TY  - JOUR
PY  - 2024//
TI  - Bayesian global optimization gated recurrent unit model for human-driven vehicle lane-change trajectory prediction considering hyperparameter optimization
JO  - Transportation research record
A1  - Hu, Xinghua
A1  - Chen, Shanzhi
A1  - Zhao, Jiahao
A1  - Cao, Yanshi
A1  - Wang, Ran
A1  - Zhang, Tingting
A1  - Long, Bing
A1  - Xu, Yimei
A1  - Chen, Xinghui
A1  - Zheng, Mintanyu
A1  - Guo, Jianpu
SP  - 560
EP  - 572
VL  - 2678
IS  - 3
N2  - To address the low accuracy and inefficiency of current lane-change trajectory prediction methods for human-driven vehicles, this study develops a neural network lane-change trajectory prediction model with hyperparametric optimization capability using Bayesian optimization and gated recurrent units to consider the effect of lane-change intention on vehicle lane-change behavior and to predict it. The proposed model was instantiated using trajectory data of 8,721 vehicles. The results show that the overall recognition accuracy of intention recognition under the optimal input is 93.54%, and the recognition accuracy of keeping straight, left lane-change and right lane-change is 95.59%, 91.72%, and 93.30%, respectively. The root mean square errors of the predicted and actual trajectories to the left and to the right under the optimal input are 0.2582 and 0.2957, respectively. This paper demonstrates that, for the intention recognition module, the low-dimensional input enables the model to obtain high prediction accuracy, while for the trajectory prediction module, the high-latitude input enables the model to obtain a low prediction error. The developed trajectory prediction model can be used to assist in driving decision-making, path planning, and so forth.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/03611981231182390
ID  - ref1
ER  -