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5 July 2024 Personalized lane change decision model based on long short-term memory network
XiaoBin Qi, YanQiang Li, Yong Wang, DaiFeng Zhang, ZhiBang Zhong, Qian Du
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Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318421 (2024) https://doi.org/10.1117/12.3032874
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
A vehicle's lane-changing behavior is affected by the surrounding environment and driver factors, which makes it difficult to identify accurately. To solve this problem, a personalized lane-changing decision model based on a Long Short-term Memory (LSTM) network is proposed. First, an unsupervised clustering method is applied to recognize three distinct driving styles; Second, by considering the interactions among the target vehicle and surrounding vehicles, a benefit function is constructed to measure these interactions and generate the lane-changing benefit values. The lane-changing gain values and feature parameters are used as model inputs to construct a personalized lane-changing decision model using LSTM. Finally, the proposed method is validated with the NGSIM dataset: the overall accuracy of the model reaches 97.8% when considering different driving styles, which proves that the proposed method can achieve personalized lane-changing decisions based on different drivers' lane-changing behaviors.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
XiaoBin Qi, YanQiang Li, Yong Wang, DaiFeng Zhang, ZhiBang Zhong, and Qian Du "Personalized lane change decision model based on long short-term memory network", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318421 (5 July 2024); https://doi.org/10.1117/12.3032874
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KEYWORDS
Data modeling
Autonomous driving
Decision making
Autonomous vehicles
Feature extraction
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