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The traditional OV model and Gipps model have problems such as limited applicability frequent fluctuations of acceleration and so on when applied to autonomous driving vehicles. The driving experiment was carried out by using the driving simulator, and the driving state of vehicle was divided according to the experimental analysis results. Based on the driving targets in different states of the vehicle, a multi-mode autonomous car-following model based on driving simulator was proposed to realize the safe and stable operation of autonomous driving vehicle. The results show that the model is more stable in the driving simulator simulation environment, and it provides a basis for the research of autonomous driving behavior.

Rehabilitation robots have been widely used in clinical rehabilitation in stroke subjects. The safety and comfort in rehabilitation training still are affected by many problems, however, such as the single rehabilitation training mode, the poor human-robot interaction and adaptability. In this paper, an adaptive trajectory planning method of lower limb rehabilitation robot based on surface Electromyography (sEMG) signal and interactive force (IF) is proposed to improve the human-robot interaction and adaptability. Firstly, the envelope difference method is used to identify flexion or extension of the lower limb based on the surface EMG signals from biceps femoris and vastus lateralis muscles; Secondly, the sEMG-IF fusion model is established on the plantar pressure and recognition result of flexion or extension with logical reasoning, and then the velocity trajectory of the robot can be planned with the model; Meanwhile, the evaluation of muscle activity level is introduced to adjust the robot's auxiliary coefficients and modify the velocity trajectory adaptively. In this paper, the corresponding three velocity trajectories of the robot were planned based on EMG signals during the three periodic movements. The result showed that the proposed method is feasibility and effectiveness.