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Biologically Inspired Online Learning of Visual Autonomous Driving
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-6096-3648
2014 (English)In: Proceedings British Machine Vision Conference 2014 / [ed] Michel Valstar; Andrew French; Tony Pridmore, BMVA Press , 2014, 137-156 p.Conference paper, Published paper (Refereed)
Abstract [en]

While autonomously driving systems accumulate more and more sensors as well as highly specialized visual features and engineered solutions, the human visual system provides evidence that visual input and simple low level image features are sufficient for successful driving. In this paper we propose extensions (non-linear update and coherence weighting) to one of the simplest biologically inspired learning schemes (Hebbian learning). We show that this is sufficient for online learning of visual autonomous driving, where the system learns to directly map low level image features to control signals. After the initial training period, the system seamlessly continues autonomously. This extended Hebbian algorithm, qHebb, has constant bounds on time and memory complexity for training and evaluation, independent of the number of training samples presented to the system. Further, the proposed algorithm compares favorably to state of the art engineered batch learning algorithms.

Place, publisher, year, edition, pages
BMVA Press , 2014. 137-156 p.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-110890DOI: 10.5244/C.28.94ISBN: 1901725529 (print)OAI: oai:DiVA.org:liu-110890DiVA: diva2:750039
Conference
British Machine Vision Conference 2014, Nottingham, UK September 1-5 2014
Note

The video contains the online learning autonomous driving system in operation. Data from the system has been synchronized with the video and is shown overlaid. The actuated steering singnal is visualized as the position of a blue dot. The steering signal predicted by the system is visualized by a green circle. During autonomous operation, these two coincide. When the vehicle is controlled manually (training), the word MANUAL is displayed in the video.The first sequence evaluates the ability of the system to stay on the road during road reconfiguration. The results of the first sequence indicate that the system primarily reacts to features on the road, not features in the surrounding area. The second sequence evaluates the multi-modal abilities of the system. After initial training, the vehicle follows the outer track, going straight in the two three-way junctions. By forcing the vehicle to turn right at one intersection, by means of a short application of manual control, a new mode is introduced. When the system later reaches the same intersection, the vehicle either turns or continues straight ahead depending on which of the two modes is the strongest. The ordering of the modes depends on slight variation in the approach to the junction and on noise.The third sequence is longer, evaluating both multi-modal abilities and effects of track reconfiguration. Container: MP4Codec: h264 1280x720

Available from: 2014-09-26 Created: 2014-09-26 Last updated: 2016-06-14Bibliographically approved
In thesis
1. Online Learning for Robot Vision
Open this publication in new window or tab >>Online Learning for Robot Vision
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In tele-operated robotics applications, the primary information channel from the robot to its human operator is a video stream. For autonomous robotic systems however, a much larger selection of sensors is employed, although the most relevant information for the operation of the robot is still available in a single video stream. The issue lies in autonomously interpreting the visual data and extracting the relevant information, something humans and animals perform strikingly well. On the other hand, humans have great diculty expressing what they are actually looking for on a low level, suitable for direct implementation on a machine. For instance objects tend to be already detected when the visual information reaches the conscious mind, with almost no clues remaining regarding how the object was identied in the rst place. This became apparent already when Seymour Papert gathered a group of summer workers to solve the computer vision problem 48 years ago [35].

Articial learning systems can overcome this gap between the level of human visual reasoning and low-level machine vision processing. If a human teacher can provide examples of what to be extracted and if the learning system is able to extract the gist of these examples, the gap is bridged. There are however some special demands on a learning system for it to perform successfully in a visual context. First, low level visual input is often of high dimensionality such that the learning system needs to handle large inputs. Second, visual information is often ambiguous such that the learning system needs to be able to handle multi modal outputs, i.e. multiple hypotheses. Typically, the relations to be learned  are non-linear and there is an advantage if data can be processed at video rate, even after presenting many examples to the learning system. In general, there seems to be a lack of such methods.

This thesis presents systems for learning perception-action mappings for robotic systems with visual input. A range of problems are discussed, such as vision based autonomous driving, inverse kinematics of a robotic manipulator and controlling a dynamical system. Operational systems demonstrating solutions to these problems are presented. Two dierent approaches for providing training data are explored, learning from demonstration (supervised learning) and explorative learning (self-supervised learning). A novel learning method fullling the stated demands is presented. The method, qHebb, is based on associative Hebbian learning on data in channel representation. Properties of the method are demonstrated on a vision-based autonomously driving vehicle, where the system learns to directly map low-level image features to control signals. After an initial training period, the system seamlessly continues autonomously. In a quantitative evaluation, the proposed online learning method performed comparably with state of the art batch learning methods.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 62 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1678
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-110892 (URN)10.3384/lic.diva-110892 (DOI)978-91-7519-228-4 (ISBN)
Presentation
2014-10-24, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (Swedish)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 247947Swedish Research Council
Available from: 2014-09-26 Created: 2014-09-26 Last updated: 2016-05-04Bibliographically approved

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