A Novel Method for the Driver Lane-Changing Intention Recognition

With the development of communication sensing technology and onboard technology, the future will enter a connected environment. Traffic information is transformed from static isolation to dynamic interconnection, and drivers’ attention distribution and environmental perception abilities are changed. At present, the study on driver lane-changing intention (LCI) has received little attention. Therefore, this article presents a novel method for recognizing the driver LCI in the connected environment. First, an innovative driving simulator experiment was designed to mimic the connected environment. Second, the statistical results demonstrate that the LCI time window is 6.6 s in the connected environment, and the feature parameters of LCI are determined. These parameters include yaw rate, vehicle lateral speed, vehicle lateral acceleration, steering wheel angle, steering wheel torque, gaze direction (
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). Finally, a novel driver LCI recognition model is established. The phase-space reconstruction and recurrence plot techniques are used to convert the time-series feature parameters into images. The Swin transformer algorithm with state-of-the-art performance is introduced to classify the images into three categories: lane-changing left (LCL), lane keeping, and lane-changing right (LCR). This novel method overcomes the problems of vanishing gradient and low recognition accuracy caused by the long time-series input. The accuracy of the proposed LCI model is 89.90% at 3 s prior to the lane-changing maneuver, which is higher than that of the classical machine learning algorithms. This study contributes to the accurate response of lane-changing assistance systems and the smooth transfer of control rights in human–machine co-driving systems.