scholarly journals Improved target tracking with road network information

2009 ◽  
Author(s):  
Umut Orguner ◽  
Thomas B. Schon ◽  
Fredrik Gustafsson
Keyword(s):  
Target Tracking ◽  
Road Network ◽  
Network Information

Pedestrian network information extraction based on VGI

GEOMATICA ◽  
2018 ◽  
Vol 72 (3) ◽  
pp. 85-99 ◽  
Author(s):  
Xuejing Xie ◽  
Guojian Ou
Keyword(s):  
Information Extraction ◽  
Road Network ◽  
New Method ◽  
Location Based Service ◽  
Network Information ◽  
The Road ◽  
Pedestrian Network ◽  
Gps Trajectories ◽  
Data Extracting

Pedestrian network information plays an important role in pedestrian location based service (LBS), and its completeness determines the quality of a pedestrian LBS. This study used volunteered data and BaiduMap to research how to extract pedestrian network information on the basis of pedestrian GPS trajectories. The method extracts human road information by three steps: cleaning track data, extracting the road network, and detecting and analysing the recognised pedestrian road facilities. Once the road network information is extracted, the information regarding road facilities can be obtained, e.g., pedestrian crossings, overpasses, and underground passages. This paper describes a new method for incrementally updating electronic maps.

A quantitative approach to measure road network information based on edge diversity

2015 ◽  
Author(s):  
Xun Wu ◽  
Hong Zhang ◽  
Tian Lan ◽  
Weiwei Cao ◽  
Jing He
Keyword(s):  
Road Network ◽  
Quantitative Approach ◽  
Network Information

scholarly journals Pedestrian tracking with an infrared sensor using road network information

2012 ◽  
Vol 2012 (1) ◽  
Author(s):  
Per Skoglar ◽  
Umut Orguner ◽  
David Törnqvist ◽  
Fredrik Gustafsson
Keyword(s):  
Road Network ◽  
Infrared Sensor ◽  
Pedestrian Tracking ◽  
Network Information

Integration of a road network into a radar ground moving target tracking (GMTT) system and its performance evaluation

2009 ◽  
Author(s):  
Sam Blackman ◽  
Kathy Fong ◽  
Douglas E. Carroll ◽  
Justin Lancaster ◽  
Robert Dempster
Keyword(s):  
Performance Evaluation ◽  
Target Tracking ◽  
Road Network ◽  
Moving Target ◽  
Moving Target Tracking

scholarly journals A METHOD OF URBAN ROAD NETWORK EXTRACTION BASED ON FLOATING CAR TRAJECTORY DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1579-1585
Author(s):  
C. Mi ◽  
F. Lu
Keyword(s):  
Road Network ◽  
Network Data ◽  
Trajectory Data ◽  
Network Information ◽  
Urban Road ◽  
The Road ◽  
Road Network Extraction ◽  
Geometric Precision ◽  
Road Width ◽  
Adaptive Radius

<p><strong>Abstract.</strong> With the gradual opening of floating car trajectory data, it is possible to extract road network information from it. Currently, most road network extraction algorithms use unified thresholds to ignore the density difference of trajectory data, and only consider the trajectory shape without considering the direction of the trajectory, which seriously affects the geometric precision and topological accuracy of their results. Therefore, an adaptive radius centroid drift clustering method is proposed in this paper, which can automatically adjust clustering parameters according to the track density and the road width, using trajectory direction to complete the topological connection of roads. The algorithm is verified by the floating car trajectory data of a day in Futian District, Shenzhen. The experimental results are qualitatively and quantitatively analyzed with ones of the other two methods. It indicates that the road network data extracted by this algorithm has a significant improvement in geometric precision and topological accuracy, and which is suitable for big data processing.</p>

Vehicle routing problems with road-network information: State of the art

Networks ◽  
2018 ◽  
Vol 72 (3) ◽  
pp. 393-406 ◽  
Author(s):  
Hamza Ben Ticha ◽  
Nabil Absi ◽  
Dominique Feillet ◽  
Alain Quilliot
Keyword(s):  
Vehicle Routing ◽  
Road Network ◽  
State Of The Art ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Network Information ◽  
Information State

scholarly journals Tracking Multiple Vehicles Constrained to a Road Network From a UAV with Sparse Visual Measurements

2021 ◽  
Vol 8 ◽  
Author(s):  
Jared J. Moore ◽  
Craig C. Bidstrup ◽  
Cameron K. Peterson ◽  
Randal W. Beard
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Road Network ◽  
Particle Filters ◽  
Target Location ◽  
Data Association ◽  
Field Of View ◽  
Multiple Target ◽  
Multiple Vehicles ◽  
Aerial Vehicle

Multiple-target tracking algorithms generally operate in the local frame of the sensor and have difficulty with track reallocation when targets move in and out of the sensor field-of-view. This poses a problem when an unmanned aerial vehicle (UAV) is tracking multiple ground targets on a road network larger than its field-of-view. To address this problem, we propose a Rao-Blackwellized Particle Filter (RBPF) to maintain individual target tracks and to perform probabilistic data association when the targets are constrained to a road network. This is particularly useful when a target leaves and then re-enters the UAV’s field-of-view. The RBPF is structured as a particle filter of particle filters. The top level filter handles data association and each of its particles maintains a bank of particle filters to handle target tracking. The tracking particle filters incorporate both positive and negative information when a measurement is received. We implement two path planning controllers, receding horizon and deep reinforcement learning, and compare their ability to improve the certainty for multiple target location estimates. The controllers prioritize paths that reduce each target’s entropy. In addition, we develop an algorithm that computes the upper bound on the filter’s performance, thus facilitating an estimate of the number of UAVs needed to achieve a desired performance threshold.

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