PhD (Universite d'Evry Val d'Essonne)
Sebastien Glaser is Professor in Intelligent Transportation System at CARRS-Q, where he focuses on a safe and sustainable development/ deployment of Automated Driving System in interaction with others road users (drivers, cyclists, pedestrians …).
He has obtained his PhD in Automatic and Control in 2004 (defining a driving assistance system in interaction with the driver) and worked, as a researcher in the development of Connected and Automated Vehicles (CAV). He was involved in several European Union initiatives (EU FP6, such as SAFESPOT on V2I communication) and in French National Research Agency (ANR) initiative (such as PARTAGE on shared control between the driver and the vehicle).
Since 2009, he has worked across academic and industrial sectors and held senior researcher positions. He led the French ABV project, gathering 8 academic and industrial partners, to develop a CAV solution at low speed. He has created, with Dominique Gruyer, CIVITEC, which commercialized the research outputs on
virtual environment and simulation (and is now a part of the ESI Group). He has been the deputy director and director of LIVIC (a research unit of IFSTTAR, the French institute of science and technology for transport, spatial planning, development and networks) from 2012-2015 and project leader of VEDECOM (public private partnership research institute) between 2015-2017, developing the Autonomous Vehicle prototypes.
He was involved in French (ANR) and European (FP7 and H2020) initiatives on AV development, test, and evaluation. He was also leading the technological roadmap task force in France for the AV.
- New Industrial France: Leadership of the workgroup on embedded intelligence for autonomous vehicle to define and evaluate the roadmap on the AV technologies and to determine a budget for the Ministry of Industry on specific research and development needs.
- AUTOMATE, European Project (Grant Agreement No. 690705; 2016-2019): The AutoMate project is working a novel driver-automation interaction and cooperation concept based on viewing and designing the automation as the driver’s transparent and comprehensible cooperative companion or teammate.
- ADAS&Me, European Project (Grant Agreement No. 688900; 2016-2020), Responsible of the range anxiety use case, development of related ADAS: The ADASANDME project develops adapted advanced driver assistance systems that take into account the driver’s state and the situational and environmental context to automatically transfer control between the vehicle and the driver for safer and more efficient road usage.
- CARTRE, European Project (Grant Agreement No. 724086; 2016-2018): accelerating development and deployment of automated road transport by increasing market and policy certainties.
- VRA, European Project (Grant Agreement No. 610737; 2013-2016): Vehicle and Road Automation is a support action funded by the European Union to create a collaboration network of experts and stakeholders working on deployment of automated vehicles and its related infrastructure.
- eFUTURE, European project (Grant agreement No. 258133; 2011-2013), Responsible of project dissemination activities and Green ADAS development: The eFuture project wants to prepare the next generation of electric vehicle based on a first prototype by creating a platform which minimises its energy needs but can still optimise dynamically its decision between safety and energy efficiency.
- ABV, French National Research Agency (ANR-VTT-2009-01), Project leader, development of fully automated vehicles at low speed (less than 50 km/h) on a secured route, which, moreover, is assisted outside of these areas. This route, although secured, would be opened to traffic.
- HaveIT, European project (Grant agreement No. 212154; 2008-2011), development of vehicle control, path planning and transition to continental demonstrator: The main objective is the improvement of safety and environment by highly automated vehicle applications supporting the driver in over- and underload situations built on the joint system driver – co-system selecting the appropriate degree of automation depending on driving situation and driver state.