The interest in autonomous driving has continuously increased in the last two decades. However, to be adopted, such critical systems need to be safe. Concerning the perception of the ego-vehicle environment, the literature has investigated two different types of methods. On the one hand, traditional analytical methods generally rely on handcrafted designs and features while on the other hand, learning methods aim at designing their own appropriate representation of the observed scene.

Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments.

Existing readiness indices for Connected and Automated Vehicles (CAVs) produce a single composite score per city. This conflates use cases with very different territorial requirements and, being compensatory, allows strong performance on one criterion to mask a deficiency that is, in practice, a hard prerequisite. This paper proposes and demonstrates an assessment framework with four elements. First, the verdict is stratified by Operational Design Domain (ODD) profile, so that one territory yields one verdict per use case. Second, each profile is assessed only on its eligible service area, the roads on which the corresponding service could actually operate. Third, a non-compensatory veto layer is overlaid on a compensatory segment-level score, which separates the readiness score from the fit share, the proportion of network length that satisfies every critical threshold. Fourth, the diagnosis is extended to specific corridors through a readiness-aware routing formulation. The framework is demonstrated on the Versailles, Satory, and Velizy perimeter of about 92 square kilometes, using a real OpenStreetMap network of 4834 nodes and 733 kilometres, combined with an explicitly illustrative synthetic indicator calibration. Restricting each profile to its service area changes one verdict outright. The campus shuttle and urban robotaxi profiles obtain near-identical scores, both close to 0.51, but fit shares differing seven-fold, a distinction that a single score cannot express. Readiness-aware routing reroutes up to 42 percent of origin and destination pairs onto better-equipped itineraries for about 1 percent additional travel length. Monte Carlo analysis and cross-method tests using TOPSIS and PROMETHEE-II leave all verdicts unchanged. We position the work as a methodological demonstration rather than a validated deployment tool: the indicator values are synthetic, and empirical calibration, expert weight elicitation, and field validation remain prerequisites for operational use.

We introduce TriHex-Cluster, a hierarchical overlapping clustering framework built on the self-similar geometry of the triangular lattice (6-regular planar graph). The primary algorithm is regime C (greedy 2-packing followed by Voronoi completion), a practical hierarchical clustering method producing disjoint clusters with the Voronoi-contact graph $G_{\mathrm{Vor}}^{(k+1)}$ as the next-level graph and aggregation complexity $O(nlogn)$ (embedding cost excluded). On regular triangular domains with near-perfect packings, regime C achieves $n^{(k+1)}\le n^{\left(k\right)}/7+O\left(\sqrt{n^{\left(k\right)}}\right)$ per level; the measured depth on finite data is $K^{\ast }=\lceil {log}_{7}n\rceil \pm 1$. Two variants complete the framework: regime A (full-overlap edge-induced, ${\mathcal{C}}^{\left(k\right)}=V^{\left(k\right)}$) adds native overlap semantics by preserving the EI meta-graph 6-regularity without reducing the vertex count; regime B (deterministic index-7 sublattice, ${\mathcal{C}}^{\left(k\right)}={\mathsf{\Lambda }}_k$ with $\mathbf{a}=2-\omega$) is a theoretical construction establishing an exact sublattice density ratio of 7 per level on the infinite lattice $\mathbb{T}$, and exact termination in $K={log}_{7}n$ levels on finite periodic domains with $n=7^K$. Unconditional results: EI 6-regularity in regime A; perfect star-tiling and exact index-7 structure in regime B; strict hierarchy via Voronoi-completed clusters in regime C; tile-shape alternation proven at levels 1–2 (hexagonal, then triangular-like) and conjectured, with numerical verification, beyond; $h_{\mathrm{Ward}}$ (as an unnormalised SSE) strictly admissible and $h_{max}$ weakly admissible. Aggregation complexity, embedding excluded: $O(nlogn)$ in regime C, $O\left(n\right)$ in regime B, $O(n·K_{max})$ in regime A. We provide a fully reproducible reference implementation (trihex2, MIT-licensed) with extensive parameter sweeps on UCI benchmarks, synthetic Gaussians, non-convex shapes, and overlapping distributions. The genuine contributions of the framework are the multi-scale hierarchical structure with provable geometric guarantees and, in regime A, native overlap semantics that no hard-clustering baseline can provide. A central empirical finding concerns the embedding: an ablation isolating the 2D-lattice projection shows it to be the main bottleneck, and a lattice-free variant that runs the same combinatorial core directly on a k-nearest-neighbour graph in the original feature space—with no embedding and no quantisation—removes the projection entirely and improves accuracy on six of seven pilot datasets. With a frozen, fully unsupervised meta-selection rule (graph-geodesic arbitration between a convex-consensus and a graph-min-cut candidate, no per-dataset tuning), this variant reaches ARI $0.871$ on moons and $1.000$ on circles, where k-means, HAC, and GMM all collapse to $\le 0.43$ and $\le 0.00$, respectively. On a 73-dataset benchmark (23 real UCI, 50 synthetic, all loaded with validated class labels), TriHex is the most frequently best method on the synthetic panel ($46\%$ win rate) and close behind GMM overall ($34\%$ versus $36\%$), while having the lowest mean ARI—the signature of a specialist: it dominates on non-convex structure (rings, spirals, manifolds) and is outperformed on convex tabular data, where we make no claim of superiority. We also report a genuine robustness limitation: with the default configuration, TriHex fails on Cancer (ARI $0.042$, essentially uncorrelated with the ground truth) because the default lattice over-fragments a two-class problem; competitive performance requires a dataset-appropriate configuration, and we report this explicitly rather than only the best configurations. On overlapping Gaussians, regime A detects the boundary points that the data-generating process itself classifies as ambiguous with precision $1.00$ at heavy overlap ($\delta =0.5$); the detector over-flags as the clusters separate (precision falls to $0.43$ at $\delta =3.0$), so its usefulness is confined to the strong-overlap regime. Within that regime, it provides a measurable capability unavailable to hard-clustering baselines.