## 1 Introduction  
Significant progress has been conducted in the field of perception, decision making, path planning and control authority for autonomous vehicles (AVs) in the past few decades. However, it is still far away from widespread market penetration because of its unmatured technology. As a promising technique aimed at improving the safety of AV, the concept of \"human-vehicle shared control\" (HVSC) has achieved a great deal of research effort in recent years [1]. For HVSC, the automation and driver take control authority of the vehicle sequentially after each other, or co-pilot the vehicle simultaneously [2]. The dynamic model is of great significance for the understanding of the intrinsic and developing control authority of HVSC. In this way, many existing scholarly works have demonstrated the contributions associated with the dynamic model in the HVSC domain. For example, the biomechanical properties of the driver for a vehicle steering task is considered a novel technique of estimating the admittance and total response [3]. By investigating the influence of haptic aids on the pilot's NMS response, an online estimator of the time-varying NMS dynamic method based on Recursive Least Squares is proposed [4, 5]. On the basis of the electric power-assisted steering system, [PERSON] et al. [6] proposed a time-varying method based on a sliding mode observer and the exponentially weighted recursive least squares (EWRLS) algorithm for the estimation of the viscoelastic properties in the driver's arm. The characterization of driver NMS dynamics, implemented to analyze the influence of active and passive steering tasks and hand positions, is also highly sought after [7]. Subsequently, in order to further reflect the relationship between driver and vehicle, the novel Queuing Network is integrated into the driver's NMS dynamic [8]. Similarly, by reducing the internal cost function of the driver, a human-vehicle steering control strategy with the driver's NMS dynamic is proposed [9]. In this way, an intra-driver variability human-like steering model inspired the research boom [10]. Soon after, [PERSON] et al. [11] proposed a shared steering controller by imitating the driver's characteristics of the NMS system. Furthermore, other physiological characteristics including the driver's vision and blood volume pulse, which are regarded as vital indexes, are utilized to demonstrate the mechanism of driving behavior and construct the human-vehicle shared control model [12; 13]. When the driver executes the operation of following the leading car, the driver's leg model plays a vital role in HVSC. In regard to this field, the pedal drive unit is applied to the human-machine interface analysis experiment by investigating the physiological characteristics of the driver's leg [14]. It is noteworthy that the mechanism of the individual muscle-tendon biomechanics metabolism also is parsed, and then a Hill-based human leg model with a muscle and joint is proposed [15]. In further research, a put-forward two-dimensional leg model with a knee, along with ankle passive and quasi-passive elements is optimized [16]. By investigating the influence of muscle force during flexion-extension movement, a Hill-based electromyography (EMG)-driven model of the elbow is proposed [17]. In light of our literature review, there are only a few studies involved in the following aspects. The current driver dynamic models with NMS characteristic are only represented by a single steering arm. The leg NMS dynamic of the driver was not taken into account thoroughly. Particularly in the condition of a roundabout or overtaking, the driver executes the sequential steering-following operation. Therefore, the relative influence between arm and leg NMS characteristic should not be neglected for practical application of the dynamic model. For L3 autonomous vehicle (AV), the driver is required to take over the control authority of the vehicle when the automation system fails to deal with the emergency situation.