The field of traffic surveillance and autonomous driving is witnessing significant advancements, driven by innovative applications of deep learning and computer vision techniques. Researchers are focusing on improving the accuracy and efficiency of traffic sign recognition, crash detection, and accident anticipation systems. Novel architectures and models are being proposed to address the challenges posed by variable environmental conditions, occlusion, and class imbalance. The integration of attention mechanisms, transformers, and metaformers is leading to improved performance and robustness in these systems. Furthermore, the development of lightweight and efficient models is enabling real-time operation on resource-limited hardware, making them suitable for deployment in autonomous vehicles and advanced driver-assistance systems. Notable papers in this area include:

• Enhancing Traffic Sign Recognition On The Performance Based On Yolov8, which presents an enhanced YOLOv8-based detection system with advanced data augmentation techniques and novel architectural enhancements.
• LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine, which introduces a lightweight attention-based engine that achieves state-of-the-art predictive capabilities and computational efficiency.
• EMF: Event Meta Formers for Event-based Real-time Traffic Object Detection, which proposes a novel event-based object detection backbone that outperforms the state-of-the-art on the Prophesee Gen1 dataset.