Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classi- fier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

Vehicular traffic surveillance and road lane detection using radar interferometry

Speed enforcement on public roadways is an important issue in order to guarantee road security and to reduce the number and seriousness of traffic accidents. Traditionally, this task has been partially solved using radar and/or laser technologies and, more recently, using video-camera based systems. All these systems have significant shortcomings that have yet to be overcome. The main drawback of classical Doppler radar technology is that the velocity measurement fails when several vehicles are in the radars beam. Modern radar systems are able to measure speed and range between vehicle and radar. However, this is not enough to discriminate the lane where the vehicle is driving on. The limitation of several vehicles in the beam is overcome using laser technology. However, laser systems have another important limitation: They cannot measure the speed of several vehicles simultaneously. Novel video-camera systems, based on license plate identification, solve the previous drawbacks, but they have the problem that they can only measure average speed but never top-speed. This paper studies the feasibility of using an interferometric linear frequency modulated continuous wave radar to improve top-speed enforcement on roadways. Two different systems based on down-the-road and across-the-road radar configurations are presented. The main advantage of the proposed solutions is they can simultaneously measure speed, range, and lane of several vehicles, allowing the univocal identification of the offenders. A detailed analysis about the operation and accuracy of these solutions is reported. In addition, the feasibility of the proposed techniques has been demonstrated with simulations and real experiments using a Ka-band interferometric radar developed by our research group.

Development of a methodology and tool to evaluate the impact of ICT measures on road transport emissions

The paper presents the main elements of a project entitled ICT-Emissions that aims at developing a novel methodology to evaluate the impact of ICT-related measures on mobility, vehicle energy consumption and CO2 emissions of vehicle fleets at the local scale, in order to promote the wider application of the most appropriate ICT measures. The proposed methodology combines traffic and emission modelling at micro and macro scales. These will be linked with interfaces and submodules which will be specifically designed and developed. A number of sources are available to the consortium to obtain the necessary input data. Also, experimental campaigns are offered to fill in gaps of information in traffic and emission patterns. The application of the methodology will be demonstrated using commercially available software. However, the methodology is developed in such a way as to enable its implementation by a variety of emission and traffic models. Particular emphasis is given to (a) the correct estimation of driver behaviour, as a result of traffic-related ICT measures, (b) the coverage of a large number of current vehicle technologies, including ICT systems, and (c) near future technologies such as hybrid, plug-in hybrids, and electric vehicles. The innovative combination of traffic, driver, and emission models produces a versatile toolbox that can simulate the impact on energy and CO2 of infrastructure measures (traffic management, dynamic traffic signs, etc.), driver assistance systems and ecosolutions (speed/cruise control, start/stop systems, etc.) or a combination of measures (cooperative systems).The methodology is validated by application in the Turin area and its capacity is further demonstrated by application in real world conditions in Madrid and Rome.