Efficient Road Scene Understanding for Intelligent Vehicles Using Compositional Hierarchical Models

Töpfer, Daniel; Spehr, Jens; Effertz, Jan; Stiller, Christoph

doi:10.1109/tits.2014.2354243

Cited by 29 publications

(13 citation statements)

References 22 publications

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“…Some gradient based algorithms can be commonly found in the literature, e.g., Sobel edge detector with symmetrical local threshold [93], adaptive thresholding [91], and gradient-enhancing conversion [94]. However, these algorithms are sensitive to noise and can result in a large number of outliers from clutter and shadows.…”

Section: Lane Line Marking Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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Section: Lane Line Marking Detectionmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…In the case of the global navigation satellite system (GNSS), the position of the receiver is calculated by using satellite networks through triangulation, utilizing the time at which the satellite signal arrives at the ground-surface receiver and the position information of other satellites. However, owing to various error factors, a positioning error of approximately 7 m occurs [ 10 , 11 , 12 , 13 , 14 , 15 ]. In addition, differential GNSS uses the ground master station to address satellite clock, ion/ionospheric, and orbit errors; the positioning error is approximately 2 m in this case, and obtaining an accurate measurement is still difficult [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of 2-D Positioned, Indoor, Fuzzy-Logic, Autonomous Navigation System Based on Chromaticity and Frequency-Component Analysis of LED Light

Jeong

Park

2021

Sensors

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Topics concerning autonomous navigation, especially those related to positioning systems, have recently attracted increased research attention. The commonly available global positioning system (GPS) is unable to determine the positions of vehicles in GPS-shaded regions. To address this concern, this paper presents a fuzzy-logic system capable of determining the position of a moving robot in a GPS-shaded indoor environment by analyzing the chromaticity and frequency-component ratio of LED lights installed under the ceiling. The proposed system’s performance was analyzed by performing a MATLAB simulation of an indoor environment with obstacles. During the simulation, the mobile robot utilized a fuzzy autonomous navigation system with behavioral rules to approach targets successfully in a variety of indoor environments without colliding with obstacles. The robot utilized the x and y coordinates of the fuzzy positioning system. The results obtained in this study confirm the suitability of the proposed method for use in applications involving autonomous navigation of vehicles in areas with poor GPS-signal reception, such as in tunnels.

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“…Regarding the characteristics of commonly used positioning sensors, the global navigation satellite system (GNSS) involves a satellite network to calculate the position of a receiver through triangulation by using the time of satellite signal arrival and the position of other satellites. However, it retrieves a measurement error of approximately 7 m due to various factors [1]- [11], rendering it inaccurate for autonomous vehicles. The differential GNSS uses a ground master station to handle satellite clock error, ion/ionospheric error, and orbit error, reducing the measurement error to approximately 2 m [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…However, Wi-Fi has a varying positioning accuracy depending on the installation interval and number of surrounding signal sources (i.e., access points). In the combined inertial navigation system-global positioning system (GPS), sensor bias and noise accumulate over time and are reflected in the measurements, thus sharply decreasing the positioning accuracy and reliability [1]- [11]. Although GPS is widely used for vehicle positioning to track an estimated vehicle position through satellite by transmitting navigation signals, it cannot operate when vehicles are within tunnels and other GPS-shaded areas [3].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Vehicle Positioning System Based on Chromaticity and Frequency Components of LED Illumination

2019

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Given their wide applicability, vehicle positioning systems are being actively studied currently. As the conventional global positioning system (GPS) cannot determine the vehicle position inside tunnels and other GPS-shaded areas, we propose to improve vehicle positioning using a fuzzy logic system that can retrieve the position of a moving vehicle in such areas. The proposed system analyzes the chromaticity and frequency components of active LED illumination, which is being commonly installed at locations like inside tunnels. We verify the system performance in a test environment that resembles a tunnel. As the proposed method uses the chromaticity and frequency of LED lights for vehicle positioning, it is suitable for areas where GPS reception is poor or unavailable.

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Efficient Road Scene Understanding for Intelligent Vehicles Using Compositional Hierarchical Models

Cited by 29 publications

References 22 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Numerical Analysis of 2-D Positioned, Indoor, Fuzzy-Logic, Autonomous Navigation System Based on Chromaticity and Frequency-Component Analysis of LED Light

Fuzzy Logic Vehicle Positioning System Based on Chromaticity and Frequency Components of LED Illumination

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