Leveraging Human Driving Preferences to Predict Vehicle Speed

Sen Yang, Wenshuo Wang, Junqiang Xi*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

7 引用 (Scopus)

摘要

Accurate speed prediction is practically critical to eco-safe driving for intelligent vehicles. Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We first designed an Oriented Hidden Semi-Markov Model (Oriented-HSMM) to learn and predict the driver's driving preference sequences while considering traffic flow influence. Then, we developed an optimal speed prediction algorithm to retrieve the smooth speed trajectories with maximal likelihood based on the estimated driving preferences. Finally, we evaluated the proposed model using the Next Generation Simulation (NGSIM) data compared to its counterparts that do not consider driving preferences. Experimental results demonstrate that our proposed Oriented-HSMM method reaches the best results and achieves a satisfying performance with a low mean absolute error (4.16 km/h) and root mean square error (5.08 km/h) at a 200 m prediction horizon.

源语言英语
页(从-至)11137-11147
页数11
期刊IEEE Transactions on Intelligent Transportation Systems
23
8
DOI
出版状态已出版 - 1 8月 2022

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