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ovm.py
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import numpy as np
class OptimalVelocityModel:
def __init__(self, num_vehicles=5, params=None, cav_index = None):
# Set the default parameters
if params is None:
params = {
'alpha': 0.6,
'beta': 0.9,
'tau': 1.5,
'v0': 15, # Equilibrium velocity
's0': 20, # Equilibrium spacing
's_st': 5,
's_go': 35,
'v_max': 30,
'velocity_noise': 2, # Velocity fluctuation amplitude for the leading vehicle
'a_max': 5, # 5
'a_min': -5 # 5
}
self.num_vehicles = num_vehicles
self.alpha = params['alpha']
self.beta = params['beta']
self.tau = params['tau']
self.v0 = params['v0']
self.s0 = params['s0']
self.s_st = params['s_st']
self.s_go = params['s_go']
self.v_max = params['v_max']
self.velocity_noise = params['velocity_noise']
self.a_max = params['a_max']
self.a_min = params['a_min']
self.num_vehicles = num_vehicles
self.spacing = np.zeros(num_vehicles)
self.velocity = np.zeros(num_vehicles)
self.position = np.zeros(num_vehicles)
self.control_input = np.zeros(num_vehicles)
self.cav_index = cav_index
self.acceleration = np.zeros(num_vehicles)
self.t = 0
self.disturbance = None
def reset(self, disturbance = None):
# The spacing between the vehicles
self.spacing.fill(self.s0)
# The velocity of the vehicles
self.velocity.fill(self.v0)
self.t = 0
# The position of the vehicles
for i in range(self.num_vehicles):
self.position[i] = (self.num_vehicles - i - 1) * self.s0
# The control input of the vehicles
self.control_input.fill(0)
# The acceleration of the vehicles
self.acceleration.fill(0)
self.disturbance = disturbance
def set_control_input(self, vehicle_idx, control_input):
# Set the control input of the vehicle
self.control_input[vehicle_idx] = control_input
def update(self, dt, select_scenario, pure_car_following):
# Update the time
self.t += dt
if select_scenario == 0 or select_scenario == 1:
scenario_id = [0]
duration = [0,5]
elif select_scenario == 2:
scenario_id = [3]
duration = [0,8]
elif select_scenario == 3:
scenario_id = [4]
duration = [0,8]
elif select_scenario == 4:
scenario_id = [1]
duration = [0,5]
if self.disturbance is not None:
duration = [0, self.disturbance[0]]
# Update all the vehicles
for i in range(self.num_vehicles - 1, 0, -1):
# Update the velocity
if i in self.cav_index and not pure_car_following:
# The autonomous vehicle uses the provided control input
dv = self.velocity[i-1] - self.velocity[i]
if self.control_input[i] > self.a_max:
self.control_input[i] = self.a_max
elif self.control_input[i] < self.a_min:
self.control_input[i] = self.a_min
self.velocity[i] += self.control_input[i] * dt
# Update the spacing
self.spacing[i] += dv * dt
self.position[i] += self.velocity[i] * dt
self.acceleration[i] = self.control_input[i]
elif i in scenario_id and self.t >= duration[0] and self.t < duration[1]:# and not pure_car_following
# Update the lead vehicle (the first vehicle) in different scenarios
dv = self.velocity[i-1] - self.velocity[i]
if select_scenario == 2:
if self.disturbance is not None:
emergent_acc = self.disturbance[1]
else:
emergent_acc = 1
if self.t >= duration[0] and self.t < duration[1]/2:
self.velocity[i] += emergent_acc * dt
self.acceleration[i] = emergent_acc
if self.t >= duration[1]/2 and self.t < duration[1]:
self.velocity[i] += 0
self.acceleration[i] = 0
self.position[i] += self.velocity[i] * dt
self.spacing[i] += dv * dt
elif select_scenario == 3:
if self.disturbance is not None:
emergent_acc = self.disturbance[1]
else:
emergent_acc = 1
if self.t >= duration[0] and self.t < duration[1]/2:
self.velocity[i] += emergent_acc * dt
self.acceleration[i] = emergent_acc
if self.t >= duration[1]/2 and self.t < duration[1]:
self.velocity[i] += 0
self.acceleration[i] = 0
self.position[i] += self.velocity[i] * dt
self.spacing[i] += dv * dt
elif select_scenario == 4:
if self.disturbance is not None:
braking_acc = self.disturbance[1]
else:
braking_acc = -4
if self.t >= duration[0] and self.t < duration[1]/2:
self.velocity[i] += braking_acc * dt
self.acceleration[i] = braking_acc
if self.t >= duration[1]/2 and self.t < duration[1]:
self.velocity[i] += 0
self.acceleration[i] = 0
self.position[i] += self.velocity[i] * dt
self.spacing[i] += dv * dt
else:
# The other vehicles use the OVM model
dv = self.velocity[i-1] - self.velocity[i]
ds = dv
# calculate the desired velocity and spacing
s_star = self.s0
v_star = self.alpha*self.v_max/2*np.pi/(self.s_go-self.s_st)*np.sin(np.pi*(s_star-self.s_st)/(self.s_go-self.s_st))
# calculate the desired acceleration
cal_D = self.spacing[i]
if cal_D > self.s_go:
cal_D = self.s_go
elif cal_D < self.s_st:
cal_D = self.s_st
acceleration = self.alpha * (self.v_max/2*(1-np.cos(np.pi*(cal_D-self.s_st)/(self.s_go-self.s_st))) - self.velocity[i]) + self.beta * dv
if acceleration > self.a_max:
acceleration = self.a_max
elif acceleration < self.a_min:
acceleration = self.a_min
self.acceleration[i] = acceleration
self.velocity[i] += acceleration * dt
# Update the spacing
self.spacing[i] += dv * dt
self.position[i] += self.velocity[i] * dt
if select_scenario == 0:
# Random noise scenario
self.velocity[0] += (self.control_input[0] + np.random.normal(0, self.velocity_noise)) * dt
self.spacing[0] += 0
self.position[0] += self.velocity[0] * dt
self.acceleration[0] = (self.control_input[0] + np.random.normal(0, self.velocity_noise))
elif select_scenario == 1:
# Emergency braking scenario
braking_acc = -5
self.acceleration[0] = 0
if self.t >= duration[0] and self.t < duration[1]/2:
self.velocity[0] += braking_acc * dt
self.acceleration[0] = braking_acc
elif self.t >= duration[1]/2 and self.t < duration[1]:
self.velocity[0] += -braking_acc * dt
self.acceleration[0] = -braking_acc
self.position[0] += self.velocity[0] * dt