To investigate motion sickness caused by mismatched visual and operational information in the integration of autonomous driving and virtual reality technologies, this paper simultaneously collects electroencephalogram (EEG) signals from participants using a dual-task paradigm that combines active driving and autonomous driving on a simulated driving platform. This approach is complemented by the Go/No-go behavioral paradigm and standardized motion sickness questionnaires to explore the impact of different driving modes on the allocation of brain cognitive resources. Results indicate that autonomous driving scenarios significantly exacerbate motion sickness symptoms due to visual-vestibular conflict. Autonomous driving based on virtual reality is particularly prone to inducing motion sickness. The underlying neural mechanisms are characterized by increased power spectral density in the Pz, Cz, and Fz EEG channels (p<0.05), as well as decreased amplitude and shortened latency of the N200 and P300 components (p<0.05). Furthermore, a convolutional neural network classification model is constructed that integrates time-domain ERP, frequency-domain PSD, and nonlinear complexity features. The model achieves an accuracy of 92.7%, which provides a scientific basis for real-time monitoring and the optimization of human-computer interaction design.