OPEN TALK (AI): Blending AI and Embedded AI for the Best - Smart City Use Case

Stephane Gervais
LACROIX Group, EVP Strategic Innovation & Smart Data

Stéphane Gervais has more than 30 years of experience in the high-tech industry as global marketing, business development, innovation, project management, and product designer.
He has worked for multi-national companies in Europe and in Asia in the consumer and industrial segments. Stéphane also has experience in emerging technologies and markets. His current position is Executive VP Strategic Innovation at LACROIX Group : Industrial Internet-of-Things (IIoT), new technologies, electronics, autonomous vehicle and smart city are some of his favorite’s topics.
Stéphane holds a Doctorate degree (Ph. D) in Electronics from the University of Bordeaux (France) and an MBA degree from Newcastle University (Australia). He holds patents and wrote numerous papers.

Through an innovative project, reducing CO2 emissions and all other air pollution induced by the mobility in cities by 30% by deploying a solution for a real-time automatic emission-based road traffic micro-regulation, we managed to use the best of AI technologies. Indeed, AI is the key enabler addressing the complexity of real-time analysis of mobility in crossroads and local air pollution with the trend predictions that leads to recommendation to how to regulate road traffic to decrease air pollution and apply these recommendations directly to traffic lights. Using embedded AI at local camera level was instrumental to allow detecting the different road users (vehicle, public transportation, pedestrian, cyclist…) in real-time, while respecting privacy and GDPR, in order to apply mobility strategies for the optimal mobility management with minimum pollution impact. This last part is combining two AI engines with 5 models. This project, [AI]Roads, is an European awarded project and the outcome is tested in some major cities in EU. Beyond technical challenge, we will share some key advantages of combining AI and embedded AI, which might become the mainstream for some applications, and how we offered a scalable solution to a complex problem: the automatic and best trade-off between air pollution and mobility.