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 …

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 …

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 …

Vehicle light inspection is a crucial aspect of vehicle inspection, as vehicle lights play a vital
role in understanding vehicle behavior. Due to diverse ambient illumination, non-uniform …

In this study, we present a novel approach leveraging the segment anything model (SAM) for
the efficient detection and tracking of vehicles in urban traffic surveillance systems by …

Autonomous vehicles face significant challenges in accurately identifying vehicles, objects,
and traffic signals under adverse weather conditions and poor lighting. To address these …

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 …

Recent advancements in deep neural networks have significantly improved the detection
and recognition of traffic lights for advanced driver assistance systems (ADAS). Traditional …