The VisLab of the University of Parma has been working on cognitive vehicles for more than 15 years and have successfully fielded a number of fully autonomous vehicles that shaped the history of robotic vehicles. These systems included perception, reasoning, and actuation. Other systems based on driver interaction are now being studied. VisLab has developed a number of perception systems, mainly in the vehicular robotics domain using artificial vision, laser-scanners, radar, and implementing high and low level fusion techniques. Some of these systems have been the main systems on which autonomous vehicles such as ARGO, TerraMax, and BRAiVE based their automatic features.

VisLab gained remarkable experience in the field of data fusion and provided sensing systems based on different sensors (using both direct and indirect measurements). Low and high level fusion have been implemented and tested on real prototypes. Moreover, VisLab is also focused on developing x-by-wire systems for autonomous vehicles.




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Vehicle detection for autonomous parking using a Soft-Cascade AdaBoost classifier

Alberto Broggi, Elena Cardarelli, Stefano Cattani, Paolo Medici, and Mario Sabbatelli IEEE Intelligent Vehicles Symposium 2014 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
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Toward Automated Driving in Cities using Close-to-Market Sensors, an Overview of the V-Charge Project

Paul Furgale et al. IEEE Intelligent Vehicles Symposium (IVS) 2013 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
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