research-article

Turn Prediction for Special Intersections and Its Case Study

Authors:

Wei-Ting Tseng,

Wenlu WangAuthors Info & Claims

ICPP Workshops '19: Workshop Proceedings of the 48th International Conference on Parallel Processing

Article No.: 3, Pages 1 - 9

https://doi.org/10.1145/3339186.3339190

Published: 05 August 2019 Publication History

Abstract

The effect of growing population brings heavy traffic which in turn leads to increased number of traffic accidents. In particular, the majority of traffic accidents happen at special intersections in situations such as heavy traffic, poor intersection design, etc. In this paper, we propose a turn prediction system to predict which road a vehicle will take at special intersection, e.g., T-junction, Y-junction, or junction where more than 4 roads meet. The proposed system uses the radar installed at the intersection to collect vehicle dynamics. The collected data is processed to calculate deflection angles of vehicles corresponding to the road. The smoothing technique is adopted to filter the noise of calculated deflection angles. The ensemble methods are utilized to construct the model to predict future deflection angles of vehicles corresponding to the road. According to the predicted deflection angle, we can predict which road a vehicle will take at a special intersection and alert other vehicles when necessary. To assess the performance of the model prediction, a real-world experiment is carried out, which utilizes radar to collect the dataset at Kaixuan 4th Rd. and Zhenxing Rd., Qianzhen Dist., Kaohsiung City, Taiwan. The experiment results show that the accuracy of the Random Forest algorithm is the highest among all datasets.

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Cited By

Mukherjee SWallace AWang S(2021)Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural NetworksIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2021.31059202(254-268)Online publication date: 2021
https://doi.org/10.1109/OJITS.2021.3105920

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Turn Prediction for Special Intersections and Its Case Study

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ICPP Workshops '19: Workshop Proceedings of the 48th International Conference on Parallel Processing

August 2019

241 pages

ISBN:9781450371964

DOI:10.1145/3339186

Copyright © 2019 ACM.

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University of Tsukuba: University of Tsukuba

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Published: 05 August 2019

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ICPP 2019

ICPP 2019: Workshops

August 5 - 8, 2019

Kyoto, Japan

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Overall Acceptance Rate 91 of 313 submissions, 29%

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Cited By

Mukherjee SWallace AWang S(2021)Predicting Vehicle Behavior Using Automotive Radar and Recurrent Neural NetworksIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2021.31059202(254-268)Online publication date: 2021
https://doi.org/10.1109/OJITS.2021.3105920

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