One of the most important tasks for ensuring safe autonomous driving systems is accurately
detecting road traffic lights and accurately determining how they impact the driver's actions …

Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive
overview of the underlying model architectures for this task is currently missing. In this work …

The lack of discernible vehicle contour features in low-light conditions poses a formidable
challenge for nighttime vehicle detection under hardware cost constraints. Addressing this …

The challenge of evaluating deep learning-based object detection models in complex traffic
scenarios, characterized by changing weather and lighting conditions, is addressed in this …

With the advances of deep neural networks, there is progress on the detection and
recognition of traffic lights for advanced driver assistance systems (ADAS). However …

Decision-level information fusion methods using radar and vision usually suffer from low
target matching success rates and imprecise multi-target detection accuracy. Therefore, a …

Few of the known quantum algorithms can be reliably executed on a quantum computer.
Therefore, as an extension, we propose a Parallel Quantum Hough transform (PQHT) …

Smart transportation is being envisaged as an important parameter in building smart cities.
Although conceptualized to have major advantages, lack of intelligent systems makes more …

Autonomous driving faces a significant challenge in recognizing traffic signs in adverse
weather conditions. This research aims to address this challenge while also reducing the …

This study explores the application of multi-agent reinforcement learning (MARL) to enhance
the decision-making, safety, and passenger comfort of Autonomous Vehicles (AVs) at …