The future of autonomous driving is slowly approaching, but there are still many steps to take before it can become a reality. It is crucial to pay attention to road infrastructure, because without it, intelligent vehicles will not be able to operate reliably, and it will never be possible to dispense of driver's control. This paper presents the work carried out for the detection of road markings damage using computer vision techniques. This is a complex task for which there are currently not many papers and large image sets in the literature. This study uses images from the public Road Damage Detection dataset for the D44 defect and also provides 971 new labelled images for Spanish roads. For this purpose, three detectors based on deep learning architectures (Faster RCNN, SDD and EfficientDet) have been used and single-source and mixed-source models have been studied to find the model that best fits the target images. Finally, F1-score values reaching 0.929 and 0.934 have been obtained for Japanese and Spanish images respectively which improve the state-of-the-art results by 25%. It can be concluded that the results of this study are promising, although the collection of many more images will be necessary for the scientific community to continue advancing in the future in this field of research.

European road safety has improved greatly in recent decades. However, the current numbers are still far away to reach the European Commission's road safety targets. In this context, Cooperative Intelligent Transport Systems (C-ITS) are expected to significantly improve road safety, traffic efficiency and comfort of driving, by helping the driver to make better decisions and adapt to the traffic situation. This paper puts forward two vision-based applications for traffic sign recognition (TSR) and real-time weather alerts, such as for fog-banks. These modules will support operators in road infrastructure maintenance tasks as well as drivers, giving them valuable information via C-ITS messages. Different state-of-the-art methods are analysed using both publicly available datasets (GTSB) as well as our own image databases (Ceit-TSR and Ceit-Foggy). The selected models for TSR implementation are based on Aggregated Chanel Features (ACF) and Convolutional Neural Networks (CNN) that reach more than 90% accuracy in real time. Regarding fog detection, an image feature extraction method on different colour spaces is proposed to differentiate sunny, cloudy and foggy scenes, as well as its visibility level. Both applications are already running in an onboard probe vehicle system.

Featured Application Comparison two digital solutions (tablet based and Augmented Reality based) for bus maintenance against the traditional solution based on paper. This paper shows two developed digital systems as an example of intelligent garage and maintenance that targets the applicability of augmented reality and wearable devices technologies to the maintenance of bus fleets. Both solutions are designed to improve the maintenance process based on verification of tasks checklist. The main contribution of the paper focuses on the implementation of the prototypes in the company's facilities in an operational environment with real users and address the difficulties inherent in the transfer of a technology to a real work environment, such as a mechanical workshop. The experiments have been conducted in real operation thanks to the involvement of the public transport operator DBUS, which operates public transport buses in the city of Donostia-San Sebastian (Spain). Two solutions have been developed and compared against the traditional process: one based on Tablet and another one based on Microsoft HoloLens. The results show objective metrics (Key Performance Indicators, KPI) as well as subjective metrics based on questionnaires comparing the two technological approaches against the traditional one based on manual work and paper.

As urbanization increases, mobility becomes more complex and supposes a major challenge for public transportation authorities. An approach to tackle this problem is to introduce systems that ease vehicle operation and monitoring. However, these systems are hard to maintain and integrate. On this paper, a new information technology (IT) architecture is introduced. Developed by the ITxPT association, the architecture aims to improve the cost-effective implementation of the IT modules in public transport fleets. This architecture has capitalized the work of several European projects to create a solid standard ready to test on field. Finally, a use case of the introduction of an eco-driving assistance system that employs this architecture in real operation is presented.

Purpose This article focuses on a novel Advanced Train LocAtion Simulator (ATLAS) for on-board railway location using wireless communication technologies, such as satellite navigation and location based systems. ATLAS allows the creation of multiple simulation environments providing a versatile tool for testing and assessing new train location services. This enhancement reduces the number of tests performed in real scenarios and trains, reducing the cost and development time of new location systems as well as assessing the performance level for given tracks. Methods The simulation platform is based on modular blocks, where each block can be replaced or improved. The platform uses Monte Carlo Simulation to generate results with statistical significance. This implementation allows the modification of the development platform to cover multiple requirements, such as, ranging errors in the input parameters or including other positioning technologies. In this paper, the generated input parameter errors have been taken from the results of the field tests realized by the 3GPP ensuring the validity of the used parameter errors. However, these could be easily adapted by the user to particular characterized environments. Results Case studies for the validation of ATLAS will be also introduced, including preliminary results related to the use of Global System for Mobile communications in Railway (GSM-R) and Universal Mobile Telecommunications System (UMTS) technologies for positioning. The validation stage provides a way to test the platform functionalities and verify its flexibility. Conclusions The versatility of the platform to perform simulations using same configuration parameters for different case studies can be highlighted. Furthermore, first conclusions are drawn from the obtained results. The characterization of the infrastructure for the simulation and the performance improvement of the location systems in the tunnels (e.g., by including Inertial Measurement Unit (IMU)) are necessary to achieve accuracy levels that can be valid for ETCS level 3.

Automotive simulators are an important tool when developing and validating new vehicle functions and devices. Thereby, configurability is an essential requirement for simulators that are used in the field of research. The growing number of new systems makes it imperative to reduce their time to market to a minimum. Furthermore, full digital instrument clusters are becoming more common inside consumer vehicles today. They erase the traditional limitations of analog dashboards and enable the design of intuitive and efficient solutions, for instance, by integrating various onboard devices in one central human-machine interface (HMI). Naturally, this generates new challenges in the human-in-the-loop model of validation. Driven by these demands, a new architecture is proposed that allows a complete (but easy) reconfiguration of a driving simulator, in particular, at the front end (the HMI). The core element is a Virtual Instrument Cluster consisting of a personal computer and a high-resolution thin film transistor (TFT) screen. Focusing on the interaction between the driver and the simulator, the objective is to facilitate the integration of new systems and designs into a realistic environment. To show the potential of the Virtual Instrument Cluster, this paper presents three practical examples to demonstrate the capability, usability, validity and adaptivity to new systems.