Citation

Huang T, Fu R, Sun Q, Deng Z, Liu Z, Jin L, Khajepour A. Transp. Res. C Emerg. Technol. 2024; 160: e104497.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trc.2024.104497

PMID

unavailable

Abstract

Driver lane change intention (DLCI) predicting has become an essential research for the development of human-machine co-driving system. This work makes an attempt to predict the DLCI, which is the result of complex interaction between human drivers and driving scene. While few works have explored the relationship between driver behavior features and key features of driving scene when predicting the DLCI. To solve this gap, we developed a DLCI prediction method based on topological graph constructed by driver behaviors and traffic context. However, challenges trend on the heels of that because of some unavoidable features that are irrelevant to the DLCI prediction in the topological graph, the difficulty of capturing global dependencies in the driver's head pose sequence, the dynamics of the relationship between different categories, and insufficient of the DLCI dataset. Therefore, we designed a DLCI predicting model based on dynamic graph convolution network with semantic attention module (DGCN-SAM) and self-supervised guided learning based on the understanding of topological graph (SGL-UTG). Specifically, an invert residual module with anthropomorphic attention mechanism (IRM-AAM) was designed to extract important features in topological graphs. The Transformer with multi-head self-attention was used to capture the global dependences of driver's head pose sequence. DGCN-SAM was developed to model the relationship between different categories or nodes in the graph. And SGL-UTG was proposed to improve the generalization performance and prevent overfitting in the absence of sufficient DLCI data. The experimental results demonstrate that the proposed method can predict the DLCI in real-time with high accuracy.

Language: en

Keywords

Anthropomorphic attention mechanism; Driver lane change intention; Dynamic graph convolution network; Self-supervised guided learning; Topological graph; Transformer