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arXiv · 2212.10737

Driving Style Recognition at First Impression for Online Trajectory Prediction

Tu Xu·Kan Wu·Yongdong Zhu·Wei Ji
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Abstract

This paper proposes a new driving style recognition approach that allows autonomous vehicles (AVs) to perform trajectory predictions for surrounding vehicles with minimal data. Toward that end, we use a hybrid of offline and online methods in the proposed approach. We first learn typical driving styles with PCA and K-means algorithms in the offline part. After that, local Maximum-Likelihood techniques are used to perform online driving style recognition. We benchmarked our method on a real driving dataset against other methods in terms of the RMSE value of the predicted trajectory and the observed trajectory over a 5s duration. The proposed approach can reduce trajectory prediction error by up to 37.7\% compared to using the parameters from other literature and up to 24.4\% compared to not performing driving style recognition.

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BibTeXRIS

Tu Xu, Kan Wu, Yongdong Zhu, Wei Ji. 2022-12-21. Driving Style Recognition at First Impression for Online Trajectory Prediction. https://doi.org/10.1016/j.ifacol.2023.10.323

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