Future requirements for drastic reduction of CO2 production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times.Automated cars have the potential to reduce the environmental impact of driving, and increase the safety of motor vehicle travel. The current state-of-the-art in vehicle automation requires a suite of expensive sensors. While the cost of these sensors is decreasing, integrating them into electric cars will increase the price and represent another barrier to adoption.The V-Charge Project, funded by the European Commission, seeks to address these problems simultaneously by developing an electric automated car, outfitted with close-to-market sensors, which is able to automate valet parking and recharging for integration into a future transportation system. The final goal is the demonstration of a fully operational system including automated navigation and parking. This paper presents an overview of the V-Charge system, from the platform setup to the mapping, perception, and planning sub-systems.
Abstract-This paper presents a monocular algorithm for front and rear vehicle detection, developed as part of the FP7 V-Charge project's perception system. The system is made of an AdaBoost classifier with Haar Features Decision Stump. It processes several virtual perspective images, obtained by unwarping 4 monocular fish-eye cameras mounted all-around an autonomous electric car. The target scenario is the automated valet parking, but the presented technique fits well in any general urban and highway environment. A great attention has been given to optimize the computational performance. The accuracy in the detection and a low computation costs are provided by combining a multiscale detection scheme with a Soft-Cascade classifier design. The algorithm runs in real time on the project's hardware platform.The system has been tested on a validation set, compared with several AdaBoost schemes, and the corresponding results and statistics are also reported.
Abstract-Autonomous Ground Vehicles designed for extreme environments (e.g mining, constructions, defense, exploration applications) require a reliable estimation of terrain traversability, in terms of both terrain slope and obstacles presence. In this paper we present a new technique to build, in real time and only from a 3D points cloud, a dense terrain elevation map able to: 1) provide slope estimation; 2) provide a reference for segmenting points into terrain's inliers and outliers, to be then used for obstacles detection. The points cloud is first smartly sampled into a 2.5 grid map, then samples are fitted into a rational B-Spline surface by means of re-weighted least square fitting and equalization. To meet an extensive range of extreme off-road scenarios, no assumptions on vehicle pose are made and no road infrastructure or a-priori knowledge about terrain appearance and shape is required. The algorithm runs in real time; it has been tested on one of VisLab's AGVs using a modified SGM-based stereo system as 3D data source.
Abstract-This paper presents the control system of an autonomous vehicle capable of perceiving and describing the environment using different inputs, such as GPS waypoints, roadways borders and lines, leader vehicles, and obstacles to be avoided. The controller has been implemented and tested for the VisLab Intercontinental Autonomous Challenge, a long intercontinental trip that aims to demonstrate capabilities of modern autonomous vehicles. To fulfill this mission a generalpurpose real-time motion planning system was designed and implemented. This pathplanner, based on the estimation of feasible trajectories on a cost map, is described and analyzed. System perfomance has been evaluated during tests: experimental results have demonstrated the capability of the system in vehicle following.
This paper introduces a novel Human Machine Interface (HMI) that allows users to interact with a fleet of Automated Guided Vehicles (AGVs) used for logistics operations in industrial environments. The interface is developed for providing operators with information regarding the fleet of AGVs, and the status of the industrial environment. Information is provided in an intuitive manner, utilizing a three-dimensional representation of the elements in the environment. The HMI also allows operators to influence the behavior of the fleet of AGVs, manually inserting missions to be accomplished
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