CARAMEL is a European project that aims amongst others to improve and extend cyberthreat detection and mitigation techniques for automotive driving systems. This paper highlights the important role that advanced artificial intelligence and machine learning techniques can have in proactively addressing modern autonomous vehicle cybersecurity challenges and on mitigating associated safety risks when dealing with targetted attacks on a vehicle's camera sensors. The cybersecurity solutions developed by CARAMEL are based on powerful AI tools and algorithms to combat security risks in automated driving systems and will be hosted on embedded processors and platforms. As such, it will be possible to have a specialized anti-hacking device that addresses newly introduced technological dimensions for increased robustness and cybersecurity in addition to industry needs for high speed, low latency, functional safety, light weight, low power consumption.

This work is part of a research project named as 'HYDROGIS LAB'. A preliminary underground survey using the newly acquired ground penetrated radar is presented. This technology provides accurate scanning and 3D spatial representation of the underground piping network. The main aim is to refine the digital imprint of the water supply network under consideration. Work has also been initiated in the development of a GIS platform for managing all information (maps, satellite imaging, 3D scans, network system components etc). The geo-radar scanning is required to support the modeling of water supply network, design and operation.

No abstract