Improved time series models for the prediction of lane-change intention

ABSTRACT

To improve the accuracy of lane-change intention prediction and analyze the influence of driving styles on prediction outcomes, the T-Encoder-Sequence model is proposed in this paper. It integrates the Transformer’s encoder module with various recurrent neural network (RNN) models and introduces a multimodal fusion input structure. Building on this, a risk indicator model, capable of reflecting driver stress, is established to calculate the model’s input parameters. Consequently, the model can simultaneously capture global information and consider the impact of vehicle classes on drivers. Furthermore, the k-means++ algorithm is employed to categorize vehicle trajectories into conservative, conventional, and aggressive types for further analysis. The results demonstrate that training the model with risk indicator parameters markedly enhances prediction performance. Under identical input parameters, the T-Encoder-Sequence model exhibits notably superior prediction efficacy compared to the original model. The T-Encoder-Sequence model, trained with risk indicator parameters, demonstrates substantial advantages compared to other studies.

KEYWORDS:

• Lane-change intention prediction
• driving style
• transformer
• RNN
• trajectory data
• risk indicator

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. The dataset used to support the findings of this study have been deposited at https://www.highd-dataset.com/.

Additional information

Funding

The work was supported by the Fundamental Research Funds for the Central Universities [300102210657,300102343204]; National Natural Science Foundation of China [52102404]; Natural Science Basic Research Program of Shaanxi Province [2023-YBSF-116,2021JQ-239].