A Novel Lane-Changing Recognition Method Using Frequency Analysis

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A Novel Lane-Changing Recognition Method Using Frequency Analysis

Hou, Xianjun / Li, Wenbo / Zou, Bin / Tang, Luqi / Wang, Kewei / Huang, Wenjun

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.