Tight integration of digital map and in-vehicle positioning unit for car navigation in urban areas

2003 ◽  
Vol 8 (2) ◽  
pp. 551-556 ◽  
Author(s):  
Chen Wu ◽  
Yu Meng ◽  
Li Zhi-lin ◽  
Chen Yong-qi ◽  
J. Chao
Keyword(s):  
Urban Areas ◽  
Digital Map ◽  
Vehicle Positioning ◽  
Tight Integration ◽  
Car Navigation

scholarly journals Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1181
Author(s):  
Keisuke Yoneda ◽  
Akisuke Kuramoto ◽  
Naoki Suganuma ◽  
Toru Asaka ◽  
Mohammad Aldibaja ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spatial Information ◽  
Recognition Performance ◽  
Computational Cost ◽  
Region Of Interest ◽  
Traffic Light ◽  
Automated Driving ◽  
Digital Map ◽  
Traffic Lights

Traffic light recognition is an indispensable elemental technology for automated driving in urban areas. In this study, we propose an algorithm that recognizes traffic lights and arrow lights by image processing using the digital map and precise vehicle pose which is estimated by a localization module. The use of a digital map allows the determination of a region-of-interest in an image to reduce the computational cost and false detection. In addition, this study develops an algorithm to recognize arrow lights using relative positions of traffic lights, and the arrow light is used as prior spatial information. This allows for the recognition of distant arrow lights that are difficult for humans to see clearly. Experiments were conducted to evaluate the recognition performance of the proposed method and to verify if it matches the performance required for automated driving. Quantitative evaluations indicate that the proposed method achieved 91.8% and 56.7% of the average f-value for traffic lights and arrow lights, respectively. It was confirmed that the arrow-light detection could recognize small arrow objects even if their size was smaller than 10 pixels. The verification experiments indicate that the performance of the proposed method meets the necessary requirements for smooth acceleration or deceleration at intersections in automated driving.

scholarly journals Nonlinear Modeling of Azimuth Error for 2D Car Navigation Using Parallel Cascade Identification Augmented with Kalman Filtering

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Umar Iqbal ◽  
Jacques Georgy ◽  
Michael J. Korenberg ◽  
Aboelmagd Noureldin
Keyword(s):  
Kalman Filtering ◽  
Urban Areas ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Nonlinear Modeling ◽  
Land Vehicle ◽  
Bias Drift ◽  
Parallel Cascade Identification ◽  
Linear And Nonlinear ◽  
Car Navigation

Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in urban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and vehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial sensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed measurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates over time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors requires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade Identification (PCI) module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and residual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments in a land vehicle.

Utilizing GIS in the Development of Water Quality River Modeling for Penchala River, Malaysia

2014 ◽  
Vol 580-583 ◽  
pp. 2078-2081
Author(s):  
Faridah Othman ◽  
Alaa Eldin Mohamed Elamin ◽  
Siti Azireen Hezza Azahar ◽  
Siti Asiah Muhammad
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Urban Areas ◽  
Catchment Area ◽  
Storm Water ◽  
Rapid Manufacturing ◽  
Quality Model ◽  
Digital Map ◽  
River Modeling

The quality of river water has been an important issue, due to its significant important function to the human being. In Asia, Malaysia is considered as one of the most speedily urbanizing countries and it’s facing huge environmental challenges. An increased pressure on urban areas has been generated by rapid manufacturing especially in the Penchala River Basin. Penchala River originates from Kiara Hill and passes through several important townships as it flows through residential and industrial areas before meeting with Klang River. Although the river is relatively short, with an approximately 12 km in length with a catchment area of 28 kilometer square, this catchment is noted to be under environmental stress emanating from storm water pollution, solid waste, sedimentation and micro-pollutant. To study the water pollution issue, it is always best if it is tied up with the geographic information system. The objective of this study is to use the GIS for preparing and organizing the geometric data to satisfy the water quality model requirements. As a result of this study, a digital map for Penchala River has been created.

Efficient extraction of road information for car navigation applications using road pavement markings obtained from aerial images

2006 ◽  
Vol 33 (10) ◽  
pp. 1320-1331 ◽  
Author(s):  
Jin Gon Kim ◽  
Dong Yeob Han ◽  
Ki Yun Yu ◽  
Yong Il Kim ◽  
Sung Mo Rhee
Keyword(s):  
Urban Areas ◽  
Template Matching ◽  
Location Based Services ◽  
Aerial Images ◽  
Aerial Image ◽  
Ground Truth Data ◽  
Pavement Markings ◽  
Road Pavement ◽  
Efficient Extraction ◽  
Car Navigation

The efficient extraction of road information is increasingly important with the rapid growth of road-related services, such as car navigation systems, telematics, and location-based services. Conventional methods of creating and updating road information are expensive and time consuming. Therefore, a set of processes is required that collects the same information more efficiently. We propose a new method for collecting road information in complex urban areas from road pavement markings located on aerial images. This information includes lane and symbol markings that guide direction; the geometric properties of the pavement markings and their spatial relationships are analyzed. Road construction manuals and a series of cutting-edge techniques, including template matching, are used in our analysis. To validate our approach, the accuracy of our results was evaluated by comparing the data with manually extracted ground truth data. Our approach demonstrates that road information can be extracted efficiently to an extent in a complex urban area.Key words: aerial image, automatic extraction, pavement marking, road information, CNS.

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