Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic
environment characterized by complex spatial and temporal dynamics at various scales …

Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …

Software is increasingly vital, with automated systems regulating critical functions. As
development demands grow, manual code review becomes more challenging, often making …

Traffic Sign Recognition (TSR) plays a vital role in intelligent transportation systems (ITS) to
improve road safety and optimize traffic management. While existing TSR models perform …

Deep learning methods have shown outstanding results in various applications. Still, they
suffer from time-consuming training and inference due to multiple cascade layers, along with …

By integrating automated driving systems (ADS) and AI-driven advanced driver assistance
systems (ADAS) like the traffic sign detection (TSD) technology, the automotive sector can …

Autonomous vehicle navigation technology is increasing rapidly. However, automatic sign
recognition in complex illumination environments like low-light, hazy regions is a significant …

In the quest for autonomous vehicle safety and road infrastructure management, traffic sign
recognition (TSR) remains paramount. Recent advancements in accuracy across various …

Accurate traffic sign data recognition is crucial for enhancing safety in autonomous driving
system. However, recognizing traffic signs from natural scenes is challenging due to factors …

Traffic sign recognition is crucial for sustainable utilization of road assets. However, on real
roads, traffic signs are often obscured, making their recognition challenging. Unfortunately …