Evolving and coevolving computer go players using neuroevolution.

The Go game is ancient very complex game with simple rules which still is a challenge for the AI.This work cover some neuroevolution techniques used in reinforcement learning applied to the GO game as SANE (Symbiotic Adaptive Neuro-Evolution) and presents a variation to this method with the intention of evolving better strategies in the game. The computer Go player based in SANE is evolved againts a knowed player which creates some problem as determinism for which is proposed the co-evolution. Finally, it is introduced an algorithm to co-evolve two populations of neurons to evolve better computer Go players

HMI Design in Vehicles based in Usability and Accesibility Concepts

Presently, there are a gl'eat variety of systems to aid the driving task. Their objective is to'optimize the safety, efficiency and comfoli of the transport, improving the functionality of the cars and the highways using functionalities supplied by the Information and Communication Technologies. However, most of these technologies have not been designed following accessibility and usability principies. In this papel'we describe the design and implementation of a new generation of Human-Machine Interface for road vehicles, based in user centered design and accessibility concepts

Comparison between Floating Car Data and Infrastructure Sensors for Traffic Speed Estimation

The development of new generation Intelligent Vehicle Technologies will enable a better level of road safety and CO2 emission reductions. However, the bottleneck of all of these systems is the need of a comprehensive and reliable data. For traffic data acquisition, two sources are available today: infrastructure sensors and floating vehicles. The first ones consist on a set of static underground sensors installed in the roads; the second ones consist of the use of intelligent vehicles as mobile sensors. Both of them make use of different communication systems, V2V, V2I and I2I. In this paper we present a comparison of the performance of both kinds of traffic data source for road traffic speed estimation. A set of real experiments has been performed in several traffic conditions, using infrastructure sensors and the information retrieved by one instrumented intelligent vehicle. After processing these data, the results show the better accuracy of the floating cat data as well as its low cost in the case of a massive implantation

Floating car data augmentation based on infrastructure sensors and neural networks

The development of new-generation intelligent vehicle technologies will lead to a better level of road safety and CO2 emission reductions. However, the weak point of all these systems is their need for comprehensive and reliable data. For traffic data acquisition, two sources are currently available: 1) infrastructure sensors and 2) floating vehicles. The former consists of a set of fixed point detectors installed in the roads, and the latter consists of the use of mobile probe vehicles as mobile sensors. However, both systems still have some deficiencies. The infrastructure sensors retrieve information fromstatic points of the road, which are spaced, in some cases, kilometers apart. This means that the picture of the actual traffic situation is not a real one. This deficiency is corrected by floating cars, which retrieve dynamic information on the traffic situation. Unfortunately, the number of floating data vehicles currently available is too small and insufficient to give a complete picture of the road traffic. In this paper, we present a floating car data (FCD) augmentation system that combines information fromfloating data vehicles and infrastructure sensors, and that, by using neural networks, is capable of incrementing the amount of FCD with virtual information. This system has been implemented and tested on actual roads, and the results show little difference between the data supplied by the floating vehicles and the virtual vehicles

Integración de diferentes sistemas ADAS en un interfaz de usuario adaptado a las características del conductor

Los sistemas ADAS suponen una ayuda al conductor y se ha puesto de manifiesto el gran potencial de la integración de información proveniente de diferentes fuentes, con el fin de lograr mejoras en la seguridad superiores a la suma de los efectos individuales de cada dato aislado. Sin embargo, la proliferación de sistemas de asistencia a la conducción en los vehículos ha provocado que los interfaces de usuario puedan llegar a ser confusos, poco intuitivos y suponer una distracción para el conductor más que una ayuda. Además, existe una gran variedad de conductores con capacidades muy diversas, lo que hace que un único interfaz de usuario pueda no ser adecuado para todos ellos y deba recurrirse a adaptaciones personalizadas. En este sentido, se distinguen dos tipos de avisos. Por una parte, se incluyen los avisos de situaciones de riesgo como aproximación a curvas a velocidad excesiva, desplazamiento hacia los límites del carril, intento de adelantamiento con falta de visibilidad, circulación hacia un obstáculo, entre otras. Por otra parte, se considera la función de asistencia a la conducción lejos de las situaciones críticas de circulación, especialmente orientada a personas de edad avanzada o con alguna capacidad disminuida de forma que su percepción del entorno y/o sus tiempos de reacción sean peores que la media, de manera que se puedan paliar estar carencias con un sistema de aviso y ayuda. En el presente trabajo se muestra el diseño de un interfaz de usuario integrado para diversas aplicaciones ADAS considerando criterios de usabilidad y accesibilidad en su diseño, así como diferentes tipos de usuario