

Cone of Perception

EXAMPLE OF USE IN URBAN TRAFFIC

The example below shows the predictive nature of safetyNex : when you get in an intersection, it’s a little before that you must slow down because you can not know what is likely to emerge from this intersection. However, when one is in the intersection, it is not dangerous to re-accelerate. This is the way that safe drivers use to drive.

Therefore, the risk score is not correlated to the value of the deceleration or acceleration but to the adequacy of speed to potential dangers of the infrastructure. You may drive sporty or lazy and have the same good or bad safety score computed by SafetyNex.

Video with sound (spoken explanations)

CONCLUSION

SafetyNex is now available for sale and is operating in the following environments:
. Framework RT-MAPS PC : This version is for automakers researchers, scientists of tier one techno suppliers, statisticians and actuaries of insurance companies. It allows real-time replay, in order to see what areas make the risk climb, it also allows to correlate the new variable (risk) with all other variables available, and for car manufacturers, it lets develop ADAS based on this module.
RT-MAPS is interfaced with the Data Base Management Systems, which is convenient to apply SafetyNex on the company’s information systems.
. electronic device of an automotive tier one company : the announcement will be made soon by the automotive tier one techno supplier.
. mobile phones (December 2015), which will allow everyone to have this road safety module.

To know more : sales@nexyad.net

Sep
08

Validation Database New
Road Detection & Road Safety
NEXYAD tools for ADAS

NEXYAD Automotive & Transportation Newsletter #4, the 7th of September 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


*****



Road detection for ADAS and autonomous vehicle :
NEXYAD module RoadNex V2.1

A useful complement to markings detection

The detection of the road is a key element of driver assistance systems (ADAS) and autonomous vehicles.
Indeed, objects, obstacles, other road users, must be detected but also positioned relatively to the road.
The detection of the entire route, that is to say not only its markings or edges, but all the way, should enable
embedded intelligence to select appropriate action.

The company NEXYAD has been working on this issue for over 20 years without interruption, and has accumulated a large number of cases of road types, of coatings, in various atmospheric conditions.
This is to detect the rollable area on the road, without regard to, in a first step, lane markings.
Indeed, in Europe, there are many unmarked roads, and work on a marked road may change the markings and
make a « follow the markings strategy » dangerous.

In the images below you can see on the left a typical French countryside road with no markings, and on the right image, new markings was achieved while former markings still strongly visible.
Road without MarkingRoad with old and new Markings

These cases are quite common on our European roads and a driver assistance system, or a driving delegation
system, must at least understand such cases and if necessary tell the driver to cope with it by himself.

The NEXYAD road detection module, RoadNex V2.1 is a brick to go further to cope with these cases :
RoadNex V2.1

RoadNex V2.1 should be coupled with road signs detection, road markings detection, obstacle detection, in order to build an intelligent perception system. RoadNex is then a key module of such a system.

The road detection module NEXYAD, RoadNex V2.1 is available as a component into the asynchronous real time framework RT-MAPS : See HERE


*****



Road Safety for ADAS and autonomous vehicle :
NEXYAD module SafetyNex running as real-time component
of Framework RT-Maps

SafetyNex (safety level estimation for ADAS)
SafetyNex Onboard is a high level functional bloc (software) of safety measurement, taking into account map and GPS geolocation (shape of the road, crossing roads, … ahead), speed, accelerations, visibility, adherence, distance to obstacle, etc.
SafetyNex measures adaptation of the driving style to infrastructure topology, and possibly Dangerous situations.
Two main applications :
_ Car industry : intelligent Navigation system providing valuable advices to keep the car in a good level of safety; sending alarms on dangers
_ Insurance : driving style measurment correlated with accidentology (insurance pricing, Pay How You Drive)

SafetyNex is now running in RT-Maps by IMTEMPORA
SafetyNex is under fusion with Ecogyzer (eco driving rating system) : this “package” will be the ultimate tool for eco and safe driving combination.

SafetyNex V2.1
SafetyNex v2.1

Aug
24

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

May
19

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”.


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