Among the few companies that worked on ADAS and Autonomous Vehicle since a long tima, NEXYAD is one that have the most cumulated experience.
1993 : scientific paper by Gerard Yahiaoui (Founder of NEXYAD with Pierre Da Silva Dias) with a researcher of the Automotive Company PSA Peugeot Citroën : « 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,
2007 : participation of NEXYAD to the DARPA Challenge (Autonomous Vehicle) in the team « Blue Froggy » (with INRIA and INDUCT).
2016 : road detection, obstacle detection, road safety estimation, visibility measurement, available on smartphones (in March).
Experience matters !
What your company was doing in 1993 in the field of ADAS ?
What your company was doing in 2007 about Autonomous Cars ?
NEXYAD accumulated work on artificial vision based ADAS since more than 20 years, even when nobody believed that one day it will be camera inside cars.
Les Compagnies d’Assurances, les Constructeurs Automobiles et leur équipementiers ont tous des raisons pour mesurer le comportement du conducteur. Cela a déjà commencé, avec les boîtiers des assureurs dans le cadre du fameux « pay how you drive » : l’idée des compagnies d’assurance automobile est de moduler les tarifs en fonction de la dangerosité des conduites : un boîter embarqué dans le véhicule (ou une App sur le smartphone du conducteur) note le style de conduite (en utilisant les accéléromètres intégrés dans l’électronique), et lui attribue un degré de risque d’accidents.
Or, le style de conduite (exemple : freinages sévères, ou au contraire conduite coulée) n’est pas corrélé au risque d’accidents. Cela a été démontré formellement au travers de programmes de recherche collaborative (en particulier dans le cadre du programme national PREDIT sur la Sécurité Routière), et cela correspond aussi au « bon sens » : quelqu’un qui grille un stop sans faire de freinage sévère, sans accélérer comme un fou, tranquillement, à 40 km/h, est extrêmement dangereux et son comportement de conduite est fortement accidentogène, alors qu’il conduit « calmement ».
De même quelqu’un qui ne tient absolument pas compte des passages piétons (y compris lorsqu’un piéton est engagé) et le passent tranquillement sans freiner on un comportement, d’une part interdit par le code de la route, et d’autre part très accidentogène (avec des accidents mortels car il s’agit d’accident « véhicule vs vulnérable »).
Tous les boîtiers automobiles actuels qui utilisent le style de conduite comme clé de dangerosité sont donc absolument inefficaces et remontent une information fausse.
Tant qu’on ne rapproche pas le style de conduite du contexte géométrique, géographique, météo, état du conducteur etc …, on ne peux absolument rien conclure d’un style de conduite, à part pour la partie dite « Eco » (plus on conduit brutalement, moins on conserve l’inertie du véhicule, plus on consomme de carburant, et donc plus on rejette aussi de CO2).
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 :
How many kilometers should you drive to sample those few road scenes variations ?
Nexyad RoadNex v2.2 – Road Detection on little road in Forest
RoadNex detecting the lane on the road with or without markings.
The green Arrow shows the possible direction to follow.
Nexyad provides modules for ADAS (Advanced Driver Assistance Systems) : some of those modules such as RoadNex road detection or ObstaNex obstacles detection are competitors of the famous modules of the company Mobileye.
Gérard Yahiaoui will present for NEXYAD a paper about Validation of ADAS at the next Autonomous Vehicle Test & Development Symposium 31 may – 2 June 2016 in Stuttgart, Germany.
Save the dates and come to visit the Nexyad booth at the Stuttgart Messe. http://www.autonomousvehiclesymposium.com/
NEXYAD RoadNex v2.2 Road Detection for ADAS : Green Desert Track
RoadNex detects the lane and the road surface and does not depend on the level of equipment of road infrastructure. Example here, on a dirt track in the middle of nowhere…
Nexyad Automotive & Transportation has launched the blog NEXYAD-ADAS Solutions.
As the name says, it’s all about Advanced Driver Assistance Systems solutions.
RoadNex v2.2 Road Detection for ADAS : Case of Red Road
RoadNex detecting the lane and the surface of a road in red color.
Nexyad provides modules for ADAS (Advanced Driver Assistance Systems) : some of those modules such as RoadNex road detection or ObstaNex obstacles detection are competitors of the famous modules of the company Mobileye.
NEXYAD has been starting the development of a data base for artificial vision-based ADAS test and validation.
This data base will be relevant and unique because it is fully decribed in two ways :
. reality : position of road and obstacles
. driving situation (i.e. curve in a foggy weather with pedestrian crossing, …) using the methology AGENDA.
To read more :
– « Methodology for ADAS validation: Potential Contribution of other Scientific Fields which have already answered the Same Questions »,
G. Yahiaoui, P. Da Silva Dias, proceedings of the 3rd CESA Automotive Electronics Congress May 2014 Paris, Lecture Notes in Mobility,
ENERGY CONSUMPTION AND AUTONOMOUS DRIVING, Jochen Langheim Ed, Springer, pp 133-138.
– « Validation of Advanced Driving Assistance Systems », G. Yahiaoui, N. du Lac, SafetyWeek congress, Aschaffenburg, May 2015.
Business details :
A part of this data base will be soon available for free on the internet, to the worldwide ADAS and Autonomous
vehicle community (labos and firms).
If you wish to receive the link as soon as it is available, click HERE and fill the registration form.
(Free access to the NEXYAD Artificial Vision-based ADAS Validation Database)
The complete data base should be available soon through an annual membership.
For more information : contact NEXYAD Olivier BENEL +33 139 04 13 60
* * * * *
ITS World Congress in Bordeaux
From 5 to 9 October, the ITS World Congress held in Bordeaux.
It is the world’s largest gathering on the subject of intelligent transport systems and numerous corporations and government agencies were present to discuss new technologies, communication and robotics which every day are revolutionizing commercial road transport, individual and collective.
From our point of view, three main domains distinguished themselves which are however increasingly closely intertwined: the vehicle, the infrastructure and information.
The most spectacular of them represented by the many autonomous vehicles that lined the stands or went in demo mode near the fairgrounds.
We noted a trend of convergence between the connected vehicle on one hand and adas in the other hand, all autonomous vehicles were also connected vehicles and communicating vehicles : car to x and x to car (especially radio link with fires and road signs.)
The AKKA link in city car without driver of the French IT company has rolled around the lake so the city. This electric car is the result of a call for projects from the agency for the development and innovation of the Aquitaine region and it uses open data from the city of Bordeaux and its neighbourhoods.
From Renault, we saw the Next Two a piloted car remotely by a tablet. It parked all alone with no one behind the wheel. We liked particulary that this functionality avoids a walking pedestrian crossing in front of the car. Then it returns to the starting point when you recall it, always from the tablet.
Moveo-Groupement ADAS is a cluster of 8 french SME’s that put their competence in common to develop autonomous car. They showed on their booth the demo car that has been made for ENSIAME University of Valenciennes, entirely robotised by FH Electronics. Nexyad designed the eyes of this car with his vision-based road detection system RoadNex that runs on the framework RT-Maps from Intempora. The other companies showed demos, products, and competence on detection, pattern recognition, eco-driving measurement, human factors, advanced engineering for autonomous cars.
PSA Peugeot Citröen showed on its stand several R & D results in progress (for example, a work in collaboration with Nexyad : a plateform for simulation Car Easy Apps or CASA) Several autonomous vehicles rolled in urban circuit.
VeDeCoM presented four autonomous vehicles driving around the lake near the Congress place on a 7km open track. These demonstrators, which are dual-mode vehicles (manual driving and driving delegation level 4), combine the French expertise, derived from the public-private partnership research on the autonomous vehicle. We appreciated a lot the capacity of those demo cars to pass all the difficulties of the city, including roundabouts with traffic which are one of the key problems of the automated driving.
Smartlane opens up your data silos and allows you to create a secure, accessible and integrated data hub. In this way your own data are carefully combined with external sources in order to provide comprehensive information value.
Valeo came also with an autonomous vehicle in demo on the road of Bordeaux : the Valeo Cruiser4U fitted with the valeo laser scanner and the valeo camera that uses Mobileye processor. This car was designed to scale in urban and suburban driving, it can change lane, reaching 130km/h.
Nexyad was present on two booth at ITS. « Moveo Groupement ADAS » one showing innovative technologies of perception with a suite of software modules RoadNex, single camera based detection of sides of the road and detection of the surface of the road ; ObstaNex, single camera based detection of obstacles on the road and on the sides of the road ; VisiNex onboard, camera based measurement of the visibility ; and SafetyNex, a world unique tool to estimate the risk/safe in driving 100% correlated with accidentology. Nexyad was also present on the PSA Peugeot Citröen booth with the FUI, Moveo labelised, research program CASA.
Citilog showed his incidents detection system on motorways, and management of intersections in cities based on proprietary camera technology. Citilog was on Moveo Groupement ITS Infra booth with other SME’s.
ST Microelectronics showcased next generation technologies for automotive applications, with a range of solutions including telematics, positioning, ADAS, digital radio, and sensors. We discovered his partner AutoTalks the pioneer and leader of the V2X Technology.
TomTom makes his navigation more and more precise and efficient. They have fully mapped in 3D the roads of Germany to render, in the future, automated driving possible, and they will do the rest of Europe before the world, they say.
Navya is an electric shuttle 100% French without driver that moves at low speed through an embedded robot and multi-sensor system. Designed for urban mobility, first for closed sites and latter for the first or the last kilometer of a journey, it can accommodate up to twenty passengers safely. Demo on a course in the city of Bordeaux.
Atlatec makes ground reality which is very valuable for validation of ADAS. Put their box in your car, calibrate it and run. Then it stores a mass of data and the software creates automatically high precision 3D maps of the environment with high resolution top view of the road.
Here presented high definition maps combined with cloud technology. The leader of navigation brings to the driver, real-time location experiences through of a broad range of connected devices from smartphones, tablets to wearables and vehicles; and always more informations like road surface horizon (slope/cant track).
On the ITS World Congress we could feel very clearly that car manufacturers, Tier One and Tier Two Companies, stakeholders in mobility, in general, (including many SMEs) have heavily invested on ADAS and autonomous vehicles. It leads to a multitude of very advanced exhibitions, and present or future availability of high performance sensors at low costs, with associated signal processing, which are also mature.
Nexyad tries Autonomous Vehicle by VeDeCom at ITS World in Bordeaux
* * * * *
Using Nexyad ADAS Modules for Autonomous Vehicle and Safety/Risk Estimation
by NEXYAD
INTRODUCTION
The company NEXYAD developped software modules for Advanced Driver Assistance Systems :
. RoadNex (Road detection) : lane detection, detection of the borderlines of drivable area in the lane, detection of the surface of drivable area in the lane.
Sensor : camera (color)
. ObstaNex (Obstacles detection) : obstacles detection (if they have a vertical dimension or – inclusive – if they have their own movement)
Sensor : camera (N&B or color), accel, gyro
. VisiNex onboard (weather visibility measurement) : visibility measurement (quality and distance)
Sensor : camera
. SafetyNex : onboard road safety / risk estimation
Sensor : navigation map, gps, accel or car speed
Those modules were made to develop very efficient ADAS.
There are many ways of comining those modules, depending on the function that should be developped.
LANE KEEPING AND AUTOMATIC BRAKING : FOR CAR MANUFACTURERS AND TIER ONE COMPANIES
For this function, modules may be integrated in a rather complex way :
Such an application needs to know where it works and where it doesn’t work (reliability). For that, VisiNex helps because it measures weather visibility and the nit is possible to know in which context artificial vision algorithms are efficient or not. It is also possible to switch setting parameters of artificial vision based algorithms using visibility characteristics, in order to expand the range of good performance of the global system (this is robustness).
NEXYAD applies a validation methodology called AGENDA (see papers in CESA Automotive 2014 in Paris and in SATETYWEEK 2015 in Aschaffenburg). This methodology is the onlt approach that allows to know what the system is supposed to do in a functional point of view, with measurable characterisctics of road scenes.
NEXYAD of course uses the NEXYAD ADAS validation data base : a part of this validation data base for artificial vision-based ADAS will be soon online for free (usable by every researcher or engineer in the world).
Note : the AGENDA methodology also provides a method to measure the similarity of a road scene in the validation data base anda current road scene : this is applied to estimate a confidence score.
SAFETY / RISK ESTIMATION FOR INSURANCE COMPANIES
SafetyNex measures the adequation of driving to road infrastructure characteristics.
It generates then a risk if the driver goes too fast when approaching a crossing road or a dangerous curve.
Of course, a poor visibility should lead the driver to drive slower.
In addition, there could be auxiliary inputs that would tell SafetyNex if there are obstacles on the pathway :
This scheme is the same than the previous one but the outputs of RoadNex and ObstaNex are used INSIDE the scheme (they don’t provide an output of the global scheme).
Measuring road safety in the context experienced by the driver is a topic of interest for several activities :
. car manufacturers, who can inform the driver of potential dangers
. autonomous vehicle developers who need to prove that the driving actually minimizes risk of accident.
. fleet managers and insurance companies who wish to measure the risk taken by drivers (how they drive)
. managers of road infrastructure that alway change infrastructure to adapt and lower the risk of accident
The company NEXYAD has been developing since 2001 an embedded onboard module, SafetyNex, to
estimate in real time the risk of accident.
PRECONCEIVED IDEAS ABOUT ROAD ACCIDENTS AND DRIVING STYLE
Many trials have been completed or in progress, particularly by insurance companies and fleet managers,
In order to measure what is called the « driving style ».
The assumption is that some drivers are more « nervous » than others, and that this has an impact on the accident: those that speed up or slow down quite often brutally would be « bad drivers » while those with a quieter driving style would be « good » drivers.
This assumption is contradicted by the facts. There is no statistical connection between the driving style and
the accident.
Formally, one can easily fancy very well that if a driver operates very quietly, without slowing, without
accelerating at 30 km / h, and if this driver passes through a stop road sign without braking … then the driving style is quiet but very accident-prone.
We then see that beyond the possible statistical link (that doesn’t exist), there can be no relationship of cause and effect.
All experiments that were conducted led to this result.
All those that will be conducted, based on more or less intelligent thresholding of the acceleration values are doomed to failure.
Do not confuse eco-driving and safe driving.
Driving style cannot be interpreted itself without context description :
. infrastructure shape and characteristic, on which the vehicle is traveling
. traffic (presence of other road users)
. weather conditions (visibility, grip, …)
. level of driver vigilance (distraction, drowsiness, sleep …)
NEXYAD has developed a scalable solution capable of taking into account all these factors.
SafetyNex is therefore able to estimate the risk of driving using all those variables.
Version 2.1 of SafetyNex, under deployment, takes into account the adequacy of driving style with the type and shape of infrastructure (breaks on route characteristics, turns, pedestrian crossings, intersections …).
This version has been intentionally reduced to « driving style vs infratructure characteristics », because it already gives a 90% correlation with accident and because this version is deployable at very low cost:
. on smart phone
. electronic device (developed by an automotive tier one company), without using the OBD socket)
CORRELATION OF RISK OF ACCIDENT ESTIMATED BY SafetyNex V2.1 AND ACCIDENT
NEXYAD participated in collaborative research programs since 2001, and worked then with experts from the road equipment.
In particular, SARI research program led to detecting what experts call « Break on the route characteristics ». For example, a turn with a big curve may be a danger when it arrives behind a long straight line, while the same curve will not be dangerous bend on a mountain road.
NEXYAD published a paper at the conference on road safety May 6, 2010 in Paris: PRAC 2010
Risk Prevention and Save The Conduct, Session 1 Characterization of road risk vs. infrastructure
« Evaluation du risque routier pour l’aide à la conduite ou le diagnostic de l’infrastructure », Johann Brunet, Pierre Da Silva Dias, Gérard Yahiaoui, PRAC 2010, Mai 2010, Paris.
The work that led to this publication were integrated in the available product SafetyNex. This means that by construction, the risk estimated by SafetyNex is correlated to the accident. This is true by construction, and NEXYAD conducted tests on roads, downtown, on motorways in urban areas, etc … and was able to validate this result.
PRINCIPLE OF SafetyNex V2.1
SafetyNet is a knowledge based system (expert system) which applies rules of the experts of the equipment.
These rules are stored in a rules data base in a mathematical form that can adapt to gradual actual characteristics of the infrastructure.
Required inputs are :
. the navigation map and the GPS: To examine the shape and type of the infrastructure located downstream of the vehicle (turns with their radius of curvature, points of interest like pedestrian crossing, crossroads, etc …)
. the instantaneous vehicle speed
From these two inputs, SafetyNex evaluates, by applying the rules, the adequacy of the driving speed of the vehicle to difficulty and danger of infrastructure.
A sporty driver accelerating hard, braking hard, but passing dangerous places at low speed will be scored with a low risk.
A quiet driver that passes through a stop road sing at 30 km / h without braking will be scored with a high risk.
A brutal braking cannot be considered as « bad driving » if it is necessary to avoid an accident …
We see then, that SafetyNex risk estimation is not correlated with the absolute value of acceleration, but with ACTUAL speed adaptation to difficulty and danger of the infrastructure, in real time.
Additional inputs (optional) are already scheduled, and can afford to modulate the estimated risk to increase acuracy of SafetyNex :
. grip (if one has a sensor to connect to the input provided for the purpose of SafetyNex)
. weather report (if one has the temporal and spatial information)
. atmospheric visibility (if one has adequate measure: example: a camera and the measuring module of atmospheric visibility : VisiNex)
. distance to potential obstacles (if it has an adequate sensor : eg radar, lidar, or camera with RoadNexObstaNex modules)
. a driver distraction factor (if the driver is observed with a camera and / or if one monitors the activity of mobile phone, etc …)
All these additional inputs are already ready to be used by SafetyNex but of course, they increase the cost of deployment, involving sensors (camera, …) and additional computing power before getting in SafetyNex to process signals and images from the optional sensors.
Using SafetyNex V2.1 with only the required inputs already allows a very high correlation of the estimated risk with the accident. We recommend to implement this version, already infinitely more effective than any other onboard measurements.
The interest of SafetyNex is that the future is already assured: Moore’s Law by rapidly lowering the cost of electronics and embedded computing, SafetyNex is ready to process the additional inputs, when users want to integrate cameras and sensors.
TYPICAL USES OF SafetyNet V2.1
. Insurance Companies:
– Pay how you drive
– Predictive modeling of bonus malus: the same accident under the same conditions does not lead to the same conclusions based on accumulated historical and recording the last seconds risk SafetyNex
– Generation of a dumb risk variable, correlated to the accident, to help actuaries refine pricing (big data)
. Fleet managers
. Automotive equipment suppliers:
– Alarm on risk
– Intelligent Navigation able to advise the driver
. Engineers and researchers from autonomous vehicle:
– Driving Quality Assessment generated by the robot
CONCLUSION
Embedded estimation of road risk of accident is now a problem completely solved by a product available for deployment, SafetyNex.
SafetyNex is deployable at Low cost on:
. mobile phones
. electronic device of a Automotive Tier 1 supplier (without plugging the OBD).
And SafetyNex already planned to integrate (once the cost is acceptable) grip sensors and cameras (for example) to estimate traffic and atmospheric visibility, as well as information such as weather and driver distraction.
All of these are already processed by SafetyNex rules based system, so that the tool can quickly evoluate with each decrease in the cost of sensor elements and cost of computing power needed to compute sensors outputs.
* * * * *
NEXYAD Automotive & Transportation in Media
« when the smartphone becomes a lookout driver »
« Autonomous car is a dream the French Automotive sector »
NEXYAD was compared to Mobileye and considered as a serious player in the competition.
In french magazine Le Journal de l’Automobile, pp 52-54, 18 Sept 2015
« MobilEye a de la concurrence : longtemps en position monopolistique, la société israélienne a désormais un
concurrent qui s’annonce sérieux dans le domaine des algorithmes de gestion des caméras embarquées, Nexyad.
Rencontre avec les ingénieurs français qui pourraient changer la donne »
Measuring road safety in the context experienced by the driver is a topic of interest for several activities :
. car manufacturers, who can inform the driver of potential dangers
. autonomous vehicle developers who need to prove that the driving actually minimizes risk of accident.
. fleet managers and insurance companies who wish to measure the risk taken by drivers (how they drive)
. managers of road infrastructure that alway change infrastructure to adapt and lower the risk of accident
The company NEXYAD has been developing since 2001 an embedded onboard module, SafetyNex, to
estimate in real time the risk of accident.
PRECONCEIVED IDEAS ABOUT ROAD ACCIDENTS AND DRIVING STYLE
Many trials have been completed or in progress, particularly by insurance companies and fleet managers,
In order to measure what is called the « driving style ».
The assumption is that some drivers are more « nervous » than others, and that this has an impact on the accident: those that speed up or slow down quite often brutally would be « bad drivers » while those with a quieter driving style would be « good » drivers.
This assumption is contradicted by the facts. There is no statistical connection between the driving style and
the accident.
Formally, one can easily fancy very well that if a driver operates very quietly, without slowing, without
accelerating at 30 km / h, and if this driver passes through a stop road sign without braking … then the driving style is quiet but very accident-prone.
We then see that beyond the possible statistical link (that doesn’t exist), there can be no relationship of cause and effect.
All experiments that were conducted led to this result.
All those that will be conducted, based on more or less intelligent thresholding of the acceleration values are doomed to failure.
Do not confuse eco-driving and safe driving.
Driving style cannot be interpreted itself without context description :
. infrastructure shape and characteristic, on which the vehicle is traveling
. traffic (presence of other road users)
. weather conditions (visibility, grip, …)
. level of driver vigilance (distraction, drowsiness, sleep …)
NEXYAD has developed a scalable solution capable of taking into account all these factors.
SafetyNex is therefore able to estimate the risk of driving using all those variables.
Version 2.1 of SafetyNex, under deployment, takes into account the adequacy of driving style with the type and shape of infrastructure (breaks on route characteristics, turns, pedestrian crossings, intersections …).
This version has been intentionally reduced to « driving style vs infratructure characteristics », because it already gives a 90% correlation with accident and because this version is deployable at very low cost:
. on smart phone
. electronic device (developed by an automotive tier one company), without using the OBD socket)
CORRELATION OF RISK OF ACCIDENT ESTIMATED BY SafetyNex V2.1 AND ACCIDENT
NEXYAD participated in collaborative research programs since 2001, and worked then with experts from the road equipment.
In particular, SARI research program led to detecting what experts call « Break on the route characteristics ». For example, a turn with a big curve may be a danger when it arrives behind a long straight line, while the same curve will not be dangerous bend on a mountain road.
NEXYAD published a paper at the conference on road safety May 6, 2010 in Paris: PRAC 2010
Risk Prevention and Save The Conduct, Session 1 Characterization of road risk vs. infrastructure
« Evaluation du risque routier pour l’aide à la conduite ou le diagnostic de l’infrastructure », Johann Brunet, Pierre Da Silva Dias, Gérard Yahiaoui, PRAC 2010, Mai 2010, Paris.
The work that led to this publication were integrated in the available product SafetyNex. This means that by construction, the risk estimated by SafetyNex is correlated to the accident. This is true by construction, and NEXYAD conducted tests on roads, downtown, on motorways in urban areas, etc … and was able to validate this result.
PRINCIPLE OF SafetyNex V2.1
SafetyNet is a knowledge based system (expert system) which applies rules of the experts of the equipment.
These rules are stored in a rules data base in a mathematical form that can adapt to gradual actual characteristics of the infrastructure.
Required inputs are :
. the navigation map and the GPS: To examine the shape and type of the infrastructure located downstream of the vehicle (turns with their radius of curvature, points of interest like pedestrian crossing, crossroads, etc …)
. the instantaneous vehicle speed
From these two inputs, SafetyNex evaluates, by applying the rules, the adequacy of the driving speed of the vehicle to difficulty and danger of infrastructure.
A sporty driver accelerating hard, braking hard, but passing dangerous places at low speed will be scored with a low risk.
A quiet driver that passes through a stop road sing at 30 km / h without braking will be scored with a high risk.
A brutal braking cannot be considered as « bad driving » if it is necessary to avoid an accident …
We see then, that SafetyNex risk estimation is not correlated with the absolute value of acceleration, but with ACTUAL speed adaptation to difficulty and danger of the infrastructure, in real time.
Additional inputs (optional) are already scheduled, and can afford to modulate the estimated risk to increase acuracy of SafetyNex :
. grip (if one has a sensor to connect to the input provided for the purpose of SafetyNex)
. weather report (if one has the temporal and spatial information)
. atmospheric visibility (if one has adequate measure: example: a camera and the measuring module of atmospheric visibility : VisiNex)
. distance to potential obstacles (if it has an adequate sensor : eg radar, lidar, or camera with RoadNexObstaNex modules)
. a driver distraction factor (if the driver is observed with a camera and / or if one monitors the activity of mobile phone, etc …)
All these additional inputs are already ready to be used by SafetyNex but of course, they increase the cost of deployment, involving sensors (camera, …) and additional computing power before getting in SafetyNex to process signals and images from the optional sensors.
Using SafetyNex V2.1 with only the required inputs already allows a very high correlation of the estimated risk with the accident. We recommend to implement this version, already infinitely more effective than any other onboard measurements.
The interest of SafetyNex is that the future is already assured: Moore’s Law by rapidly lowering the cost of electronics and embedded computing, SafetyNex is ready to process the additional inputs, when users want to integrate cameras and sensors.
TYPICAL USES OF SafetyNet V2.1
. Insurance Companies:
– Pay how you drive
– Predictive modeling of bonus malus: the same accident under the same conditions does not lead to the same conclusions based on accumulated historical and recording the last seconds risk SafetyNex
– Generation of a dumb risk variable, correlated to the accident, to help actuaries refine pricing (big data)
. Fleet managers
. Automotive equipment suppliers:
– Alarm on risk
– Intelligent Navigation able to advise the driver
. Engineers and researchers from autonomous vehicle:
– Driving Quality Assessment generated by the robot
CONCLUSION
Embedded estimation of road risk of accident is now a problem completely solved by a product available for deployment, SafetyNex.
SafetyNex is deployable at Low cost on:
. mobile phones
. electronic device of a Automotive Tier 1 supplier (without plugging the OBD).
And SafetyNex already planned to integrate (once the cost is acceptable) grip sensors and cameras (for example) to estimate traffic and atmospheric visibility, as well as information such as weather and driver distraction.
All of these are already processed by SafetyNex rules based system, so that the tool can quickly evoluate with each decrease in the cost of sensor elements and cost of computing power needed to compute sensors outputs.
La voiture autonome fait rêver la filière française
Un article du journal Le Monde.
«Avec les autres sociétés, nous rassemblons environ 150 personnes et réalisons tous ensemble un millions d’euros de chiffre d’affaires, soit une entreprise de taille intermédiaire. Cela nous permet de démarcher les équipementiers et les constructeurs, et de les rassurer sur notre pérennité. Seul, c’est beaucoup plus compliqué», explique Gérard Yahiaoui, le PDG de Nexyad, une PME qui développe des algorithmes capables d’agréger les images captées par les caméras afin de «voir» la route. C’est surtout l’un des principaux concurrents de la start-up israélienne Mobileye, le leader mondial de ce nouveau marché.
Groupement ADAS (Mov’eo) installing the booth at ITS WORLD in Bordeaux
Dominique Fernier (Transpolis) and Denis Foussard (FH Electronics) installing the booth with Nexyad Demos, real robotized car, films of ADAS use cases.
NEXYAD will show RoadNex, ObstaNex, VisiNex, SafetyNex.
From the 5th to the 9th of October, NEXYAD will show their onboard modules for ADAS of booth B112 at
ITS World Congrès in Bordeaux.
– Road detection with RoadNex
– Obstacles detection with ObstaNex
– Visibility measurement with VisiNex
– Risk/road safety estimation with SafetyNex
Onboard road safety/risk Measurement correlated to accidents :
How to use SafetyNex V2.1 for Insurance applications, and for
Automotive applications (ADAS & autonomous vehicle)
by NEXYAD
INTRODUCTION: Description of 2.1 SafetyNex
SafetyNex is a software module proposed by the French company NEXYAD. This module aims to match the driving style (acceleration, vehicle speed) with the danger characteristics of infrastructure.
SafetyNex development started in 2001 and benefited from three national collaborative research programs (PREDIT and FUI) that allowed NEXYAD modeling expertise of road equipment notation accident-prone infrastructure.
Indeed, the accident-prone nature of a local infrastructure, cannot be deduced from statistical studies on this local element, for the simple reason that accidents are rare events.A driver has one accident every 70 000 km.
To investigate the accident, the experts of the equipment mainly use two methods:
. aggregated statistics at national and European level : it provides large numbers of accidents and thus make statistics relevant. But heterogeneous infrastructure through Europe makes it difficult to project results on a local infrastructure that has its own characteristics, sometimes far from the average characteristic in France or Europe.
. the observation of « near misses » or « almost accident » as they are numerous : most potential accidents can be nearly avoided by drivers. Experts of infrastructure developed observatories for those danger situations and it brings useful information.
From these elements, the experts of the infrastructure have published a set of rules for predicting accidents, based on characteristics, the accident-prone characters of a piece of infrastructure.
For example, a bend may be accident-prone if its radius greatly shortens in its middle (curve which closes), but a « normal » curve may become dangerous if it comes after a long distance of straight line (this is called « break out on the pathway « ).
NEXYAD integrated all these rules in SafetyNex and has made numerous tests on real world to validate the efficiency of scoring danger.
SafetyNex V2.1 reads on-board navigation map that gives infrastructure characteristics , downstream of the vehicle, and then SafetyNex scores the relevancy of the driving style, knowing the shape of the road ahead and other important things (pedestrian pathway, …).
Note: in a higher version (already being tested), SafetyNex may also reflect the presence of other users on the road (cars, trucks, bicycles, motorcycles, pedestrians …) by coupling it to RoadNex (road detection by camera), and ObstaNex (obstacles detection by camera).
But this version will require to have appropriate computing power to process videos. The Moore law lets us think i twill be easy and cheap in 2 or 3 years.
For now, we think that adaptation of the driving type with infrastructure characteristics meets a real issue and SafetyNex V2.1 is the only available (and for sale) module in the world for performing this function.
The output computed by SafetyNex is the road safety risk, between 0 and 100%, at every moment.
It is possible to use SafetyNex in real time to alert the driver.
It also may be used off-line, aggregating the risk : SafetyNex used to complete big data with revelant variables to study the road safety risk, in a statistical way.
SafetyNex is available in three types of running environments:
. PC under Windows or Linux
. Electronic device of an Automotive Tier One Company, for the new vehicles and aftermarket
. smartphones
SafetyNex V2.1 FOR USE OF INSURANCE
Insurance Companies have several ways to use SafetyNex V2.1:
. free distribution of the SafetyNex App for smartphones to all their customers :
In this case, the business model is to seek partners who want to reduce their cost of acquiring customers online.
In fact, when you divide the advertising budget of a major retailer (Amazon, Fnac, Darty, …) by the number of purchases, there are about 30 euros (cost of a customer).
The idea of NEXYAD then is to propose a serious game that « gives » points to drivers who behave well.
These points are converted into « coupons » with one or more distributors. The drivers will go on the website in order to validate coupons by purchasing products. In doing so (spotted digitally with flash code …), the distributor will give back 15 euros : 7,5 euros for the cupons, and 7,5 euros for the Insurance Company that distributed the NEXYAD App to their millions of customers. The insurer therefore makes money and share revenue with NEXYAD and its technology partners.
This solution is particularly interesting for several reasons:
. the insurance company earns money directly on millions of purchases.
. drivers do their best to earn coupons, and therefore change their driving behavior to achieve it.
The insurer therefore modifies in a sustainable way the driving style, and contributes to the decline in the number of accidents. The Communications Department of the the Insurance company may use this fact for communicating it to the public.
. the largest distributors lower their customers acquisition costs and they can then lower their advertising budget
. the driver improves self safety, and is able to purchase more.
. distribution in an electronic device :
The electronic device has the advantage of being still operational without driver intervention.
This solution is particularly interesting for business fleets, and allows to quickly and efficiently initiate a type of pricing system « pay how you drive » that may be seen as an extension of the bonus / malus system, but with a predictive power.
. constitution of big data for actuaries:
Risk can be recorded onboard, or in aggregate, giving statisticians a new variable for more accuracy on pricing.
SafetyNex V2.1 FOR USE IN ADAS AND AUTONOMOUS CAR APPLICATIONS
The road safety risk measurement is of course interesting for automotive applications :
. SafetyNex read the shape of infrastructure downstream of the vehicle (electronic horizon), and can therefore help to choose which types of sensors are relevant.
. SafetyNex can estimate the maximum distance targetable by sensors (radar, camera, …) depending on the shape of the infrastructure (turn, climb, …)
. During a delegation of driving, the robot drives the vehicle and then SafetyNex V2.1 can estimate the road safety related to the automatic driving.
The road safety score can be used in several ways:
. Off-line: the engineers who design and develop driving delegation system uses the risk score of SafetyNex V2.1 as a feedback, and try to minimize it by successive tests.
. On-line: the risk score is used by the decision-making system to generate actions that lead to the lowest possible risk.
CONCLUSION
SafetyNex V2.1 is now available and NEXYAD is currently working on deployment opportunities worldwide at the beginning of 2016.
This module is unique and has a direct effect, if properly used, on road safety.
Visibility is one of the structural elements of road safety. Indeed, the sense of sight is the only one that let us perceived the future path of the vehicle and then let us act on it : the driver « can see » in front of the vehicle, he predicts where the vehicle will go, and he can act on the controls (brake, steering wheel, …) in order to control the trajectory.
No other way allows us to anticipate.
If we model the task of driving with an automatic control engineering scheme, then we can notice that vision is used quite everywhere :
Vision plays a critical role in driving task, and what sizes the efficiency of this sense is « visibility ».
Visibility can be affected by many kinds of factors:
. the absence or insufficience of light (that is why the infrastructure is sometimes illuminated at night, and why vehicles are equipped with lighting.
. rain deposited on the windshield (that is why vehicles are equipped with wipers)
. mist on the windshield (that is why vehicles are equipped with demisting systems)
. humidity, fog or mist suspended in the air in the road scene.
Experts of road infrastructure add elements to enhance the visibility of the path :
. lane markings (white lines), reflective elements.
Similarly, automobile experts equip their vehicle with systems enabling them to improve visibility for the driver, but also allowing the vehicle to be more easily seen by other drivers.
We then understand that measurement of visibility is an important area of potential improvement of road safety in via ADAS.
VISIBILITY MEASUREMENT
The founders of the company NEXYAD have been working since the 80s on the measurement of visibility, early on military applications.
Indeed, it is the military who have studied since the 60’s which criteria allow human visual perception system to detect objects on their clutter.
For the military, the constant search for stealth (camouflage, for example) requires modeling the performance of the detection by human, depending on the light of a scene in the visible wavelength.
The work carried out tests on panels of thousands of soldiers, and led to predictive models for human vision of the ability to detect objects or not, depending on the image quality.
NEXYAD is one of the very few companies in the world to hold these models and have experience of their implementation for more than 20 years.
In simplified terms, we can consider that our eyes and brain need, depending on the size of the objects to be detected, a different contrast level.
We can then compare the contrast available in a scene (eg a road scene) with needed contrast to detect, , for each size of objects.
The comparison results in two scores :
. the apparent size of the smallest detectable object : as the apparent size of an object decreases with distance, it can then be deduced the maximum distance of detection for a reference object (a car, a truck, a pedestrian). Distances will obviously be different for every object because they don’t have the same size. Johnson criteria give let also estimate the maximum distance for object recognition, and the maximum distance for object identification.
. ease of interpretation of the visual scene. NEXYAD summarized this in a score computed from available and needed contrast: the Visual Quality Score (VQS).
This measure of visibility enables automotive application objectify the subjective. NEXYAD has developed two product lines from the same technology :
Place a vehicle on a test bench and VisiNex Lab measuring visibility among time. If there are disturbs of visibility from rain, for example (using NEXYAD RainNex rain machine, or another rain machine), then we see scores for degraded visibility. If one starts the vehicle visibility restoration systems (eg in the case where the disturbance is the rain : the wipers), then we measure the performance of the visibility restoration.
VisiNex Lab is used by the automotive industry and is still the only tool for measuring the performance of wipers, demisting system, lighting system, …
. an embedded module for ADAS : VisiNex Onboard https://nexyad.net/Automotive-Transportation/?page_id=438
VisiNex Onboard measures the image quality and predicts the detection power of the driver and onboard artificial vision modules. So we get a rating of confidence for artificial vision systems.
Again, NEXYAD is the only non military company to dispose of this technology.
CONCLUSION
Every tier one company or car manufacturer should use NEXYAD modules VisiNex in order to measure performance, robustness, and reliability of their wipers, lighting, and of their camera-based ADAS.
VisiNex Onboard is currently under implementation into the asynchronous real time framework RT-MAPS.
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.
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.
Visibility measurement for ADAS and Autonomous Vehicle
By NEXYAD
Advanced Driver Assistance Systems (ADAS), and partial or total delegation of car control systems will integrate more and more cameras. Those cameras are used to capture video and images are inputs for obstacle detection algorithms, road detection algorithms, detection of pedestrians systems, …
However, a camera can « see » only under certain conditions, and the algorithms used to exploit image need a certain level of image quality. It is possible that some algorithms test themselves if they are in a case of good image quality or not, but in the general case, they don’t, and it is then prudent to have a qualification system that is independent of the detection systems.
The company NEXYAD has worked for years on atmospheric visibility measurement for military application, and was able to develop predictive models of the ability for a human to detect objects. This work can be easily set to pass from a performance prediction of the human vision to a prediction of performance for a machine vision system.
The models consist in comparing the contrast in the scene with the required contrast for detection and / or pattern recognition.
Such a system requires that is respected a compromise between several characteristics of the image:
. number of different gray levels (for a digital camera, it depends on the number of bits)
. size of the objects to be detected
. contrast of objects from their background
Note for Automotive engineers : a performance specification for a camera-based detection system, without giving the minimum contrast, le maximum number of pixels, the number of bits … does NOT have any sense. It is important to know that fact in order to make applications that work and application that know when they work.
For instance, we are all able to detect stars in a dark night sky : the size of objects is very small, the number of Grayscale is very low (pure black and pure white), and the contrast of objects from the background is huge.
Similarly, we are able to distinguish clouds over gray sky : the size of objects is very large, and even on edges there is no detail (no high frequency / contours), and the number of different gray levels is very large (gradual grey scale from black to white).
Between these two extremes are all possible cases, and in particular with all traffic scenes that may vary greatly from one to another :
. sunny day, overcast day, dark night, undergrowth, sunset, night in headlights, fog, rain, etc …
In addition to these technical compromise, there are criteria (eg criteria Johnson) that allow to objectify the subjective.
NEXYAD has developed a tool called VisiNex that integrates models and criteria described above, which led to two products:
. VisiNex Lab : test bench for visibility measurement. It sets a vehicle with calibrated visibility disturbances (rain machine, fog machine, …), and VisiNex Lab measures the evolution of the available visibility during the disturbance and during activation of visibility restoration systems (lighting, demisting, wiping, …).
VisiNex Lab is used to adjust the rain sensors, the wiper systems, the lighting systems. VisiNex is a world leader on this type of use : https://nexyad.net/Automotive-Transportation/?page_id=159
. VisiNex Onboard : NEXYAD took his model into onboard applications to apply and qualify road visibility along the route running (important place to qualify for the road safety applications).
VisiNex Onboard is currently being integrated into the framework for asynchronous real-time applications development RT-MAPS, and will soon be in the NEXYAD vision modules pack for ADAS and driving delegation applications.
Standard visibility on a highway scene. Degraded visibility when approaching a tunnel
VisiNex Onboard can be used in automotive application on the following topics :
. visibility measurement to control Visibility restoration systems (wiper, lighting, …)
. qualification of visibility conditions where an obstacle detection or road detection system will work properly.
The second point is important because road safety applications require to maximize the reliability of vision systems.
To know more :sales@nexyad.net
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Demos, real robotized car, films of ADAS use cases.
