Teresa Seco

Video anomaly detection plays a crucial role in intelligent transportation systems by enhancing urban mobility and safety. This review provides a comprehensive analysis of recent advancements in artificial intelligence methods applied to traffic anomaly detection, including convolutional and recurrent neural networks (CNNs and RNNs), autoencoders, Transformers, generative adversarial networks (GANs), and multimodal large language models (MLLMs). We compare their performance across real-world applications, highlighting patterns such as the superiority of Transformer-based models in temporal context understanding and the growing use of multimodal inputs for robust detection. Key challenges identified include dependence on large labeled datasets, high computational costs, and limited model interpretability. The review outlines how recent research is addressing these issues through semi-supervised learning, model compression techniques, and explainable AI. We conclude with future directions focusing on scalable, real-time, and interpretable solutions for practical deployment.

Robot localization inside tunnels is a challenging task due to the special conditions of these environments. The GPS-denied nature of these scenarios, coupled with the low visibility, slippery and irregular surfaces, and lack of distinguishable visual and structural features, make traditional robotics methods based on cameras, lasers, or wheel encoders unreliable. Fortunately, tunnels provide other types of valuable information that can be used for localization purposes. On the one hand, radio frequency signal propagation in these types of scenarios shows a predictable periodic structure (periodic fadings) under certain settings, and on the other hand, tunnels present structural characteristics (e.g., galleries, emergency shelters) that must comply with safety regulations. The solution presented in this paper consists of detecting both types of features to be introduced as discrete sources of information in an alternative graph-based localization approach. The results obtained from experiments conducted in a real tunnel demonstrate the validity and suitability of the proposed system for inspection applications.