Detection of Lane Changing Vehicles with Wavelet Transform and K-Nearest Neighbor Algorithm
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Date
2021-05-21
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Publisher
Yalova University
Abstract
Traffic management is getting more complicated due to the increasing urbanization rate day by day. Therefore, many models have been developed using smart transportation systems to overcome this problem. Lane changing, which is one of the important issues of smart transportation, is one of the basic driving behaviors that has a major impact on traffic efficiency, safety, and flow. Many various approaches have been presented in the literature for lane changing detection. In this study, a novel method for lane changing detection with a wavelet transform approach is presented. In the study, the pNEUMA dataset was used to evaluate the performance of the proposed method. In detecting lane changing, the azimuth angles of the vehicles were calculated using the WGS-84 coordinates in the dataset. Multi-level discrete wavelet transform, and lateral deviation were applied to the azimuth series of vehicles on a sample street in the dataset, and the data obtained were then classified with K-Nearest Neighbor Algorithm to determine whether there was a lane changing. In addition, the direction and time of the lane changing were determined by using the maximum amplitude obtained with wavelet transform methods. The proposed approach in the study achieved an average accuracy rate of 98%. Compared to other approaches, the proposed method has less computation complexity and therefore can find results more quickly.
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Keywords
intelligent transportation systems, classification, lane changing, traffic