research-article

A lane change prediction algorithm based on probabilistic modeling

Authors:

Momiao ZhouAuthors Info & Claims

CIPAE 2020: Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education

Pages 211 - 216

https://doi.org/10.1145/3419635.3419694

Published: 16 October 2020 Publication History

Abstract

According to the previous research, lane-changes are a major cause of serious traffic accidents. Thus, it is essential to build an efficient prediction algorithm for vehicles lane change on Advanced Driving Assistance System (ADAS) to ensure a safe and comfort driving for host vehicle. At present, many methods for lane change prediction have been proposed. However, most of them require a lot of data training to have a good prediction performance. Considering the practical applicability of the prediction algorithm in ADAS, this paper proposes a prediction method by probabilistic modelling. This method combines two aspects lane change evidence. On the one hand, model the lane change probability caused by the target vehicle's driving context. On the other hand, model the lane change probability reflected by vehicle posture in the road. The evaluation of the whole algorithms was done by using simulation data and real lane change data. The results show that the algorithm performs well in predicting accuracy and reducing false alarms.

References

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Digital Library

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Cited By

Zhao ZWei ZTian DReimer BGershon PMoradi-Pari E(2023)End-to-End Spatio-Temporal Attention-Based Lane-Change Intention Prediction from Multi-Perspective Cameras2023 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55152.2023.10186602(1-8)Online publication date: 4-Jun-2023
https://doi.org/10.1109/IV55152.2023.10186602
Xue QXing YLu J(2022)An integrated lane change prediction model incorporating traffic context based on trajectory dataTransportation Research Part C: Emerging Technologies10.1016/j.trc.2022.103738141(103738)Online publication date: Aug-2022
https://doi.org/10.1016/j.trc.2022.103738
B. M. SGupta VKedia AAsawa LSubramanian K(2021)Neural Network Based Intelligent Traffic System2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS53288.2021.9660846(1101-1107)Online publication date: 22-Sep-2021
https://dl.acm.org/doi/10.1109/IDAACS53288.2021.9660846

Index Terms

A lane change prediction algorithm based on probabilistic modeling
1. Information systems
  1. Information systems applications
    1. Process control systems

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CIPAE 2020: Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education

October 2020

527 pages

ISBN:9781450387729

DOI:10.1145/3419635

Copyright © 2020 ACM.

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Publication History

Published: 16 October 2020

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CIPAE 2020

CIPAE 2020: 2020 International Conference on Computers, Information Processing and Advanced Education

October 16 - 18, 2020

ON, Ottawa, Canada

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CIPAE 2020 Paper Acceptance Rate 101 of 216 submissions, 47%;

Overall Acceptance Rate 101 of 216 submissions, 47%

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Cited By

Zhao ZWei ZTian DReimer BGershon PMoradi-Pari E(2023)End-to-End Spatio-Temporal Attention-Based Lane-Change Intention Prediction from Multi-Perspective Cameras2023 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55152.2023.10186602(1-8)Online publication date: 4-Jun-2023
https://doi.org/10.1109/IV55152.2023.10186602
Xue QXing YLu J(2022)An integrated lane change prediction model incorporating traffic context based on trajectory dataTransportation Research Part C: Emerging Technologies10.1016/j.trc.2022.103738141(103738)Online publication date: Aug-2022
https://doi.org/10.1016/j.trc.2022.103738
B. M. SGupta VKedia AAsawa LSubramanian K(2021)Neural Network Based Intelligent Traffic System2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS53288.2021.9660846(1101-1107)Online publication date: 22-Sep-2021
https://dl.acm.org/doi/10.1109/IDAACS53288.2021.9660846

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