Journal of Electronic Systems Pit Pichappan 16 1 2026 https://doi.org/10.6025/jes/2026/16/1/17-30 https://www.dline.info/jes/fulltext/v16n1/jesv16n1_2.pdf This work establishes a principled framework for contextual intelligence in autonomous scene understanding, moving beyond isolated object detection toward holistic environmental interpretation. Leveraging the nuImages dataset, we quantify semantic dependencies through four integrated components: (1) object– object relationship graphs revealing statistically significant co occurrences (e.g., pedestrian crosswalk correlation of 0.91); (2) conditional probability models demonstrating how contextual cues modulate object presence sidewalks elevate pedestrian likelihood to 0.70 versus a marginal baseline of 0.34, while highways suppress it to 0.08; (3) a multi dimensional Scene Complexity Index (SCI) that stratifies environments by semantic density and interaction potential, yielding medians of 8.1 (urban), 5.2 (residential), and 2.3 (highway); and (4) context aware risk scoring that quantifies perception uncertainty across scene typologies (urban: 0.53; residential: 0.33; highway: 0.08). Critically, we establish a near deterministic positive correlation between scene complexity and perception risk (r = 0.977, R² = 0.954, p < 0.001), validating that environmental semantics directly govern detection uncertainty. These findings substantiate a paradigm shift toward context adaptive perception architectures that proactively allocate computational resources, adjust detection thresholds, and anticipate hazards based on environmental typology. By formalizing contextual priors through statistical models rather than heuristic rules, our framework enhances robustness in safety critical autonomous applications particularly in complex urban environments where semantic ambiguity and multi agent interactions dominate failure modes.