Modeling of vehicle trajectory clustering based on LCSS for traffic pattern extraction

Choong, Mei Yeen; Angeline, Lorita; Chin, Renee Ka Yin; Yeo, Kiam Beng; Teo, Kenneth Tze Kin

doi:10.1109/i2cacis.2017.8239036

Cited by 30 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, in this paper, we improved the Itakura parallelogram to further improve the efficiency and accuracy of the DTW algorithm. As mentioned in Bergroth and colleagues, [19][20][21] the traditional LCSS algorithm is not applicable to most situations due to the condition that the elements must be consistent, so we used a threshold range for the constraint. From Su and others, 10,11,[22][23][24][25][26][27][28] it can be seen that most researchers, when studying the similarity of trajectories, only consider the distance between trajectories as a feature alone, which may reduce the accuracy of calculating the similarity and is also easily affected by the different degrees of dispersion of trajectory points.…”

Section: Discussionmentioning

confidence: 99%

Matching method based on similarity of working trajectories

Zhong

Chen

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

To identify whether the actual work trajectory of workers in the factory meets the predetermined work trajectory requirements, we proposed an efficient and accurate work trajectory similarity matching method. We comprehensively considered the similarity between the actual work track and the predetermined track from the two characteristics of track angle and track distance. Among them, the similarity of track rotation angle is calculated using the improved longest common subsequence algorithm, and the similarity of track distance is calculated using the improved dynamic time warping (DTW) algorithm. Then the results of the similarity calculation of these two features are weighted. Finally, the weighted results are used to evaluate the similarity between the actual work track and the predetermined track, so as to judge whether the actual work track meets the requirements of the predetermined track. Experimental data show that the trajectory similarity matching algorithm in this paper has higher accuracy and efficiency than traditional DTW and other algorithms, and has higher ability to resist the interference of trajectory point evacuation than traditional DTW and other algorithms.

show abstract

Section: Discussionmentioning

confidence: 99%

Matching method based on similarity of working trajectories

Zhong

Chen

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…However, these parameters are difficult to generalize in different scenarios and can only describe the similarity at a specific state rather than over the whole driving process. There are also methods to evaluate the consistency of the behavior of two segments by evaluating the similarity of their trajectories, such as longest common subsequence [29] and dynamic time warping [30], but these only consider the similarity of trajectories and not the velocity state at the corresponding position, with insufficient consideration of driving information. For quantitative comparison of consistency during the whole driving process, containing both track and vehicle information, the trajectory field is used to describe the vehicle motivation state and evaluate the similarity of the human and ADS driving trajectory fields by probability analysis.…”

Section: Anthropomorphic Indexmentioning

confidence: 99%

Performance Evaluation Method for Automated Driving System in Logical Scenario

Zhang

Zhu²,

Zhao³

et al. 2022

Automot. Innov.

View full text Add to dashboard Cite

With the continuous improvement of automated driving technology, how to evaluate the performance of an automated driving system is attracting more and more attention. Meanwhile, with the creation of scenario-based test methods, the traditional evaluation index based on a single test can no longer meet the requirements of high-level safety verification for automated driving system, and the performance evaluation of such a system in logical scenarios will be the mainstream. Based on the scenario-based test method and Turing test theory, a performance evaluation method for an automated driving system in the whole parameter space of a logical scenario is proposed. The logical scenario parameter space is partitioned according to the risk degree of concrete scenario, and the evaluation process in different zones are determined. Subsequently, the anthropomorphic index in the safe zone and the collision-avoidance index in the danger zone are defined by comparing test results of human driving and ideal vehicle motion. Taking front vehicle low-speed and cut-out scenarios as examples, two automated driving algorithms are tested in the virtual environment, and the test results are evaluated both by the proposed method and by human observation. The results show that the results of the proposed method are consistent with the subjective feelings of humans; additionally, it can be applied to scenario-based tests and the verification process of an automated driving system.

show abstract

“…The representative of density analysis is Kernel Density Estimation (KDE), which is a nonparametric method that generates a smooth density surface to analyze the spatial distribution characteristics of the data [12]. Many types of spatial clustering algorithms are widely used in vehicle trajectory [13,14]. In the long-term development process, many classification systems have been formed based on different ideas, such as hierarchical and partitioning methods [15].…”

Section: Introductionmentioning

confidence: 99%

Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

Chen

Tao

et al. 2021

IJGI

View full text Add to dashboard Cite

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

show abstract

Modeling of vehicle trajectory clustering based on LCSS for traffic pattern extraction

Cited by 30 publications

References 11 publications

Matching method based on similarity of working trajectories

Matching method based on similarity of working trajectories

Performance Evaluation Method for Automated Driving System in Logical Scenario

Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

Contact Info

Product

Resources

About