Economic Intelligence presentation on Nexyad-ADAS blog

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NEXYAD always looking at surveys about ADAS and driverless cars market … Here is a relevant study about Automotive Advanced Driver Assistance Systems Market (2015-2025), by

The Next Step Towards Autonomous, Self-Driving & Driverless Cars

LONDON, May 12, 2015 /PRNewswire/ — Report Details

In many ways, advanced driver assistance systems (ADAS) are the first step on the road to semi-autonomous or fully-autonomous self-driving and driverless vehicles. In 2015, Park Assistance, Surround-View Cameras, and Adaptive Cruise Control applications dominate the ADAS market; however, the penetration into passenger cars is relatively low despite the increasing rate of installations into the premium car segment manufacturers.
Often these are fitted in isolation, and even where multiple systems are fitted in a vehicle, great care is taken to ensure that these systems are perceived only as assisting the driver (who must remain fully alert) rather than taking over his role. As the number and abilities of these systems increases, the situation will move further along a continuum from driver assistance to in effect driver substitution, meaning that the driver would no longer be required to be alert and instead the onus would fall on the vehicle to warn the driver when his input was required, bringing him back to a state of full alertness.

To read more :


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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 ?

For more information :

Autonomous Vehicle TEST & DEVELOPMENT Symposium 2016

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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.

Some drivers are quiet but still dangerous (in a road safety point of view)

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Some drivers are quiet but still dangerous (in a road safety point of view) :

Most people think that brutal drivers (strong braking, etc …) are dangerous. The hypothesis is that they do not anticipate enough and then they are
led to brake very strongly. Many Insurance Companies developped their own onboard “dirving style” estimation that scores this kind of hypothesis.

To read more : Nexyad-ADAS

Nexyad RoadNex v2.2 – Road Detection on little road in Forest

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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.