機能的脳ネットワークに基づく草原道路における特殊気象条件の特定

The grassland roads in Inner Mongolia have the characteristics of low traffic density, simple road geometry, and few environmental disturbances, making them an ideal scenario for the research of autonomous driving technology. However, the frequent crossing of livestock and the high incidence of sandstorms and rain and snow pose significant challenges to the autonomous driving system. Currently, the system is still in the conditional autonomous driving stage, and it still requires the driver to take over in time. The takeover process involves complex cognitive activities involving multiple brain regions. Analyzing the brain functional network characteristics can help reveal the takeover mechanism. Further, combining brain network features with machine learning algorithms can improve the ability to identify takeover states under different weather conditions, providing more adaptive takeover decision support for the system.

A driving simulation experiment was conducted, with three weather conditions: sunny, sandstorm, and rain and snow. The typical takeover event was livestock crossing the road, and the participants' takeover response time and electroencephalogram (EEG) signals were recorded. The phase lag index was used to construct a phase synchronization network, and two-factor repeated measures variance analysis was performed on the topological characteristics of the brain network in the θ, α, and β frequency bands. Finally, a K-nearest neighbor algorithm was used to establish a weather classification model.

The takeover response time was the longest in the sandstorm condition, indicating that visual interference inhibited cognitive processing and executive control; severe weather weakened the connections between brain regions, especially in the β frequency band, affecting the stability of takeover. In the rain and snow, the integration of the θ and α frequency band networks was enhanced, which was conducive to information processing; in the sunny, the connections between brain regions remained stable, the cognitive load was lower, and thus the takeover performance was better. The accuracy of the KNN model was 96%, with good generalization ability, and it can be used for special weather recognition in autonomous driving.

The analysis based on brain functional networks not only helps to reveal the takeover mechanism in complex weather environments but also provides neurophysiological support for the optimization of autonomous driving strategies under special weather conditions.