Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy

Song, Huansheng; Wang, Xuan; Cui, Hua; Wang, Weixing; Guan, Qi; Zhang, Zhaoyang

doi:10.1007/s00500-017-2831-0

Cited by 19 publications

(5 citation statements)

References 23 publications

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“…ORB is a descriptor that is faster than SURF and SIFT and less affected by image noise. Song et al [129] proposed a trajectory clustering framework for vehicle analysis using the ORB algorithm. A matching method based on Hamming distance is used.…”

Section: Vision-based Methodsmentioning

confidence: 99%

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

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Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques.

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Section: Vision-based Methodsmentioning

confidence: 99%

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Shokravi

Bakhary

et al. 2020

Sensors

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“…Then, Intelligent Transportation System (ITS) came as a solution to this problem by implementing information technology and communications networks. One classical method of ITS was video camera technology [162]. Particularly, it has been applied to collect traffic data including vehicle detection and analysis.…”

Section: Intelligent Transportation System (Its)mentioning

confidence: 99%

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

et al. 2023

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Clustering is an unsupervised learning technology, and it groups information (observations or datasets) according to similarity measures. Developing clustering algorithms is a hot topic in recent years, and this area develops rapidly with the increasing complexity of data and the volume of datasets. In this paper, the concept of clustering is introduced, and the clustering technologies are analyzed from traditional and modern perspectives. First, this paper summarizes the principles, advantages, and disadvantages of 20 traditional clustering algorithms and 4 modern algorithms. Then, the core elements of clustering are presented, such as similarity measures and evaluation index. Considering that data processing is often applied in vehicle engineering, finally, some specific applications of clustering algorithms in vehicles are listed and the future development of clustering in the era of big data is highlighted. The purpose of this review is to make a comprehensive survey that helps readers learn various clustering algorithms and choose the appropriate methods to use, especially in vehicles.

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“…Furthermore, the problem of trajectory maps was also approached, almost simultaneously, by other communities, that is, data mining (Chen et al, 2011(Chen et al, , 2012Gaffney and Smyth, 1999;Li et al, 2010;Nanni and Pedreschi, 2006;Pelekis et al, 2009Pelekis et al, , 2017Tang et al, 2019Tang et al, , 2020, intelligent transportation systems (Atev et al 2010;Choong et al 2016Choong et al , 2017Li et al, 2006;Portugal et al, 2020;Song et al, 2017;Wang et al, 2019Wang et al, , 2020Won et al, 2009), air traffic (Enriquez and Kurcz, 2012), and maritime traffic (Dutt et al, 2018;Kontopoulos et al, 2021;Liu et al, 2014a;Li et al, 2017). The detailed discussion regarding relevant publications coming from these fields is presented in Section 7.…”

Section: Trajectorymentioning

confidence: 99%

Survey of maps of dynamics for mobile robots

Kucner

Magnusson

Mghames

et al. 2023

The International Journal of Robotics Research

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Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area.

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Vehicle trajectory clustering based on 3D information via a coarse-to-fine strategy

Cited by 19 publications

References 23 publications

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Review of Clustering Technology and Its Application in Coordinating Vehicle Subsystems

Survey of maps of dynamics for mobile robots

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