Keywords: Computer Vision and Multi-Sensor Based Perception, Machine Learning, Information Fusion, Artificial intelligence, System of Systems.
Applications: Driving scene understanding, Object detection, Collaborative perception, Human behavior, Mobile robotic systems.
Associate Editor of the Special Issue on System of Systems Engineering, 22(6) 2019 Systems engineering, The Journal of the International Council on Systems Engineering (INCOSE), Wiley.
Program co-chair of the IEEE – 13th Intl. System of Systems Engineering Conference (SoSE 2018), June 19-22, 2018. Sorbonne Université – Campus Pierre et Marie Curie, Paris, France.
(Proceedings on IEEE Xplore)
We aim at enhancing the vehicle’s perception and situational awareness of the complex and highly dynamic traffic scene, for the sake of a better autonomy, while making use of more sources of information than the one provided in a standalone way by its on-board sensors. Other traffic participants like cars, buses, trucks, pedestrians, bikes, or elements of the infrastructure, all seen as nodes of a mobile ad hoc network, can helpfully share such information. We address the problem of distributed data fusion within a complex dynamic system, in which the vehicles are not controlled by others but cooperate together. Moreover, every vehicle is not necessarily cooperative: perceived vehicles/moving objects can be shared in between the cooperative vehicles network, allowing to get a complete view of the traffic scene in some situations. First tangible result of DAPAD: our participation to the Grand Cooperative Driving Challenge (GCDC, May 28-29, 2016 - Automotive Campus Helmond, The Netherlands).