Accurate Speed Measurement from Vehicle Trajectories using AdaBoost Detection and Robust Template Tracking

2007 ◽  
Author(s):  
Bram Alefs ◽  
David Schreiber
Keyword(s):  
Vehicle Trajectories ◽  
Speed Measurement ◽  
Template Tracking

Field Theory for Some Traffic Phenomena and Fundamental Diagram

2021 ◽  
pp. 036119812110064
Author(s):  
Daiheng Ni
Keyword(s):  
Field Theory ◽  
Traffic Flow ◽  
Causal Effect ◽  
Microscopic Approach ◽  
Fundamental Diagram ◽  
Longitudinal Control ◽  
Effect Analysis ◽  
Vehicle Trajectories ◽  
Vehicle Motion ◽  
Cross Reference

A fundamental diagram consists of a scatter of traffic flow data sampled at a specific location and aggregated from vehicle trajectories. These trajectories, if presented equivalently, constitute a microscopic version of the (conventional) fundamental diagram. The cross-reference between vehicle trajectories and the microscopic fundamental diagram provides details of vehicle motion dynamics which allow causal-effect analysis on some traffic phenomena and further reveal the microscopic basis of the conventional fundamental diagram. This observation inspires theoretical modeling by a microscopic approach to address traffic phenomena and the conventional fundamental diagram. Derived from the field theory of traffic flow, the longitudinal control model is capable of serving the purpose without the modifications or exceptions used by other approaches.

scholarly journals Current-limiting amplifier for high speed measurement of resistive switching data

2021 ◽  
Vol 92 (5) ◽  
pp. 054701
Author(s):  
T. Hennen ◽  
E. Wichmann ◽  
A. Elias ◽  
J. Lille ◽  
O. Mosendz ◽  
...  
Keyword(s):  
Resistive Switching ◽  
High Speed ◽  
Limiting Amplifier ◽  
Speed Measurement ◽  
Current Limiting

scholarly journals Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 717
Author(s):  
Farhad Shamsfakhr ◽  
Andrea Motroni ◽  
Luigi Palopoli ◽  
Alice Buffi ◽  
Paolo Nepa ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Uhf Rfid ◽  
Missing Measurements ◽  
Kalman Smoother ◽  
Vehicle Trajectories ◽  
Angular Velocities ◽  
Reference Tags ◽  
Entire Trajectory ◽  
Method Accuracy ◽  
Mean Square Errors

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.

scholarly journals Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

2021 ◽  
pp. 036119812199550
Author(s):  
Danyang Sun ◽  
Fabien Leurent ◽  
Xiaoyan Xie
Keyword(s):  
Large Scale ◽  
City Planning ◽  
Human Mobility ◽  
Work Place ◽  
Trajectory Data ◽  
Floating Car Data ◽  
Vehicle Trajectories ◽  
Activity Types ◽  
Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

scholarly journals Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning

2021 ◽  
Vol 10 (5) ◽  
pp. 333
Author(s):  
Junli Liu ◽  
Miaomiao Pan ◽  
Xianfeng Song ◽  
Jing Wang ◽  
Kemin Zhu ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spatial Reasoning ◽  
Sampling Interval ◽  
Global Navigation Satellite Systems ◽  
Quality Analysis ◽  
Ancillary Data ◽  
Satellite Systems ◽  
Positioning Errors ◽  
Vehicle Trajectories ◽  
Global Navigation Satellite

Vehicle trajectories derived from Global Navigation Satellite Systems (GNSS) are used in various traffic applications based on trajectory quality analysis for the development of successful traffic models. A trajectory consists of points and links that are connected, where both the points and links are subject to positioning errors in the GNSS. Existing trajectory filters focus on point outliers, but neglect link outliers on tracks caused by a long sampling interval. In this study, four categories of link outliers are defined, i.e., radial, drift, clustered, and shortcut; current available algorithms are applied to filter apparent point outliers for the first three categories, and a novel filtering approach is proposed for link outliers of the fourth category in urban areas using spatial reasoning rules without ancillary data. The proposed approach first measures specific geometric properties of links from trajectory databases and then evaluates the similarities of geometric measures among the links, following a set of spatial reasoning rules to determine link outliers. We tested this approach using taxi trajectory datasets for Beijing with a built-in sampling interval of 50 to 65 s. The results show that clustered links (27.14%) account for the majority of link outliers, followed by shortcut (6.53%), radial (3.91%), and drift (0.62%) outliers.

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