simulate_wiedemann74_driver: Simulate Wiedemann74 Model with single driver data
In durraniu/CarFollowingModels: What the Package Does (One Line, Title Case)

Description Usage Arguments Value Examples

View source: R/simulate_wiedemann74_driver.R

This function takes in the lead vehicle trajectory and calculates speed, spacing and acceleration of the following vehicle using the Wiedemann74 Model.It also estimates the car-following state in each time frame.

simulate_wiedemann74_driver(
  resolution,
  N,
  dfn1,
  xn1,
  vn1,
  bn1,
  xn_first,
  vn_first,
  ln,
  D_MAX,
  V_MAX,
  V_DESIRED,
  FAKTORVmult,
  BMAXmult,
  BNULLmult,
  BMIN,
  CX,
  AXadd,
  BXadd,
  EXadd,
  OPDVadd
)

`resolution`	Duration of a time frame. Typical values are 0.1, 0.5, 1.0 s. Double. Must match with the resolution of the observed lead vehicle data dfn1 defined below
`N`	Number of Following Vehicles in the same lane. Integer.
`dfn1`	Unquoted name of the dataframe that contains lead vehicle data.
`xn1`	Name of the column in dfn1 that contains lead vehicle position. Character.
`vn1`	Name of the column in dfn1 that contains lead vehicle speed. Character.
`bn1`	Name of the column in dfn1 that contains lead vehicle acceleration. Character.
`xn_first`	First value of vehicle position of each of the following vehicles. A list of doubles with size equal to N.
`vn_first`	First value of vehicle speed of each of the following vehicles. A list of doubles with size equal to N.
`ln`	Length of each of the lead vehicles. A list of doubles with size equal to N.
`D_MAX`	Upper limit of reaction spacing. Double. Typical value is 150 m.
`V_MAX`	Maximum speed of the following vehicle model. Double. Typical values are 40 m/s, 60 m/s.
`V_DESIRED`	Desired speed of the following driver. Double.
`FAKTORVmult`	Controls acceleration in Free-driving state. Double. Higher values will result in large acceleration.
`BMAXmult`	Controls acceleration in Free-driving state. Double. It can be calculated as the maximum acceleration of the following vehicle model divided by V_MAX.
`BNULLmult`	Controls oscillation in speed in Following state. Double. Typical value is 0.25 m/s2.
`BMIN`	Controls maximum deceleration in Approaching and Emergency-braking states. Double.
`CX`	Calibration parameter. Double. It is a function of width of the lead vehicle and the angular velocity threshold at which driver slows down during Approaching state. Typical values are 20 - 75.
`AXadd`	Calibration parameter. Double. It is the bumper-to-bumper distance when both vehicles are stopped. Typical values are 2 - 4 m.
`BXadd`	Calibration parameter. Double. It controls the speed dependent spacing to the lead vehicle. Typical value is 2.
`EXadd`	Calibration parameter. Double. It controls the maximum spacing to the lead vehicle, as well as the speed difference in perception of closing in. Typical value is 2.
`OPDVadd`	Calibration parameter. Double. It controls the speed difference in perception of the opening process. Typical value is 1.5.

A dataframe with lead and following vehicle(s) trajectories. It also returns all the pereception thresholds.

# Time
last_time <- 3000 ## s
time_frame <- 0.1 ## s
Time <- seq(from = 0, to = last_time, by = time_frame)
time_length <- length(Time)



## Lead vehicle
vn1_first <- 13.9 ## first speed m/s
xn1_first <- 100 ## position of lead vehicle front center m
bn1_complete <- c(rep(0, 29500),
                 rep(-5, time_length - 29500))



#############################################
### Complete speed trajectory of Lead vehicle
#############################################

vn1_complete <- rep(NA_real_, time_length) ### an empty vector
xn1_complete <- rep(NA_real_, time_length) ### an empty vector

vn1_complete[1] <- vn1_first
xn1_complete[1] <- xn1_first

for (t in 2:time_length) {

 ### Lead vehicle calculations
 vn1_complete[t] <- vn1_complete[t-1] + (bn1_complete[t-1] * time_frame)

 vn1_complete[t] <- ifelse(vn1_complete[t] < 0, 0, vn1_complete[t])


xn1_complete[t] <- ifelse(
 vn1_complete[t] > 0,
 xn1_complete[t-1] + (vn1_complete[t-1] * time_frame) +
   (0.5 * bn1_complete[t-1] * (time_frame)^2),
 xn1_complete[t-1]
)

}



ldf <- data.frame(Time, bn1_complete, xn1_complete, vn1_complete)

# Run the Wiedemann function:
simulate_wiedemann74_driver(
 resolution=0.1,
 N=5,
 dfn1=ldf,
 xn1="xn1_complete",
 vn1="vn1_complete",
 bn1="bn1_complete",
 xn_first=list(85, 70, 55, 40, 25),
 vn_first=list(12, 12, 12, 12, 12),
 ln=list(5, 5, 5, 5, 5),
 D_MAX=150,
 V_MAX=44,
 V_DESIRED=14.4,
 FAKTORVmult=0.001,
 BMAXmult=0.08,
 BNULLmult=0.25,
 BMIN=-5,
 CX=50,
 AXadd=2,
 BXadd=2,
 EXadd=2,
 OPDVadd=1.5
)