## 2 Methodology
### Driving Behavior Models of Vehicles
#### 2.1.1 Longitudinal Driving Behavior Models of Vehicles  
The longitudinal driving behavior of vehicles mainly involves the free-driving behavior and the car following driving behavior. As for the free-driving behavior, just like HVs, ICVs will accelerate to their desired speeds with the maximum desired accelerations. However, with the application of SLADS, there are obvious differences in car following behavior between ICVs and HVs. Consequently, it is of great significance to select a suitable car following model to characterize these differences. It is generally believed that when the time headway of two neighboring vehicles is less than 6 s or the distance headway is less than 125 m, the following vehicle is in a state of car following, which means the speed and acceleration (or deceleration) will be adjusted with the change of the motion state of the preceding vehicle [20]. The car following models are relatively mature including 'Irritation Response Model', 'Safe Distance Model', 'Desired Speed Model', 'Psycho-physiological Model', 'Cellular Automata Model' and 'Artificial Intelligence Model' [20]. Considering the characteristics of various models, the Wiedemann 99 model, a kind of 'Psycho-physiological Model', was selected [21]. The main reasons for this are given as follows. (1) For ICVs with SLADS, it is still the human drivers who complete driving tasks. Under the overlap of the driving assistance functions and the driving experience of human drivers, vehicles probably exhibit extremely complex driving behaviors. Some nonlinear and stochastic driving behavior characteristics are difficult to depict through fixed kinematic equation descriptions. Wiedemann 99 model abstracts driving behaviors into 10 parameters. By adjusting the values of these parameters, driving behaviors of ICVs and HVs can be described from the dimensions of intelligent functions and driving styles. The specific model principle will be introduced later. (2) The Wiedemann 99 model has been used by many scholars in their research [6; 11; 22], and many sorts of famous traffic simulation software such as VISSIM and SUMO also use it as a built-in model. Therefore, reliability and accuracy of the Wiedemann 99 model can be guaranteed. The Wiedemann 99 model constructs the vehicle state space with the speed difference \(\Delta\)v between the leading and following vehicles as the abscissa and the head distance \(\Delta\)x between them as the ordinate. This state space is divided into four main areas by a number of thresholds, namely, free driving zone, car following zone, emergent braking zone and collision zone. These thresholds are calculated from 10 model parameters shown in Table 1[11]. The state of each vehicle at each moment can be represented by point (\(\Delta\)v, \(\Delta\)x) that will obviously belong to one of the four different areas. According to the current state space area of the vehicle, the Wiedemann 99 model calculates and outputs the acceleration at the next moment for each vehicle to achieve real-time control. Since the reaction speed and execution accuracy of sensors, computing platforms and intelligent actuators are higher than those of human drivers, ICVs can accurately capture the movement states of vehicles in front and react to them in time. Therefore, compared with HVs, ICVs with SLADS are expected to adjust their speed more smoothly when following a vehicle, which could reduce the sensitivity to changes in the acceleration and deceleration of the preceding vehicle, and weaken speed fluctuations caused by \"restraint\" and \"hysteresis\" of car following behavior. These differences will affect the parameters in the Wiedemann 99 model, which are CC2, CC4, CC5 and CC6 [22; 23]. As mentioned before, ICVs with SLADS and HVs are all mainly controlled by human drivers. The parameters in SLADS like activation speed and expected time headway can be adjusted by human drivers according to their personal preferences.