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

September 1, 2014 in Parma-Vislab, Publications, year 4 by admin

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 perspective images, obtained by un-warping 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.

@inproceedings{iv2014vcharge,
author="Alberto Broggi AND Elena Cardarelli AND Stefano Cattani AND Paolo Medici AND Mario Sabbatelli",
title="{Vehicle detection for autonomous parking using a Soft-Cascade AdaBoost classifier}",
booktitle = "Procs.~IEEE Intelligent Vehicles Symposium 2014",
pages="912--917",
year=2014,
address = "Dearbon, MI, USA",
month=jun
}

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Toward Automated Driving in Cities using Close-to-Market Sensors, an Overview of the V-Charge Project

July 1, 2013 in Bosch, ETHZ-ASL, ETHZ-CVG, Oxford-MRG, Parma-Vislab, Publications, TUB, VW by admin

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 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.


@inproceedings{furgale_iv13,
Address = {Gold Coast, Australia},
Annote = {full-conf-pb},
Author = {Paul Furgale and Ulrich Schwesinger and Martin Rufli and Wojciech Derendarz and Hugo Grimmett and Peter M\"{u}hlfellner and Stefan Wonneberger and Julian Timpner Stephan Rottmann and Bo Li and Bastian Schmidt and Thien Nghia Nguyen and Elena Cardarelli and Stefano Cattani and Stefan Br\"{u}ning and Sven Horstmann and Martin Stellmacher and Holger Mielenz and Kevin K\"{o}ser and Markus Beermann and Christian H\"{a}ne and Lionel Heng and Gim Hee Lee and Friedrich Fraundorfer and Ren\'{e} Iser and Rudolph Triebel and Ingmar Posner and Paul Newman and Lars Wolf and Marc Pollefeys and Stefan Brosig and Jan Effertz and C\'edric Pradalier and Roland Siegwart},
Booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
Month = {23--26 June},
Pages = {809--816},
Title = {{Toward Automated Driving in Cities using Close-to-Market Sensors, an Overview of the V-Charge Project}},
Year = {2013}
}

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