Transformer-Based Spatio-Temporal Attention Optimization for Real-Time Autonomous Driving Video Segmentation (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Real-time video segmentation constitutes a fundamental perception capability for autonomous driving systems, enabling fine-grained scene understanding under stringent latency constraints. The emergence of vision transformer architectures has introduced powerful global context modeling, yet their computational intensity poses significant challenges for deployment on resource-constrained vehicular platforms. This paper presents a comprehensive systems-level analysis of transformer-based spatio-temporal attention optimization for autonomous driving video segmentation, examining the structural trade-offs, architectural design principles, and infrastructural requirements that govern the transition from laboratory benchmarks to safety-critical real-world operation. We investigate the interplay between spatial attention precision, temporal coherence maintenance, and inference latency within embedded computing environments, while addressing broader considerations of robustness, fairness, governance, and sustainability. The discussion encompasses hardware-aware model design, distributed edge-cloud processing topologies, adversarial resilience mechanisms, and algorithmic equity across diverse operational design domains. Through a synthesis of contemporary research and engineering practice, we identify critical pathways for achieving the reliability, efficiency, and ethical alignment necessary for large-scale autonomous deployment. The paper further explores policy frameworks, standardization imperatives, and the societal dimensions of perceptual artificial intelligence in mobility systems, offering a multidisciplinary perspective that bridges systems engineering with socio-technical governance.
Keywords: autonomous driving, video segmentation, vision transformer, spatio-temporal attention, real-time systems, edge computing, algorithmic fairness