SafetyNex : driving robot maybe will mitigate human errors,
but first they have to imitate good drivers

BEWARE with the statistics : “94% of severe personal damage accidents are due to human errors” doesn’t mean that you’ll save 94% of severe accident with autonomous driving : drivers do not only make mistakes they also drive well (1 accident every 70 000 km, 3 dead every billion km – OCDE) … It is important to study also good driving and near misses (when driver has the right behaviour to avoid accident or to mitigate severity)… That’s what NEXYAD did during 15 years of research programs on road safety ^^ (that led to SafetyNex). See image (if you do not provide the “green” features, you will lose lives more than you gain with your driverless car. Our AI algorithm SafetyNex was made for this.

Example of risk tree




Let’s suppose that the flush does not exist in our toilets, and then let’s suppose that engineers able to create complex systems or even “systems of systems” are consulted to invent it, and that they apply exactly the same method than they do in the field of ADAS and Autonomous Vehicles.

Water Flush Vs Automotive Engineers

We propose to apply the method of scenarios, which consists in crossing all the factors that can modify the situation, then in each case of the complete combination, propose a solution. For this, it is necessary to note the number of possible shapes for the tank, the possible volumes, all the possible locations for the water supply entry, the possible diameters of the inlet pipe, the flow rates and possible pressures of water, the possible residual water levels before filling. We can generate the combinatorial of these factors, which allows us to generate all the possible scenarios of the “flush” problem. In each case, it is possible to give a solution, namely, the duration of filling of the tank (opening and closing of the water tap).

This approach is fully compatible with deep learning, which will also interpolate between two reference cases (quality of interpolation/generalization to be controlled, of course) if characteristics had to drift over time. Of course, the tank must integrate a system of sensors to evaluate the configuration (diameter of pipe, pressure of water, position pipe, capacity of the tank, etc …). We can use a camera, lasers, ultrasounds, etc. So that this recognition of situation is as accurate as possible. For such an approach, automation/control engineers talk about open-loop (feed forward) control because the data flow is as follows:

Open Loop

It is easy to understand that the flush thus designed will be perfectly functional (there is no reason for it does not work), but for a high cost due to the sensors to integrate. Similarly, the robustness of the system to a measurement error or to a bad situation recognition is not guaranteed : we can very good to fill too much or not enough. The accuracy of the configuration case recognition is very important.

If you have the curiosity to disassemble your flush, you will notice that it is much simpler than the system described above: A float indicates when the water supply valve should be closed. The figure is as follows:

Closed Loop

Automation engineers call this a closed loop control (servo control). The feed forward “open” control is reduced to “open the tap thoroughly without worrying about the flow of water, the volume of the tank, and turn off the tap as soon as the float asks for it “. Note that this method works regardless of the configuration of the flush : we do not even need to know the volume of the tank that can be modified (for example: by filling half of the tank with glass beads) without affecting the operation of the flush. It is a robust and cheap system.


The information processing chain of the autonomous vehicle follows the general feed forward form :
NEXYAD has developed the SafetyNex system which dynamically estimates in real time the risk that the driver (human or artificial) takes. However, the autonomous vehicle may be functionally specified as follows:

AD feeding

“transport someone from point A to point B as quickly as possible, and safely.”

The “quickly” aspect is the historical business of the automobile. The “safely” notion integrates intrinsic safety of the system (its dependability: it should not explode, sensors or power supply may not be disabled, etc.), and since it is a vehicle, its ability to move with a good road safety, that is to say by “not taking too much risk in driving”. Since SafetyNex estimates this driving risk dynamically and in real time, it can be said that SafetyNex is a dynamic indicator of “SOTIF” (Safety Of The Intended Function). SafetyNex acts as a “driving risk float” : when the risk arrives at the maximum accepted level (like the float of the flush) we stop the action that raised the risk (example: we stop accelerating or we slow down). Thus, the response of an autonomous driving system is made adaptive (at the decision level) : even if the feed forward open loop is not perfect, it can correct itself to take into account, among other things, the instruction and the measure of driving risk. This system is completely independent of the automatic driving system in terms of information processing, so it represents redundancy of processing.

SafetyNex uses to estimate risk :

. risk due to inadequacy of driving behaviour to the difficulties of the infrastructure : navigation map, GPS, accelerometers

. risk due to inadequacy of driving behaviour to the presence of other road users (cars, pedestrians, …) : data extracted from the sensors (camera, lidar, radar, etc) such as “time to collision”, “inter distance (in seconds)”, number of vulnerables around, etc.

. risk due to inadequacy of driving behaviour to weather conditions: in particular to atmospheric visibility (fog, rain, snow, sand, penumbra). Knowing that when visibility is low, vehicle must pay more attention (and slow down) even if this autonomous vehicle is not impacted by the decrease in visibility (if it only uses a lidar for example) because the avoidance of an accident is done at the same time by the two protagonists : if one of them (pedestrian, human driver), does not see the autonomous vehicle, then it finds itself only to be able to avoid the accident, which doubles the probabilities of a potential accident.

. other

The use of SafetyNex allows to make adaptive an artificial intelligence of autonomous driving, on the following diagram :

Adapt Closed loop AI

If you have a lean computer, then you only apply one loop between t and (t+1) as it is shown on the figure. If you have a powerful computer, you can then even simulate a big number of decisions and take the less risky one (like automaticians do with predictive control systems). Of course, SafetyNex is only ONE way to close the loop (on a crucial notion : driving risk). This figure may be extanded to other variables of contol that make sense for an autonomous vehicle. More complex adaptation rules may switch from a decision to another if risk simulation shows that finally it is less risky (ex : slow down or turn wheel ?).

SafetyNex uses the map in addition to sensors (same sensors as the driving system or parallel tracks) and does not need to accurately identify the situation but instead to estimate a risk (this is a different task). SafetyNex is a knowledge-based AI system (knowledge extracted from human experts in road safety, from 19 countries – Europe Japan USA – who validated the system over 50 million km. Total research program duration : 15 years). This technology is still being improved, of course, but it can already be integrated into autonomous vehicles and avoid a large number of accidents by its ability to make the system adaptive to unknown situations. In particular, in the case of autonomous urban vehicles (autonomous shuttles, robot taxis), the adaptation of driving behaviour to complexity of infrastructure is made possible by SafetyNex, which decodes this complexity by reading the navigation map in front of the vehicle. SafetyNex makes the autonomous vehicle anticipate more by following “rules of safety” : with SafetyNex emergency situations (that still will need emergency braking and other emergency actions) become much more rare. Autonomous vehicle acts like an experienced cautious driver. Note : if you modulate Maximum Accepted Risk, then you modulate aggressiveness of the autonomous vehicle. This might make sense not to let the autonomous vehicle trapped in complex human driving situations (where the autonomous vehicle would stopped indefinitly).

NEXYAD at CES 2018 in Las Vegas

NEXYAD Automotive & Transportation Newsletter #20, January 22th, 2018


NEXYAD at CES 2018 in Las Vegas

Headlines :

– CES 2018 Nexyad Report

– SafetyNex animated video of a use case : The Car Insurer’s Choice

– Nexyad in media

– SafetyNex can bring Artificial Intelligence into Autopilots in respect of ASIL ISO 26262

– Nexyad team wishes you an happy New Year 2018

* * * * *

CES 2018 Nexyad Report

For the second time, NEXYAD went to CES (2018) in Las Vegas (from 9th to 12th of Jan).

Paris CDG AirportArrival at Las VegasTakeoff at Paris CDG Airport                                         Arrival in Las Vegas
Flight to Vegas from Paris is long but it’s worth the trip for a high-tech startup like NEXYAD.

Of course, for NEXYAD, it is the year of deployment in series for our onboard software modules (Connected car/Car telematics, ADAS, Autopilots, Drive and Car sharing), and especially for SafetyNex (estimation of driving risk 20 times per second), and we had organized for a while 6 meetings per day : ADAS and Telematics OEMs that are already currently integrating SafetyNex, and of course new prospects. Very good new contacts too with qualifies prospects from the USA, Japan, Europe.
We also got some interest for RoadNex that integrates a computer vision based free space detection that works perfectly for large round abouts and intersections. We brought a real time RoadNex implementation into an android smartphone (using the smartphone cam and CPU) and we could do some real time demos that show that RoadNex works in a regular smartphone in real time (for those who care of CPU consumption) :

This year we had a barrow on the LeddarTech booth located at Central Plaza, close to Faurecia, Valeo, Google, Visteon, etc … : LeddarTech is member of the MOV’EO Groupement ADAS, and the whole Groupement was part of the « LeddarTech ecosystem » showcase area.

LeddarTech BoothGroupement ADAS DeskLeddar Ecosystem Pavillon at LVCC                               Groupement ADAS Desk

We also used some time slots to do our homeworks on Business Intelligence and visited many boothes including competitors of NEXYAD.
From this visits we could extract some heavy trends : of course, CES deals with quite EVERY subject, then we focused on mobility and what is connected to mobility issues.
First, we must notice that 2018 is THE year of Lidar :
Of course, our partner LeddarTech, but also many other solutions from Startups to Major automotive companies :

Lidar LeddarTech   LeddarTech;                                       
Lidar InnovizLidar Velodyne   Innoviz                                                                           Velodyne
Lidar QuanergyLidar Pioneer   Quanergy                                                                       Pioneer
Startup II-VIAEye   Startup II – VI                                                                      Startup AEye


Another heavy trend is smart cities :

Itron (Energy issues)                                                         Deloitte (Complete systems and strategy)
LoRa (IOT)                                                                       Ericsson (Telecom 5G)
Mobility was a big part of Smart Cities and Urban mobility this year in Las Vegas.
Some soft mobility solutions (electric and connected 2-wheels vehicles) :

Ujet                                                                                   Genze
And of course, autonomous shuttles were numerous this year :

Navya                                                                              IBM (Olli)

Toyota (ePalette)                                                            Transdev

                                         Startup ISFM (MILLA) © MILLA is an innovation from ISFM
As you may notice, you can find shuttles from : a pure leader player, an IT major firm, a major car manufacturer, a major operator of urban mobility, and a high-tech startup.
At least, another completely new trend is the autonomous flying vehicles :


* * * * *

SafetyNex animated video of a use case :
The Car Insurer’s Choice

SafetyNex by NEXYAD is a Driving Risk Assessment App/API for prevention (accompanied driving, young drivers, individuals, professional drivers, seniors) in every kind of 4 wheels vehicle. SafetyNex is worth for UBI (risk profiles, usage profiles) at the end of every trip; reduction of costs (lower rate of accident and in particular of personal injuries + transformation of some severe personal injuries accidents into material accidents); Detection of behaviour modifications in time and Distraction detection (mobile phone …) : under implementation.

* * * * *

Nexyad in media

Rémi Bastien interview, the new President of competitive cluster Mov’eo and also President of VEDECOM Institute and VP Automotive Prospective of Renault Group.

    For English subtitles click the button on the video

Mov’eo is a Mobility and Automotive R&D competitiveness cluster, which since 2006 has been mobilizing its energies at the service of its members to meet the objectives assigned by the State to competitiveness clusters: to foster the development of collaborative projects between members, to contribute to development in the regions of companies, in particular SMES, and to promote innovation in the sector.

Created in february 2014, VEDECOM is a French Institute for Public-Private Partnership Research and Training dedicated to individual, carbon-free and sustainable mobility.

The new MOV’EO President quote NEXYAD SafetyNex at the beginning of its intervention…

* * * * *

SafetyNex can bring Artificial Intelligence into Autopilots
in respect of ASIL ISO 26262

SafetyNex integration in the ASIL 26262.

SafetyNex Asil 26262
© NEXYAD 2017

Complex Automation MUST be ASIL ISO 26262.
Artificial Intelligence CANNOT BE ASIL ISO 26262 (by definition) and acts only on parameters of Complex Automation doing ++/– – variations, never skipping « reflexes actions » (emergency braking, etc), but allowing anticipation speed adaptation to reduce frequency of emergency situations (and then give more margin to reflexes actions and also improve comfort). Maximum acceptable Driving Risk can be changed depending on driving situation in order to set « aggressivity level» of HAV.

* * * * *

Nexyad team wishes you an happy New Year 2018

Greetings 2018

SafetyNex episode 3 : The Car Insurer’s Choice

SafetyNex by NEXYAD is a Driving Risk Assessment App/API for prevention (accompanied driving, young drivers, individuals, professional drivers, seniors) in every kind of 4 wheels vehicle. SafetyNex is worth for UBI (risk profiles, usage profiles) at the end of every trip; reduction of costs (lower rate of accident and in particular of personal injuries + transformation of some severe personal injuries accidents into material accidents); Detection of behaviour modifications in time and Distraction detection (mobile phone …) : under implementation.

SafetyNex episode 2 : Your Driving Assistance System

Nexyad has developed a smartphone App which is a driving assistance system that alerts the driver IF AND ONLY IF speed of the car is not adequate with the road infrastructure. Then SafetyNex allows the driver to slow down or brake BEFORE a situation of danger. SafetyNex is also available as an API to be integrate into devices or other applications.

SafetyNex episode 1 : Driving Risk Assessment

Nexyad explains in this video the key notion of driving risk. From zero risk when the car is sleeping into the garage to 100% risk when driving behaviour is by far not adapted to a particular road context.

Newsletter #19 is now available

Nexyad at CES 2018 January 8-12
in Las Vegas

Headlines :

– Come to meet Nexyad at CES 2018 in Las Vegas – January 8-12
– NEXYAD giving the award of the best Insurtech startup «prix coup de cœur des assureurs 2017»,
organized by Cercle LAB (Laboratoire Banque Assurance) at Allianz Tower in Paris La Défense
– Nexyad invited speaker at UNESCO Conference on Artificial Intelligence : use case of autonomous vehicle
– SafetyNex driving risk assessment (20 times per second while driving): anticipation of danger
– INTEMPORA and NEXYAD, members of MOVEO Groupement ADAS interviewed on BFM Business (Major French TV)
– Bitumen Free Space Detection by Nexyad RoadNex module in real time on a Smartphone
– Nexyad on Groupement ADAS booth at Equip’Auto 2017
– Validation of ADAS Nexyad Database with ground reality
– Individual driving risk assessment and car insurance : what applications ? what business models ?
– SafetyNex can bring Artificial Intelligence into Autopilots in respect of ASIL ISO 26262
– Welcome to YOGOKO, new member of “MOV’EO” Groupement ADAS cluster

Go to the Nexyad Automotive & Transportation Newsletter #19