In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intention with a CNN-GRU hybrid machine learning model. The convolutional neural network (CNN) layer extracts features from the stochastic driver behavior, which is input to the gated-recurrent-unit (GRU) layer. And the driver’s steering intention is forecasted based on the GRU model. Our study was conducted using a driving simulator to observe the lateral control behaviors of 18 participants in four different driving circumstances. Finally, the efficiency of the suggested prediction approach was evaluated employing long-short-term-memory, GRU, CNN, Transformer, and back propagation networks. Experimental results demonstrated that the proposed CNN-GRU model performs significantly better than baseline models. Compared with the GRU network, the CNN-GRU network reduced the RMSE, MAE, and MAPE of the driver’s input torque by 33.22%, 32.33%, and 35.86%, respectively. The proposed prediction method also possesses adaptability to different driver behaviors.

China has established its national standard for Wireless LAN, called GB15629.11-2003, in which, WLAN Authentication and Privacy Infrastructure (WAP1) was proposed. However, WAP1 can be proved insecure with the Canetti-Krawczyk model. Further analysis shows that in the standard there are also other vulnerabilities, such as inability to provide identity protection and resist key consistency attack, lack of private key verification and desirable security attributes like perfect forward secrecy, key control, etc. Therefore, a new protocol is proposed to fix the security problems of WAP1, especially those in the authentication and key agreement procedure. This protocol is designed and analyzed with a modular methodology and proved secure with the Canetti-Krawczyk model, thus it can guarantee the desirable security attributes. In addition, the presented protocol has a better performance than WAP1 in computational overhead and can also be applied to IEEE 802.11i as an authenticated key agreement protocol.