Periodic Reporting for period 2 - CONNECT (Continuous and Efficient Cooperative Trust Management for Resilient CCAM)
Période du rapport: 2024-03-01 au 2025-08-31
The 5G C-V2X technology is expected to greatly enhance autonomous driving through perception sharing, path planning, real-time local updates, and coordinated driving. These features facilitate the next generation of ITS solutions for cooperative autonomous driving applications (e.g. intersection movement assist, fleet management systems, cooperative routing, and parking services), and greatly reduce emissions. The core of the smart transportation vision revolves around an integrated communication and transportation network that promotes several societal benefit sand shapes a new era of advanced road safety, enhanced personal mobility, and environmental sustainability. However, in order for this vision to materialize, security and trustworthiness are key properties of such a system. This is where CONNECT’s core contributions lie: CONNECT addresses the convergence of security and safety in CCAM by assessing dynamic trust relationships and defining a trust model and trust reasoning framework based on which involved entities can establish trust for cooperatively executing safety-critical functions.
CONNECT implemented a first-of-its-kind Trust Assessment Framework (TAF) capable of CCAM-wide trust quantification using subjective logic and runtime evidence. The framework introduces adaptive mechanisms for capturing and updating vehicles’ trust scores, anchored to decentralized hardware-based Roots of Trust (RoTs) and elevated to the CCAM level. These mechanisms leverage advanced trusted computing primitives developed as CONNECT attestation extensions, enabling complex CCAM systems to operate in a continuously monitored and trustworthy environment. The core foundation of CONNECT’s Architecture Reference Framework is to support higher levels of vehicle and service automation by enabling the secure exchange of CCAM information along with authenticated and encrypted trust information between vehicles and backend infrastructure, thereby improving service accuracy and overall trust across CCAM stakeholders.
CONNECT not only achieved its primary goal of enabling the secure and trustworthy exchange of CCAM information, through CCAM-wide trust quantification based on subjective logic and runtime evidence, but also established a comprehensive trust plane across vehicles and infrastructure, including both the central cloud and the Multi-Access Edge Computing (MEC) environment, thereby extending operational boundaries. The framework advances the vision of disaggregating services across the compute continuum, leveraging emerging networking technologies such as (B)5G and MEC to optimize latency and resource availability closer to the edge. It integrates infrastructure entities beyond traditional cloud-based services , such as traffic control centers and intersection movement assistance services, as well as centralized security solutions like PKIs. By incorporating MEC, CONNECT enables the secure offloading of tasks from vehicles to MEC nodes, providing abundant computational resources for efficient task execution and resource management.
To operationalize CCAM-wide trust quantification, CONNECT’s framework functions in two complementary phases. The Design phase establishes trust models, defines the types of evidence to monitor, and deploys trust-related components across the ecosystem. The Runtime phase executes attestation and continuously monitors system configuration integrity. Importantly, CONNECT not only provides the necessary hardware-rooted evidence for trust assessment but also extends existing solutions through the integration of Misbehavior Detection, ensuring convergence with the overall trust assessment process. Evidence is harmonized and abstracted before transmission to protect privacy, allowing comparison against the Required Trust Level (RTL) and enabling comprehensive trust assessment across all CCAM resources and actors.
To operationalize CCAM-wide trust quantification, CONNECT’s framework functions in two complementary phases. The Design phase establishes trust models, defines the types of evidence to monitor, and deploys trust-related components across the ecosystem. The Runtime phase executes attestation and continuously monitors system configuration integrity. Importantly, CONNECT not only provides the necessary hardware-rooted evidence for trust assessment but also extends existing solutions through the integration of Misbehavior Detection, ensuring convergence with the overall trust assessment process. Evidence is harmonized and abstracted before transmission to protect privacy, allowing comparison against the Required Trust Level (RTL) and enabling comprehensive trust assessment across all CCAM resources and actors.