The seeing passenger car 'VaMoRs-P'

Dickmanns, Ernst D.; Behringer, Reinhold; Dickmanns, Dirk; Hildebrandt, Thomas; Maurer, Markus; Thomanek, F.; Schiehlen, J.

doi:10.1109/ivs.1994.639472

Cited by 160 publications

(69 citation statements)

References 1 publication

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“…This is of interest for fully autonomous, self-driving cars in traffic (e.g., Dickmanns et al, 1994). The GPU-MPCNN ensemble obtained 0.56% error rate and was twice better than human test subjects, three times better than the closest artificial NN competitor (Sermanet and LeCun, 2011), and six times better than the best non-neural method.…”

Section: : Mpcnns On Gpu Achieve Superhuman Vision Performancementioning

confidence: 99%

Deep learning in neural networks: An overview

2015

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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.LATEX source: http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.tex Complete BIBTEX file (888 kB): http://www.idsia.ch/˜juergen/deep.bib Preface This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using "local search" to follow citations of citations backwards in time. Since not all DL publications properly acknowledge earlier relevant work, additional global search strategies were employed, aided by consulting numerous neural network experts. As a result, the present preprint mostly consists of references. Nevertheless, through an expert selection bias I may have missed important work. A related bias was surely introduced by my special familiarity with the work of my own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to juergen@idsia.ch.

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Section: : Mpcnns On Gpu Achieve Superhuman Vision Performancementioning

confidence: 99%

Deep learning in neural networks: An overview

2015

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“…For the sake of vehicular comfort, efficiency, and safety, research groups all over the world have worked on building autonomous technical systems that can in part replicate such capability (Bertozzi, Broggi, & Fasciol 2000;Dickmanns et al, 1994;Franke et al, 2001;Nagel, Enkelmann, & Struck, 1995;Thorpe, 1990).…”

Section: Introductionmentioning

confidence: 99%

Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge

Kammel

Ziegler

Pitzer

et al. 2008

Journal of Field Robotics

204

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This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner that delivers both range and reflectivity measurements. Whereas range measurements are used to provide three-dimensional scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition. C 2008 Wiley Periodicals, Inc.

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“…A major sensory input is vision and it is a challenge for robotics to extract all the relevant information from road image sequences acquired by a television (TV) camera mounted on a robot or an autonomous vehicle. Several vehicles are already able to drive along outdoor scenes using vision, such as the VAMORS [1]- [4], the VITA II [5], [6] and CMU-NAVLAB vehicles [7], [8]. The vision systems of these vehicles are focused on the detection of those features necessary to control the steering position and the detection of obstacles.…”

Section: Introductionmentioning

confidence: 99%

The use of optical flow for road navigation

Giachetti

Campani

Torre

1998

IEEE Trans. Robot. Automat.

207

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Abstract-This paper describes procedures for obtaining a reliable and dense optical flow from image sequences taken by a television (TV) camera mounted on a car moving in usual outdoor scenarios. The optical flow can be computed from these image sequences by using several techniques. Differential techniques to compute the optical flow do not provide adequate results, because of a poor texture in images and the presence of shocks and vibrations experienced by the TV camera during image acquisition. By using correlation based techniques and by correcting the optical flows for shocks and vibrations, useful sequences of optical flows can be obtained. When the car is moving along a flat road and the optical axis of the TV camera is parallel to the ground, the motion field is expected to be almost quadratic and have a specific structure. As a consequence the egomotion can be estimated from this optical flow and information on the speed and the angular velocity of the moving vehicle are obtained. By analyzing the optical flow it is possible to recover also a coarse segmentation of the flow, in which objects moving with a different speed are identified. By combining information from intensity edges a better localization of motion boundaries are obtained. These results suggest that the optical flow can be successfully used by a vision system for assisting a driver in a vehicle moving in usual streets and motorways.

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The seeing passenger car 'VaMoRs-P'

Cited by 160 publications

References 1 publication

Deep learning in neural networks: An overview

Deep learning in neural networks: An overview

Team AnnieWAY's autonomous system for the 2007 DARPA Urban Challenge

The use of optical flow for road navigation

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