DRIVERTIVE is a driverless cooperative vehicle developed at the University of Alcalá intended for autonomous operation on urban areas and highways. The DRIVERTIVE team won the Prize for the Best Team with Full Automation in the Grand Cooperative Driving Challenge (GCDC 2016) held in Helmond, The Netherlands, in 28-29 May 2016. (2015-2017).

We define an Assistive Pedestrian Crossing as a pedestrian crossing able to interact with users with disabilities and provide an adaptive response to increase, maintain or improve their functional capabilities while crossing. Thus, the infrastructure should be able to locate the pedestrians with special needs as well as to identify their specific disability. user location is obtained by means of a stereo-based pedestrian detection system. Disability identification is proposed by means of a RFID-based anonymous procedure from which pedestrians are only required to wear a portable and passive RFID tag. (2014-2016).

Automatic vehicle model detection is a still unresolved task, the need for a full vehicle identification approach is getting more relevant due to the increased demand for effectiveness and security. Current traffic surveillance applications, speed and access control platforms, automatic tollgate systems, etc., rely on the use of License Plate Recognition (LPR) systems that provide a unique and weak identifier for each detected vehicle: the license plate. A more detailed description of the different parameters of the vehicle would enhance current vehicle identification systems. Besides the license plate, vehicle colour, plate colour, car make, and finally, the car model, are representative variables of the vehicles. (2013-2015).

Company: Orbital Aerospace. Description: Development of an API in C++ to connect, configure and capture images from a FLIR camera using GigE Vision protocol. (2013).

The main goal of this project is to design, implement and test an intelligent transportation system able to manage a fleet of autonomous vehicles in a dedicated area to solve transportation needs on demand. This goal will lead us to solve important issues in global coordination of a fleet of vehicles; in autonomous vehicles area, like to join and leave traffic in roundabout, sensor fusion for positioning improving, positioning backing systems to improve reliability, etc. And also it will be needed to develop other functionalities to detect the system users: pedestrians that want to be transported, accident victims that need an ambulance, or even expensive items that need to moved. The specific goals are:

1. Develop a supervisor control system capable of integrating both infrastructure and vehicle information in order to manage the traffic in a dedicated area using wireless communications.

2. Develop fully-autonomous vehicles able to drive safely in dedicated areas to move persons or valuable items under real conditions.

3. Develop an infrastructure sensor and actuation (traffic lights) network able to give the needed data and operative control over the manually driven cars to the supervisor control system. In summary, work in the frontier of the knowledge in the intelligent transportation system field and, due to the available facilities we aim to run an open, public demonstration event in order to show the results of the project, showing the capacity of Spanish research institutions to go beyond the state of the art in frontier knowledge in the field of Intelligent Transportation Systems. (2012-2016).

Company: Euroconsult. Description: 2D/3D railway geometry monitoring for imperfection detection. For this project, high resolution LIDAR, cameras and IMU sensor are installed in a train and geometry of the tunnel and rails are processed using Point Cloud Library and OpenCV. (2012-2013).

The driverless public transportation systems, which are at present operating in some airports and train stations, are restricted to dedicated roads and exhibit serious trouble dynamically avoiding obstacles in the trajectory. In this project, an electric autonomous mini-bus is used during the demonstration event of the 2012 IEEE Intelligent Vehicles Symposium that took place in Alcalá de Henares (Spain). The demonstration consisted of a route 725 metres long containing a list of latitude-longitude points (waypoints). The mini-bus was capable of driving autonomously from one waypoint to another using a GPS sensor. Furthermore, the vehicle is provided with a multi-beam Laser Imaging Detection and Ranging (LIDAR) sensor for surrounding reconstruction and obstacle detection. When an obstacle is detected in the planned path, the planned route is modified in order to avoid the obstacle and continue its way to the end of the mission. On the demonstration day, a total of 196 attendees had the opportunity to get a ride on the vehicles. A total of 28 laps were successfully completed in full autonomous mode in a private circuit located in the National Institute for Aerospace Research (INTA), Spain. In other words, the system completed 20.3 km of driverless navigation and obstacle avoidance. Funded by CSIC. (2012).

In this project we have built a robotic research platform to develop high-level applications using the robotics development platform Robot Operating System (ROS). It is a differential traction platform equipped with odometry and distance sensors (ultrasound and infrared). It is designed to work indoors. The embedded cards run ROS modules to control the motors and to perceive the information from sensors. In this way the perception is completely transparent to the remote control station. The modular design has been chosen to increase the functionality and autonomy. In addition, we designed a 3D model in Gazebo simulator that can be used as prior before designing the actual application. (2012-2013).

One of the challenge still open in traffic sign recognition is to discard detected signs that do not pertain to the host road. The position of each detected traffic sign is obtained from stereo pair of cameras and those whose position are far from the vehicle lane will be discarded. However, there are some scenarios where the 3D relative position is not enough for discarding signs that do not apply to the host road, in those cases information from vehicle-to-infrastructure (V2I) communication system is proposed as solution, so V2I communication system using, wireless technology, works as support of the traffic sign recognition system. Financed by the Regional Government of Madrid. (2011).

The objective of Robocity2030-II is to develop an innovative integration of applications of Service Robots, in an effort to increase the quality of living of citizens in metropolitan areas. This means that the human is now the centre of things and the Service Robots are developed from, for and to the benefit of humans. To do so, the project brings together and coordinates the research of six leading Service Robot groups in the Community of Madrid, with around 70 projects of R+D in robotics in the past five years, nearly a third of which are European. (2010-2013).

In this project a real-time vision-based blind spot warning system that has been specially designed for motorcycles detection in both daytime and nighttime conditions. Motorcycles are fast moving and small vehicles that frequently remain unseen to other drivers, mainly in the blind-spot area. In fact, although in recent years the number of fatal accidents has decreased overall, motorcycle accidents have increased by 20%. The risks are primarily linked to the inner characteristics of this mode of travel: motorcycles are fast moving vehicles, light, unstable and fragile. These features make the motorcycle detection problem a difficult but challenging task to be solved from the computer vision point of view. In this project, we developed a daytime and nighttime vision-based motorcycle and car detection system in the blind spot area using a single camera installed on the side mirror. On the one hand, daytime vehicle detection is carried out using optical flow features and Support Vector Machine-based (SVM) classification. On the other hand, nighttime vehicle detection is based on head lights detection. The proposed system warns the driver about the presence of vehicles in the blind area, including information about the position and the type of vehicle. Extensive experiments have been carried out in 172 minutes of sequences recorded in real traffic scenarios in both daytime and nighttime conditions, in the context of the Valencia MotoGP Grand Prix 2009. (2010).

GUIADE's aim is the development of an autonomous public transport fleet based on a multi-modal perception of the environment, using information collected by the vehicles from the environment as well as from the infrastructure. Financed by the Spanish Ministry of Science and Innovation (MICINN). (2008-2011).

Company: 3M. Description: This project is a complement of VISUALISE. The goal is to install a RFID antenna in the VISUALISE vehicle and read the traffic sign RFID tag. Then, a matching process is applied to add the retrorreflection measurement with VISUALISE to the traffic sign history. Every traffic sign information (position, installation date, type of reflective sheet, etc.) is stored in a database to improve the quality of service in the road maintenance company. (2008-2010).

Company: Euroconsult. Description: VISUALISE is a high-performance unit for dynamic auscultation of traffic signs on roads. It allows automatic determination of the traffic signs conditions in regard with night visibility. By means of this Equipment it can be carried out signs retroreflection measurements at regular traffic speed. The main technological innovation of this equipment is that test data acquisition is performed dynamically. The system is installed in a vehicle that circulates at regular traffic speed. Valid data can be obtained while travelling at speeds up to 110 km/h. (2008-2010).