A Novel Lane-Changing Recognition Method Using Frequency Analysis
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 2
Abstract
Lane-changing recognition is an important task for advanced driver assistance systems, but is heavily challenged by poor driving habits, such as turning without using turn signals. To address this problem in this study, a lane-changing recognition method using frequency analysis was proposed. First, highest-frequency–based and frequency-bands–based methods were employed to evaluate the three behaviors of left lane changing, lane keeping, and right lane changing. To improve the recognition accuracy, the two methods were fused according to their classification advantages for different behaviors. The fused method was verified by lateral position data incorporating lane features that were manually extracted and annotated from the Next-Generation Simulation dataset. The frequency analysis framework achieved recognition accuracy of 91.8%, 97.4%, and 99.1% in 2, 1, and 0 s, respectively, before the vehicle crossed the lane line, which were significant improvements over the time-domain analysis methods. The proposed method was also validated by real-world road data with promising results.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the 111 Project (B17034), the Innovative Research Team Development Program of Ministry of Education of China (IRT_17R83), and the Special Fund for Key Program of Science and Technology of Hubei Province, China (2020AAA001). The author contributions are as follows: Xianjun Hou: Conceptualization, Supervision, Project administration; Wenbo Li: Methodology, Software Validation, Writing - Original Draft, Writing - Review & Editing; Bin Zou: Conceptualization, Writing - Review & Editing, Resources, Funding acquisition; Luqi Tang: Data Curation, Investigation, Formal analysis; Kewei Wang: Data Curation, Investigation, Formal analysis; Wenjun Huang: Software.
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Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 2 • February 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 2, 2021
Accepted: Sep 2, 2022
Published online: Nov 30, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 30, 2023
ASCE Technical Topics:
- Analysis (by type)
- Business management
- Data collection
- Driver behavior
- Engineering fundamentals
- Frequency analysis
- Highway transportation
- Human and behavioral factors
- Infrastructure
- Methodology (by type)
- Practice and Profession
- Research methods (by type)
- Traffic engineering
- Traffic management
- Traffic signals
- Transportation engineering
- Validation
- Vehicles
- Verification
Authors
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