A Comprehensive Review of Transformer Based, Deep Learning, and Sensor Driven Approaches for Multi Type Vehicle Detection, Tracking, Counting, and Traffic Intelligence in Complex Traffic Environments

The rapid growth of urban traffic has led to increasing demand for intelligent, automated systems that can efficiently detect, track, and count multiple types of vehicles in real-time. Traditional sensor-based solutions are steadily being replaced by powerful AI-driven frameworks integrating deep learning, LiDAR analysis, stereo vision, Wi-Fi CSI, and transformer-based global attention architectures. Over the past decade, vehicle detection, tracking, and counting have evolved from simple background-subtraction mechanisms to sophisticated deep learning pipelines incorporating convolutional neural networks, transformer-based architectures, multimodal sensor fusion, and context-aware global feature modelling. This paper provides a comprehensive review of state-of-the-art methods, combining insights from modern CNN-based detectors such as YOLO, Retina Net, R-CNN, and transformer pipelines including DETR enhanced with residual global attention. Additional discussions include LiDAR-supported maneuverer classification, KLT-based motion analysis, shallow feature-fusion tracking models, and privacy-aware passenger estimation using wireless signals. Security concerns related to adversarial robustness, sensor spoofing, and data integrity are also addressed to ensure safe deployment in Intelligent Transportation Systems.