Driver steering intention prediction method based on hybrid learning

Abstract

The invention relates to a driver steering intention prediction method based on hybrid learning, and the method is characterized in that the method comprises the following steps: 1) carrying out the multi-mode data collection on a driving simulation platform, and carrying out the preprocessing of the collected multi-mode data; 2) establishing a hybrid learning time sequence model based on the preprocessed multi-mode data; and 3) loading a hybrid learning time sequence model on the intelligent vehicle, and inputting the driver electromyographic signal sequence acquired on line into the hybrid learning time sequence model for prediction to obtain a driver continuous steering intention prediction result and a discrete steering intention prediction result. By establishing the hybrid learning time sequence model, continuous steering torque prediction and discrete intention classification prediction are achieved, and accurate prediction of the steering intention within a certain prediction time range can be realized by setting historical observation parameters. The method can be widely applied to the field of driver steering intention prediction.

Description

technical field [0001] The invention belongs to the technical field of intelligent driving, in particular to a method for predicting a driver's steering intention based on hybrid learning. Background technique [0002] The rapid development of autonomous driving technology has raised a series of challenging issues for the automotive industry and academia. Among them, exploring the role that human drivers can play in future autonomous vehicles and how humans and intelligent vehicles can effectively cooperate is one of the essential tasks. Mutual understanding is a key aspect of multi-agent teaming and collaboration, enabling human drivers and intelligent vehicles to collaborate effectively by understanding the intentions of both parties. Until fully self-driving cars are achieved, drivers will still need to share some control of the vehicle with automated devices. In this case, predicting the driver's steering intention enables the smart vehicle to optimize the assistance a...

AI Technical Summary

Problems solved by technology

Most current research focuses on discrete intent classification and prediction based on the fusion of video sequences and internal and external environments, digital maps, GPS, and lidar information. These methods usually require complex sensor fusion and data coordination.

The results show that the LDW system is very effective in predicting the intention of 0-1.5s before changing lanes, although the driving intention can be predicted in a large prediction range (usually 0s-3.5s), but due to the driver's physiological state and driving behavior Lack of connection between them, only discrete intent states can be estimated

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