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

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 ihas 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

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

NEXYAD member of VeDeCom

NEXYAD is now a member of the VeDeCoM research foundation as donator member.
NEXYAD is involved in research on ADAS and Autonomous Vehicle and is happy to contribute to the recruitment of young searchers by VeDeCom.

Groupement ADAS at SafetyWeek

INTEMPORA and NEXYAD showing their products together at safetyweek in Aschaffenburg (Germany).



Photo by Katrin Heyer



Photo by Katrin Heyer

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”.


Special announcement : the Nexyad software SafetyNex is being developed for RT-Maps of Intempora

SafetyNex is a high level functional bloc (sofware) for ADAS (Advanced Driver Assistance Systems) : onboard measurement of driving behaviour, taking into account map and GPS geolocation (shape of the road, crossing roads, … ahead), speed, accelerations, visibility, adherence, distance to obstacle, etc.

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Very soon on RT-Maps…

Another demo film of RoadNex V2.0 (by NEXYAD) module (road detection in front of a vehicle in urban traffic).

Another demo film of RoadNex V2.0 (by NEXYAD) module (road detection in front of a vehicle in urban traffic).

This module runs as a component of the framework RT-MAPS (by INTEMPORA), and can recognize the road with or without white lines : even European countryside roads are detected.

RoadNex may be used for developing ADAS (Advanced Driver Assistance Systems) and Autonomous Vehicles.

In this demo, RoadNex detects the road shape, illustrated by green lines layer, and the road surface illustrated by the red paint layer.

Notice the scooter that takes over, pushing the green lines and the red paint… It is rare to see demos where objects come over road markings.

You can see on this demo that RoadNex may be an efficient preprocessing system for obstacles detection.


RoadNex V2.0 (new film demo on a deep forest road without markings)

NEXYAD is proud to show a demo film of the RoadNex V2.0 (by NEXYAD) module (road detection in front of a vehicle).

This module runs as a component of the framework RT-MAPS (by INTEMPORA), and can recognize the road with or without white lines : even European countryside roads are detected.

RoadNex may be used for developing ADAS (Advanced Driver Assistance Systems) and Autonomous Vehicles.

In this demo, the road has got no markings at all, and is quite dark. RoadNex still works in this king of road.