Elena Cardarelli

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Future requirements for drastic reduction of CO<sub>2</sub> 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.(More)
This paper describes a method for classifying road signs based on a single color camera mounted on a moving vehicle. The main focus will be on the final neural network based classification stage of the candidates provided by an existing traffic sign detection algorithm. Great attention is paid to image preprocessing in order to provide a more simple and(More)
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 un-warping 4 monocular fish-eye cameras mounted all-around an(More)
This paper presents the preliminary results of VIAC, the VisLab Intercontinental Autonomous Challenge, a test of autonomous driving along an unknown route from Italy to China. It took 3 months to run the entire test; all data have been logged, including all data generated by the sensors, vehicle data, and GPS info. This huge amount of information has been(More)
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(More)
This paper describes data fusion methodologies for obstacle detection in an automation system based on advanced Automatic Guided Vehicles (AGV), used for automated logistics in modern factories. We present the background of the problem, introducing generic aspects of the system architecture designed to cope with the obstacle detection in automated factory(More)
Road Sign Detection is a major goal of the Advanced Driving Assistance Systems. Most published work on this problem share the same approach by which signs are first detected and then classified in video sequences, even if different techniques are used. While detection is usually performed using classical computer vision techniques based on color and/or(More)
This paper presents a method for triangular and rectangular shapes detection in a road sign recognition system based on a three step algorithm: color segmentation, shape detection and neural network classification. The shape detector is based on the evaluation of the Sobel edges and Hough images in a region of interest detected by the color-based stage.(More)
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(More)