Work meeting with the company Induct for the research project MERIT (after market ADAS involving innovetive pattern recognition and risk estimation to help drivers navigation).
The modern wiper systems for car windshields are complex mechatronics systems that implement both sensors (light sensor, rain sensor), electric motors, one or more wiper blades with rubber qualities to be defined, a software for analysis and recognition of the rain, and a software that automatically triggers the wipers with an appropriate strategy. Each of these components can be selected or adjusted by many ways:
– Rain sensor: location of the rain sensor on the windshield, adjusting the threshold of the first outbreak, its timings, its hysteresis, …
– Luminance Sensor: taking into account only the integration of infrared or visible wavelengths, the sensor location and direction (does is point only the sky?) …
– Rubbers : quality of the viscoelastic material, surface condition, …
– Arm of the wiper: with pressure points, shape memory, …
– Architecture of wipers: conventional two blades, butterfly with a stop in the down position, with throttle stop position, single blade, …
– Characteristics of wipability of the windshield (macroscopic form: take-off speeds, … surface condition)
– Not to mention the software that have huge degrees of freedom (lines of code) …
If we consider a system composed of six subsystems that can each take 10 different items (10 items of rubber, 10 potential triggering software, 10 settings of the rain sensor, …), we obtain 106 possible systems (1 million) !
On a million possible systems, the engineers have to find ONE solution that is industrially acceptable (effectiveness, overall cost of the solution …).
The traditional method and still the most widespread in the industry to test and develop such a system consists in equiping a vehicle with a configuration (set a priori), in waiting for rain, and then sending a expert driver driving on roads they know. The driver then completes qualitative assessment grids which are used by engineers to change the settings of their wiper system.
Obviously, this method is tedious, and they can only test an extremely small number of combinations, so that it passes next to the statistically best solution (the best compromise between performance and cost). In addition, weather conditions in several consecutive tests may NOT be the same: it is NEVER the same rain, NEVER the same light, …
Performance comparisons are theoretically and therefore virtually impossible: no regression testing can be performed at every change, no quantitative rating of the effectiveness can be given … in short, despite all the expertise of engineers and the care they take to perform these tests, it is far from the industrial approach, let alone the quality approach.
The sampling of this complexity requires the use of at least fractional orthogonal experimental design. For this it is necessary to know how to reproduce the weather conditions in the laboratory, dive the vehicle into a known and reproducible (calibrated) environment, and then quantitatively measure the performance of the wiping.
NEXYAD has developed a tool to achieve this. This tool is divided into three major functions:
– A system for generating calibrated lighting (to generate repeatable chronograms of illumination).
– A system for generating calibrated and reproducible artificial rainfall, (for watering the windshield with known artificial rainfall, with statistical characteristics of natural rainfall)
– A system for measuring the effectiveness of the wiper system (providing a score of effectiveness)
The measurement of effectiveness of the wiping was until fairly recently a point relatively blocker. Indeed, the wiper is not a “function” in terms of the driving task: the “function” would rather be “in all circumstances ensure good visibility for the driver”, and wiping is just a technical response to achieve that in the case where visibility is degraded by the water deposited on the windshield.
We then see that if we can measure the visibility of the road scene by the driver (through windshield), then we can measure the effectiveness of the wiping : the rain degrades the visibility, the wiping restores some of the lost visibility.
The combined use of these tools can set a wiper system in two months with two people, where before we had 5 people for over a year. In addition, the system performance is known and can be optimized (since we know the measure). The technically efficient solutions can then be compared in terms of cost, allowing more to achieve substantial savings.
Some automotive industrials already use NEXYAD tools.
Measuring the visibility of a scene for a human being needs to have a mathematical model of the human vision system.
Actually, human vision requires some compromise between measurable characteristics of image quality such as contrast, depth, and object size, so as to detect, recognize, and identify the content of collected images.
When this compromise is not met, the vision becomes very difficult, tedious or even impossible.
It is obvious that the noise in the image (electronic snow of a sensor, for example), or poor contrast (due to the presence of aerosols, fog, rain, humidity, …) may considerably lower the performance of our vision system.
We can therefore say that this “images quality” is a key point of our performance.
But we do not need the same quality to detect all types of objects. For example, we will detect a gray cloud on a gray background, even shapeless, with extremely low contrast if the luminance depth (number of bits for a digital image) is high. On the opposite, on a dark sky, we can detect a star whose contrast is extremely strong, but whose size is at the limit of our eye angular accuracy. In such a case we just need 2 luminance levels (binary images are OK).
Human vision mathematical models were originally developed by the U.S. Department of Defense who wanted to model the impact of camouflage on the probability of detection (of an infantryman, a tank, …), recognition, and identification by a watchman.
“Detection” means “I see something”.
“Recognition” means “I see a car.”
“Identification means “I see a 3 serie BMW”
Of course, it is obvious that the level of detail needed to perform these three operations is not the same.
Measurable criteria in the picture (example: Johnson criteria) could be determined after testing a variety of situations by panels of hundreds of soldiers.
Based on these criteria, it is possible to construct a mathematical model for measuring perceived quality of images. This model is predictive of the ability to detect or to understand the image content.
NEXYAD has developed such a mathematical model of human vision and applied it, among other things, to test the effectiveness of windshields wiping systems of vehicles (product : VisiNex ™) : the rain that collects on the windshield breeze down the performance of visual detection of the driver. Each pass of the wiper can restore some lost visibility.
NEXYAD is currently applying this same maths model in the context of the extent of visibility of road markings (white lines, …), depending on the weather (day / night, rain, …).
The number and scope of potential applications of such a human vision mathematical modelling system are extremely broad.
Delivery meeting of Pattern Recognition research results for the internet start up company CRYSTAL CONTENT.
NEXYAD at the board meeting of the french competitive cluster MOV’EO.
President CEO of NEXYAD at the “Startegic Orientation Council” (Conseil d’Orientation Stratégique) of the French Research Program PREDIT at the “House of Latin America” (Maison de l’Amérique Latine) in Paris.
Nexyad at the MET meeting (comité Richelieu pacte PME) with Thales.
Kick off of the research project SURVIE:
Definition of standard rules of measurement with the NEXYAD product VisiNex™ for road safety applications.
partners: LCPC, LRPC , VALEO, SAINT GOBAIN, CETE Lyon, OKTAL, AXIMUM