Cooperative Connected Automated Mobility (CCAM) will become a reality in the coming years thanks to improved connectivity and digitalization, and the evolution of solutions based on artificial intelligence and big data analytics.
This will give rise to new cyberattack surfaces and vectors becoming new challenges in the cybersecurity domain. CCAM related services and products will require high resilience to prevent mobility services disruption and human harm in case of fraud, cyberattack or cyberterrorism events. Such system’s resilience heavily relies on the data and information collected by the CCAM systems and their fusion, sharing and processing.
Therefore, guaranteeing a secure flow of generated and processed data between all stakeholders is vital for a correct, efficient, and robust management of the different services and systems in the CCAM context. Ensuring the veracity, quality and integrity of the data generated is essential in the process of mobility management and control, both in the stages of persistence, transmission, access and use of the information, as well as in the different stages of cybersecurity and security management of the CCAM ecosystem.
SELFY is a European funded project under the Horizon Europe programme aims to increase the safety, security, robustness and resilience of CCAM ecosystem’s by researching and developing a toolbox made of collaborative tools. SELFY expected outcomes include an increased effectiveness rate in the detection of vulnerable vehicles and security breaches.
Why are SELFY project expected outcomes crucial for the society as a whole?
CCAM will permanently change the traffic of the future and is one of the major future trends in the automotive industry. CCAM technologies are expected to contribute to reduce of the number of road accidents, as well as to cut CO₂ emissions, improve the accessibility to transport and the efficiency of the transport sector.
To gain societal acceptance, the CCAM ecosystem must be safe, secure and resilient, making it necessary to build a trustworthy system.
SELFY will address such challenges by conducting Research and Development on algorithms and technologies to build a toolbox to improve CCAM ecosystem resilience against cyber threats. SELFY focuses on four pillars, Situational Awareness, Resilience, Trust and Secure Data Sharing.
Therefore, one of the main objectives is the development of a situational awareness tool using sensor data fusion (Situational Awareness Cooperative Perception macro-tool) with the contribution of many tools which provide methods and technologies to aggregate and fuse sensor data and increase perception.
Resilience, detection, defence and response – the Cooperative Resilience Healing System – has been part of another objective, which has been mainly focused on another of the work packages. Partners have partially developed tools which provide intelligence, self-protection, self-defence and self-healing capabilities.
An important tool of the CRHS is the VSOC and it is related to another important objective: a complete integration and cooperation of the SELFY tools. Therefore, many tools have an interface with the VSOC providing information and receiving triggers and actions.
Trust and Secure Data Sharing, the Trust and Data Management System, has been covered by another work package. In that regard, SELFY’s partners have partially developed a set of tools to guarantee privacy, confidentiality, integrity and immutability of data.
Another objective of SELFY is the use of advanced technologies and algorithms based on artificial intelligence. Both the Situational Awareness and Collaborative Perception, the Cooperative Resilience and Healing System and the Trust Data Management System incorporate solutions with these technologies.
The toolbox will be demonstrated and validated in relevant scenarios. SELFY has selected a set of use cases linked to three big scenarios: resilient cooperative mechanisms for Vulnerable Road Users’ safety, the secure empowerment of backend system for traffic management system and the robustification of a platoon of Avs.
These three scenarios also correspond to three different validation environments, which are real-world validation, laboratory/HIL (Hardware-In-the-Loop) validation and simulation validation. These use cases have been shared and selected with the support of industry stakeholders in various workshops.
