Biologically inspired visual intelligence for unmanned ground vehicles

Human drivers effectively navigate through most outdoor unstructured environments purely based on their visual inputs. The human visual system has a capability to learn and adapt to its surrounding environment robustly despite the complexity possessed by ground...

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Main Author: Peyman Moghadam
Other Authors: Wang Dan Wei
Format: Theses and Dissertations
Language:English
Published: 2012
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Online Access:https://hdl.handle.net/10356/48017
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-480172023-07-04T16:54:55Z Biologically inspired visual intelligence for unmanned ground vehicles Peyman Moghadam Wang Dan Wei Wijerupage Sardha Wijesoma School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Human drivers effectively navigate through most outdoor unstructured environments purely based on their visual inputs. The human visual system has a capability to learn and adapt to its surrounding environment robustly despite the complexity possessed by ground cover variations, uncontrolled lighting, weather conditions, and shadows. However, none of the artificial perceptual systems in the current state of autonomous Unmanned Ground Vehicles (UGVs) have this level of intelligent perception that can continuously cope with the various unexpected situations and perform robustly in dynamic environments. Therefore, understanding the mechanisms underlying the intelligent abilities of a human driver can be the key to design fully autonomous UGVs. The main philosophy underlying this thesis is to understand how humans use their vision systems (eyes) and processing units (brains) in driving scenarios in order to develop humanlike visual intelligence for UGVs that can navigate autonomously in an unstructured outdoor environment. To achieve this goal two important aspects need to be investigated. First aspect of this research addresses the case where a UGV is expected to navigate in a familiar outdoor unstructured environment. Based on empirical evidences, it is known that when human drivers become familiar with the road situations, they prefer to look at the far field where the road edges converge (called the vanishing point) to anticipate the upcoming road trajectory and the car steering with maximal lead time. Based on these findings, vanishing point is considered as a salient and consistent feature during most of the open driving behavior tasks regardless of type of the environments. Hence, in this work, a computational model for the vanishing point estimation based on the visual gaze behavior of drivers is developed for UGVs. The proposed model uses Local Dominant Orientation (LDO) patterns such as road edges, ruts and tire tracks left by previous vehicles to vote for the global convergence point of the road in the visual field. For robust processing of Local Dominant Orientation (LDO), a novel biologically inspired mechanism is proposed utilizing distributed population coding and the opponency mechanism, found in the activities of ensemble of correlated neurons in the primary visual cortex of the mammalian brain. Apart from being biologically plausible, the proposed LDO model significantly outperforms the state of the art LDO methods in terms of both accuracy and robustness in natural and synthetic images. DOCTOR OF PHILOSOPHY (EEE) 2012-02-14T01:13:06Z 2012-02-14T01:13:06Z 2012 2012 Thesis Peyman, M. (2012). Biologically inspired visual intelligence for unmanned ground vehicles. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/48017 10.32657/10356/48017 en 230 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Peyman Moghadam
Biologically inspired visual intelligence for unmanned ground vehicles
description Human drivers effectively navigate through most outdoor unstructured environments purely based on their visual inputs. The human visual system has a capability to learn and adapt to its surrounding environment robustly despite the complexity possessed by ground cover variations, uncontrolled lighting, weather conditions, and shadows. However, none of the artificial perceptual systems in the current state of autonomous Unmanned Ground Vehicles (UGVs) have this level of intelligent perception that can continuously cope with the various unexpected situations and perform robustly in dynamic environments. Therefore, understanding the mechanisms underlying the intelligent abilities of a human driver can be the key to design fully autonomous UGVs. The main philosophy underlying this thesis is to understand how humans use their vision systems (eyes) and processing units (brains) in driving scenarios in order to develop humanlike visual intelligence for UGVs that can navigate autonomously in an unstructured outdoor environment. To achieve this goal two important aspects need to be investigated. First aspect of this research addresses the case where a UGV is expected to navigate in a familiar outdoor unstructured environment. Based on empirical evidences, it is known that when human drivers become familiar with the road situations, they prefer to look at the far field where the road edges converge (called the vanishing point) to anticipate the upcoming road trajectory and the car steering with maximal lead time. Based on these findings, vanishing point is considered as a salient and consistent feature during most of the open driving behavior tasks regardless of type of the environments. Hence, in this work, a computational model for the vanishing point estimation based on the visual gaze behavior of drivers is developed for UGVs. The proposed model uses Local Dominant Orientation (LDO) patterns such as road edges, ruts and tire tracks left by previous vehicles to vote for the global convergence point of the road in the visual field. For robust processing of Local Dominant Orientation (LDO), a novel biologically inspired mechanism is proposed utilizing distributed population coding and the opponency mechanism, found in the activities of ensemble of correlated neurons in the primary visual cortex of the mammalian brain. Apart from being biologically plausible, the proposed LDO model significantly outperforms the state of the art LDO methods in terms of both accuracy and robustness in natural and synthetic images.
author2 Wang Dan Wei
author_facet Wang Dan Wei
Peyman Moghadam
format Theses and Dissertations
author Peyman Moghadam
author_sort Peyman Moghadam
title Biologically inspired visual intelligence for unmanned ground vehicles
title_short Biologically inspired visual intelligence for unmanned ground vehicles
title_full Biologically inspired visual intelligence for unmanned ground vehicles
title_fullStr Biologically inspired visual intelligence for unmanned ground vehicles
title_full_unstemmed Biologically inspired visual intelligence for unmanned ground vehicles
title_sort biologically inspired visual intelligence for unmanned ground vehicles
publishDate 2012
url https://hdl.handle.net/10356/48017
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