Context-Aware Scene Understanding for Autonomous Driving: Quantifying Semantic Dependencies, Complexity, and Perception Risk
Pit Pichappan
Journal of Electronic Systems, Vol. 16, No. 1, 2026.
DOI: https://doi.org/10.6025/jes/2026/16/1/17-30
Link: https://www.dline.info/jes/fulltext/v16n1/jesv16n1_2.pdf
Abstract:
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) objectâ€“
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, RÂ² = 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.