def _make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
road = self.road
main_init_xpos = 215
auxi_init_xpos = 215
init_interval = 20
road.vehicles = []
for i in range(self.leader_num):
velocity = np.random.uniform(38, 40)
road.vehicles.append(
MDPVehicle(road,
road.network.get_lane(
("a", "b", 0)).position(main_init_xpos, 0),
index=i,
velocity=velocity))
main_init_xpos += init_interval
for i in range(self.follower_num):
velocity = np.random.uniform(38, 40)
road.vehicles.append(
MDPVehicle(road,
road.network.get_lane(
("j", "k", 0)).position(auxi_init_xpos, 0),
index=i + self.leader_num,
velocity=velocity))
auxi_init_xpos += init_interval
self.vehicle = road.vehicles[0]
road.leader_num = self.leader_num
road.follower_num = self.follower_num
def _make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
road = self.road
leader_v_one = MDPVehicle(road,
road.network.get_lane(
("a", "b", 0)).position(100, 0),
velocity=30)
road.vehicles.append(leader_v_one)
self.all_vehicles.append(leader_v_one)
leader_v_two = MDPVehicle(road,
road.network.get_lane(
("a", "b", 0)).position(80, 0),
velocity=30)
road.vehicles.append(leader_v_two)
self.all_vehicles.append(leader_v_two)
#test_vehicle = MDPVehicle(road, road.network.get_lane(("a", "b", 1)).position(50, 0), velocity=30)
#road.vehicles.append(test_vehicle)
#self.all_vehicles.append(test_vehicle)
#other_vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
#road.vehicles.append(other_vehicles_type(road, road.network.get_lane(("a", "b", 0)).position(90, 0), velocity=29))
#road.vehicles.append(other_vehicles_type(road, road.network.get_lane(("a", "b", 1)).position(70, 0), velocity=31))
#road.vehicles.append(other_vehicles_type(road, road.network.get_lane(("a", "b", 0)).position(5, 0), velocity=31.5))
#merging_v = other_vehicles_type(road, road.network.get_lane(("j", "k", 0)).position(110, 0), velocity=20)
merging_v_one = MDPVehicle(road,
road.network.get_lane(
("j", "k", 0)).position(110, 0),
velocity=30)
#merging_v_one.target_velocity = 30
road.vehicles.append(merging_v_one)
self.vehicle = leader_v_one
merging_v_two = MDPVehicle(road,
road.network.get_lane(
("j", "k", 0)).position(90, 0),
velocity=30)
#merging_v_two.target_velocity = 30
road.vehicles.append(merging_v_two)
self.all_vehicles.append(merging_v_one)
self.all_vehicles.append(merging_v_two)
def _make_vehicles(self):
"""
Populate a road with several vehicles on the road
:return: the ego-vehicle
"""
road = self.road
ego_vehicle = MDPVehicle(road, road.network.get_lane(("a", "b", 1)).position(30, 0), velocity=30)
road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
for i in range(3):
self.road.vehicles.append(
vehicles_type(road,
position=road.network.get_lane(("a", "b", 1))
.position(70+40*i + 10*self.np_random.randn(), 0),
heading=road.network.get_lane(("a", "b", 1)).heading_at(70+40*i),
velocity=24 + 2*self.np_random.randn(),
enable_lane_change=False)
)
for i in range(2):
v = vehicles_type(road,
position=road.network.get_lane(("b", "a", 0))
.position(200+100*i + 10*self.np_random.randn(), 0),
heading=road.network.get_lane(("b", "a", 0)).heading_at(200+100*i),
velocity=20 + 5*self.np_random.randn(),
enable_lane_change=False)
v.target_lane_index = ("b", "a", 0)
self.road.vehicles.append(v)
def make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
# Ego-vehicle
ego_lane = self.road.network.get_lane(("ser", "ses", 0))
ego_vehicle = MDPVehicle(self.road,
ego_lane.position(130, 0),
velocity=15,
heading=ego_lane.heading_at(130)).plan_route_to("nxs")
MDPVehicle.SPEED_MIN = 5
MDPVehicle.SPEED_MAX = 15
MDPVehicle.SPEED_COUNT = 3
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
# Other vehicles
other_vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
self.road.vehicles.append(
other_vehicles_type(
self.road,
self.road.network.get_lane(("we", "sx", 0)).position(0, 0),
velocity=16,
heading=self.road.network.get_lane(("we", "sx", 0)).heading_at(0)).plan_route_to("exs"))
for i in list(range(2, 3)) + list(range(-1, 0)):
self.road.vehicles.append(
other_vehicles_type(
self.road,
self.road.network.get_lane(("we", "sx", 0)).position(20*i, 0),
velocity=16,
heading=self.road.network.get_lane(("we", "sx", 0)).heading_at(20*i)))
def _create_vehicles(self):
"""
Create some new random vehicles of a given type, and add them on the road.
"""
self.vehicle = MDPVehicle.create_random(
self.road, 25, spacing=self.config["initial_spacing"])
self.road.vehicles.append(self.vehicle)
# create conservative cars on the road
vehicles_type1 = utils.class_from_path(
self.config["other_vehicles_type"])
vehicles_type2 = utils.class_from_path(
self.config["aggressive_vehicle_type"])
vehicles_type3 = utils.class_from_path(
self.config["aggressive_vehicle_type2"])
# add some aggressive vehicles in the road
count_aggressive = 0
for _ in range(self.config["vehicles_count"] +
self.config["num_aggressive"]):
a = np.random.randint(low=1, high=5)
if a == 1:
count_aggressive += 1
self.road.vehicles.append(
vehicles_type2.create_random(self.road))
if count_aggressive < 3:
self.road.vehicles.append(
vehicles_type3.create_random(self.road))
else:
self.road.vehicles.append(
vehicles_type1.create_random(self.road))
print("number of aggressive vehicles ", count_aggressive)
def _make_vehicles(self, background_vehicle=None,
auto_vehicle=(0, 50, 24)):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
background_vehicle: Each item has [lane,position,velocity]
"""
road = self.road
ego_vehicle = MDPVehicle(road, road.network.get_lane(("a", "b", auto_vehicle[0])).position(auto_vehicle[1], 0),
velocity=auto_vehicle[2])
road.vehicles.append(ego_vehicle)
if self.mode == "ACC":
other_vehicles_type = MDPVehicle
elif self.mode == "NDD":
other_vehicles_type = IDMVehicle
else:
raise NotImplementedError("Not supported mode")
for lane_id in range(3):
if lane_id != 0:
position_tmp = np.random.uniform(low=0, high=100)
velocity_tmp = 30
road.vehicles.append(
other_vehicles_type(road, road.network.get_lane(("a", "b", lane_id)).position(position_tmp, 0),
velocity=velocity_tmp))
else:
position_tmp = auto_vehicle[1]
velocity_tmp = auto_vehicle[2]
position_tmp = position_tmp + 50
velocity_tmp = 20
road.vehicles.append(
other_vehicles_type(road, road.network.get_lane(("a", "b", lane_id)).position(position_tmp, 0),
velocity=velocity_tmp))
self.vehicle = ego_vehicle
def _make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
for _ in range(6):
self._spawn_vehicles()
[(self.road.act(), self.road.step(0.1)) for _ in range(15)]
other_vehicles_type = utils.class_from_path(
self.config["other_vehicles_type"])
other_vehicles_type.DISTANCE_WANTED = 2 # Low jam distance
other_vehicles_type.COMFORT_ACC_MAX = 6
other_vehicles_type.COMFORT_ACC_MIN = -3
vehicle = other_vehicles_type.make_on_lane(self.road, ("o1", "ir1", 0),
longitudinal=0,
velocity=None)
vehicle.plan_route_to("o3")
self.road.vehicles.append(vehicle)
# Ego-vehicle
ego_lane = self.road.network.get_lane(("o0", "ir0", 0))
ego_vehicle = MDPVehicle(self.road,
ego_lane.position(0, 0),
velocity=ego_lane.speed_limit,
heading=ego_lane.heading_at(0)).plan_route_to(
"o" + str(self.np_random.randint(4)))
MDPVehicle.SPEED_MIN = 0
MDPVehicle.SPEED_MAX = 9
MDPVehicle.SPEED_COUNT = 3
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
def main():
env = gym.make('highway-v0')
road = Road.create_random_road(lanes_count=2,
lane_width=4.0,
vehicles_count=0,
vehicles_type=IDMVehicle)
vehicle = MDPVehicle.create_random(road)
road.vehicles.append(vehicle)
env.road = road
env.vehicle = vehicle
agent = MCTSAgent(env, iterations=100, temperature=20 *
5) # compare step by subtree and step by prior
sim = Simulation(env, agent)
t = 0
while not sim.done:
sim.step()
t += 1
if t == 10:
print('Added obstacle')
env.road.vehicles.append(
Obstacle(
road,
[env.vehicle.position[0] + 50., env.vehicle.position[1]]))
sim.close()
def _make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
position_deviation = 2
velocity_deviation = 2
# Ego-vehicle
ego_lane = self.road.network.get_lane(("ser", "ses", 0))
ego_vehicle = MDPVehicle(
self.road,
ego_lane.position(140, 0),
velocity=5,
heading=ego_lane.heading_at(140)).plan_route_to("nxs")
MDPVehicle.SPEED_MIN = 0
MDPVehicle.SPEED_MAX = 15
MDPVehicle.SPEED_COUNT = 4
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
# Incoming vehicle
destinations = ["exr", "sxr", "nxr"]
other_vehicles_type = utils.class_from_path(
self.config["other_vehicles_type"])
vehicle = other_vehicles_type.make_on_lane(
self.road, ("we", "sx", 1),
longitudinal=5 + self.np_random.randn() * position_deviation,
velocity=16 + self.np_random.randn() * velocity_deviation)
if self.config["incoming_vehicle_destination"] is not None:
destination = destinations[
self.config["incoming_vehicle_destination"]]
else:
destination = self.np_random.choice(destinations)
vehicle.plan_route_to(destination)
vehicle.randomize_behavior()
self.road.vehicles.append(vehicle)
# Other vehicles
for i in list(range(1, 2)) + list(range(-1, 0)):
vehicle = other_vehicles_type.make_on_lane(
self.road, ("we", "sx", 0),
longitudinal=20 * i +
self.np_random.randn() * position_deviation,
velocity=16 + self.np_random.randn() * velocity_deviation)
vehicle.plan_route_to(self.np_random.choice(destinations))
vehicle.randomize_behavior()
self.road.vehicles.append(vehicle)
# Entering vehicle
vehicle = other_vehicles_type.make_on_lane(
self.road, ("eer", "ees", 0),
longitudinal=50 + self.np_random.randn() * position_deviation,
velocity=16 + self.np_random.randn() * velocity_deviation)
vehicle.plan_route_to(self.np_random.choice(destinations))
vehicle.randomize_behavior()
self.road.vehicles.append(vehicle)
def _create_vehicles(self):
"""
Create some new random vehicles of a given type, and add them on the road.
"""
# target AV
self.target_vehicle = MDPVehicle.create_random(
self.road, 25, spacing=self.config["initial_spacing"])
self.target_vehicle.color = (200, 0, 150) # purple
self.road.vehicles.append(self.target_vehicle)
# attacker: training agent
self.vehicle = MDPVehicle.create_random(
self.road, 25, spacing=self.config["initial_spacing"])
self.road.vehicles.append(self.vehicle)
vehicles_type = utils.class_from_path(
self.config["other_vehicles_type"])
for _ in range(self.config["vehicles_count"]):
self.road.vehicles.append(vehicles_type.create_random(self.road))
def _create_vehicles(self):
"""
Create some new random vehicles of a given type, and add them on the road.
"""
self.vehicle = MDPVehicle.create_random(self.road, 25, spacing=self.config["initial_spacing"])
self.road.vehicles.append(self.vehicle)
vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
for _ in range(self.config["vehicles_count"]):
self.road.vehicles.append(vehicles_type.create_random(self.road))
def _create_road(self):
road = Road.create_random_road(
lanes_count=self.config["lanes_count"],
vehicles_count=self.config["vehicles_count"],
vehicles_type=utils.class_from_path(
self.config["other_vehicles_type"]),
np_random=self.np_random)
vehicle = MDPVehicle.create_random(
road,
25,
spacing=self.config["initial_spacing"],
prepend=True,
np_random=self.np_random)
road.vehicles.append(vehicle)
return road, vehicle
def _make_vehicles(self, n_vehicles=10):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
# Configure vehicles
vehicle_type = utils.class_from_path(
self.config["other_vehicles_type"])
vehicle_type.DISTANCE_WANTED = 2 # Low jam distance
vehicle_type.COMFORT_ACC_MAX = 6
vehicle_type.COMFORT_ACC_MIN = -3
# Random vehicles
simulation_steps = 3
for t in range(n_vehicles - 1):
self._spawn_vehicle(np.linspace(0, 80, n_vehicles)[t])
for _ in range(simulation_steps):
[(self.road.act(), self.road.step(1 / self.SIMULATION_FREQUENCY))
for _ in range(self.SIMULATION_FREQUENCY)]
# Challenger vehicle
v = self._spawn_vehicle(60,
spawn_probability=1,
go_front=True,
position_deviation=0.1,
velocity_deviation=0)
if v:
v.color = (255, 0, 0)
# Ego-vehicle
MDPVehicle.SPEED_MIN = 0
MDPVehicle.SPEED_MAX = 9
MDPVehicle.SPEED_COUNT = 3
MDPVehicle.TAU_A = 1.0
ego_lane = self.road.network.get_lane(("o0", "ir0", 0))
destination = self.config["destination"] or "o" + str(
self.np_random.randint(4))
ego_vehicle = MDPVehicle(self.road,
ego_lane.position(60, 0),
velocity=ego_lane.speed_limit,
heading=ego_lane.heading_at(50)) \
.plan_route_to(destination)
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
for v in self.road.vehicles: # Prevent early collisions
if v is not ego_vehicle and np.linalg.norm(
v.position - ego_vehicle.position) < 20:
self.road.vehicles.remove(v)
def _make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
# Ego-vehicle
ego_lane = self.road.network.get_lane(("o0", "ir0", 0))
ego_vehicle = MDPVehicle(self.road,
ego_lane.position(0, 0),
velocity=9,
heading=ego_lane.heading_at(0)).plan_route_to(
"o" + str(self.np_random.randint(4)))
MDPVehicle.SPEED_MIN = 0
MDPVehicle.SPEED_MAX = 9
MDPVehicle.SPEED_COUNT = 3
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
def make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
road = self.road
ego_vehicle = MDPVehicle(road,
road.network.get_lane(
("a", "b", 1)).position(30, 0),
velocity=30)
road.vehicles.append(ego_vehicle)
other_vehicles_type = utils.class_from_path(
self.config["other_vehicles_type"])
road.vehicles.append(
other_vehicles_type(road,
road.network.get_lane(
("a", "b", 0)).position(90, 0),
velocity=29))
if 0:
other_vehicles_type = utils.class_from_path(
self.config["other_vehicles_type"])
road.vehicles.append(
other_vehicles_type(road,
road.network.get_lane(
("a", "b", 0)).position(90, 0),
velocity=29))
road.vehicles.append(
other_vehicles_type(road,
road.network.get_lane(
("a", "b", 1)).position(70, 0),
velocity=31))
road.vehicles.append(
other_vehicles_type(road,
road.network.get_lane(
("a", "b", 0)).position(5, 0),
velocity=31.5))
merging_v = other_vehicles_type(road,
road.network.get_lane(
("j", "k",
0)).position(110, 0),
velocity=20)
merging_v.target_velocity = 30
road.vehicles.append(merging_v)
self.vehicle = ego_vehicle
def _make_vehicles(self, n_vehicles=10):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as multiple ego-vehicle.
:return: the ego-vehicle
"""
# Configure vehicles
vehicle_type = utils.class_from_path(self.config["other_vehicles_type"])
vehicle_type.DISTANCE_WANTED = 2 # Low jam distance
vehicle_type.COMFORT_ACC_MAX = 6
vehicle_type.COMFORT_ACC_MIN = -3
# Random vehicles
simulation_steps = 3
# for t in range(n_vehicles - 1):
# self._spawn_vehicle(np.linspace(0, 80, n_vehicles)[t])
# for _ in range(simulation_steps):
# [(self.road.act(), self.road.step(1 / self.SIMULATION_FREQUENCY)) for _ in range(self.SIMULATION_FREQUENCY)]
# Challenger vehicle
# self._spawn_vehicle(60, spawn_probability=1, go_straight=True, position_deviation=0.1, velocity_deviation=0)
# Ego-vehicle
MDPVehicle.SPEED_MIN = 0
MDPVehicle.SPEED_MAX = 9
MDPVehicle.SPEED_COUNT = 3
# MDPVehicle.TAU_A = 1.0
# ego_lane = self.road.network.get_lane(("o0", "ir0", 0))
ego_lane = self.road.network.get_lane(("o0", "ir0", 0))
# destination = self.config["destination"] or "o" + str(self.np_random.randint(1, 4))
destination = "o1"
# ego_lane.position(max(0, np.random.normal(10, 10)), 0),
ego_vehicle1 = MDPVehicle(self.road,
ego_lane.position(max(0, np.random.normal(10, 5)), 0),
velocity=ego_lane.speed_limit,
heading=ego_lane.heading_at(50)) \
.plan_route_to(destination)
self.road.vehicles.append(ego_vehicle1)
ego_lane = self.road.network.get_lane(("o1", "ir1", 0))
# destination = self.config["destination"] or "o" + str(self.np_random.randint(1, 4))
destination = "o2"
ego_vehicle2 = MDPVehicle(self.road,
ego_lane.position(max(0, np.random.normal(10, 5)), 0),
velocity=ego_lane.speed_limit,
heading=ego_lane.heading_at(50)) \
.plan_route_to(destination)
self.road.vehicles.append(ego_vehicle2)
ego_lane = self.road.network.get_lane(("o2", "ir2", 0))
# destination = self.config["destination"] or "o" + str(self.np_random.randint(1, 4))
destination = "o3"
ego_vehicle3 = MDPVehicle(self.road,
ego_lane.position(max(0, np.random.normal(10, 5)), 0),
velocity=ego_lane.speed_limit,
heading=ego_lane.heading_at(50)) \
.plan_route_to(destination)
self.road.vehicles.append(ego_vehicle3)
ego_lane = self.road.network.get_lane(("o3", "ir3", 0))
# destination = self.config["destination"] or "o" + str(self.np_random.randint(1, 4))
destination = "o0"
ego_vehicle4 = MDPVehicle(self.road,
ego_lane.position(max(0, np.random.normal(10, 5)), 0),
velocity=ego_lane.speed_limit,
heading=ego_lane.heading_at(50)) \
.plan_route_to(destination)
self.road.vehicles.append(ego_vehicle4)
self.vehicle = [ego_vehicle1, ego_vehicle2, ego_vehicle3, ego_vehicle4]
def _make_vehicles(self):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
"""
"""
road = self.road
other_vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
car_number_each_lane = 5
# reset_position_range = (30, 40)
reset_position_range = (100, 0)
# reset_lane = random.choice(road.lanes)
for l in road.lanes[:3]:
reset_lane = road.lanes[2]
cars_on_lane = car_number_each_lane
reset_position = (32, 0)
if l is reset_lane:
cars_on_lane += 1
# reset_position = random.choice(range(5, 6))
# github-version
position_deviation = 2
velocity_deviation = 2
# Ego-vehicle
# ego_lane = self.road.network.get_lane(("ser", "ses", 0))
ego_lane = self.road.get_lane((100, 0))
print("\nego_lane.position:", ego_lane.position(140, 0), "\n")
# ego_vehicle = MDPVehicle(self.road,
# ego_lane.position(140, 0),
# velocity=5,
# heading=ego_lane.heading_at(140)).plan_route_to("nxs")
ego_vehicle = IDMVehicle(road, (60, 0), np.pi / 2, velocity=10, max_length=500)
# MDPVehicle.SPEED_MIN = 0
# MDPVehicle.SPEED_MAX = 15
# MDPVehicle.SPEED_COUNT = 4
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
# Incoming vehicle
# destinations = ["exr", "sxr", "nxr"]
other_vehicles_type = utils.class_from_path(self.config["other_vehicles_type"])
vehicle = other_vehicles_type.make_on_lane(self.road,
(0, 30),
longitudinal=5 + self.np_random.randn() * position_deviation,
velocity=16 + self.np_random.randn() * velocity_deviation)
# if self.config["incoming_vehicle_destination"] is not None:
# destination = destinations[self.config["incoming_vehicle_destination"]]
# else:
# destination = self.np_random.choice(destinations)
# vehicle.plan_route_to(destination)
# vehicle.randomize_behavior()
self.road.vehicles.append(vehicle)
# Other vehicles
for i in list(range(1, 2)) + list(range(-1, 0)):
vehicle = other_vehicles_type.make_on_lane(self.road,
(30 * i, 0),
longitudinal=20 * i + self.np_random.randn() * position_deviation,
velocity=16 + self.np_random.randn() * velocity_deviation)
# vehicle.plan_route_to(self.np_random.choice(destinations))
# vehicle.randomize_behavior()
self.road.vehicles.append(vehicle)
# Entering vehicle
# vehicle = other_vehicles_type.make_on_lane(self.road,
# ("eer", "ees", 0),
# longitudinal=50 + self.np_random.randn() * position_deviation,
# velocity=16 + self.np_random.randn() * velocity_deviation)
# vehicle.plan_route_to(self.np_random.choice(destinations))
# vehicle.randomize_behavior()
# self.road.vehicles.append(vehicle)
vehicle = MDPVehicle.create_random(self.road, 25, spacing=3, np_random=self.np_random)
self.road.vehicles.append(vehicle)
for v in self.road.vehicles:
lane_index = v.lane_index
self.road.lanes[lane_index].vehicles.append(v)
"""
position_deviation = 5
velocity_deviation = 1.5
other_vehicles_mandatory = False
max_l = 700
road = self.road
other_vehicles_type = utils.class_from_path(
self.config["other_vehicles_type"])
car_number_each_lane = 1
# reset_position_range = (30, 40)
# reset_lane = random.choice(road.lanes)
reset_lane = ("ser", "ses", 0)
ego_vehicle = None
num = 0
print("lanes:", self.road.network.LANES, "\n")
# self.road.network.LANES_NUMBER = 28
for l in self.road.network.LANES:
# print("l:",l,"\n")
lane = road.network.get_lane(l)
cars_on_lane = car_number_each_lane
reset_position = None
if l == reset_lane:
cars_on_lane += 1
reset_position = random.choice(range(0, 3))
# reset_position = 2
for j in range(cars_on_lane):
if not ego_vehicle:
ego_lane = self.road.network.get_lane(("ser", "ses", 0))
if self.switch:
ego_vehicle = MDPVehicle(self.road,
ego_lane.position(110, 0),
velocity=15,
heading=ego_lane.heading_at(
110)).plan_route_to("nxs")
else:
ego_vehicle = IDMVehicle(self.road,
ego_lane.position(110, 0),
velocity=15,
heading=ego_lane.heading_at(
110)).plan_route_to("wxs")
# ego_vehicle.destination = 1
ego_vehicle.id = 0
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
lane.vehicles.append(ego_vehicle)
else:
destinations = ["wxr", "sxr", "nxr", "exr"]
birth_place = [("ee", "nx", 0), ("ne", "wx", 0),
("we", "sx", 0), ("se", "ex", 0),
("ee", "nx", 1), ("ne", "wx", 1),
("we", "sx", 1), ("se", "ex", 1),
("wer", "wes", 0), ("eer", "ees", 0),
("ner", "nes", 0), ("ser", "ses", 0),
("wxs", "wxr", 0), ("exs", "exr", 0),
("nxs", "nxr", 0), ("sxs", "sxr", 0)]
car = other_vehicles_type.make_on_lane(
self.road,
birth_place[np.random.randint(0, 16)],
longitudinal=70 +
np.random.randint(1, 5) * position_deviation,
velocity=5 +
np.random.randint(1, 10) * velocity_deviation)
if self.config["incoming_vehicle_destination"] is not None:
destination = destinations[
self.config["incoming_vehicle_destination"]]
else:
destination = destinations[np.random.randint(0, 4)]
# destination = self.np_random.choice(destinations)
"""
if 0<= num <=6:
destinations = ["wxr", "sxr", "nxr"]
car = other_vehicles_type.make_on_lane(self.road, ("ee", "nx", 1),
longitudinal=5 + self.np_random.randn() * position_deviation,
velocity=5 + self.np_random.randn() * velocity_deviation)
if self.config["incoming_vehicle_destination"] is not None:
destination = destinations[self.config["incoming_vehicle_destination"]]
else:
destination = self.np_random.choice(destinations)
# if other_vehicles_mandatory:
# car.destination = 1
# car.plan_route_to(destination)
# car.randomize_behavior()
# self.road.vehicles.append(car)
# road.vehicles.append(car)
# lane.vehicles.append(car)
elif 7<=num<=13:
destinations = ["exr", "sxr", "wxr"]
car = other_vehicles_type.make_on_lane(self.road, ("ne", "wx", 1),
longitudinal=5 + self.np_random.randn() * position_deviation,
velocity=5 + self.np_random.randn() * velocity_deviation)
if self.config["incoming_vehicle_destination"] is not None:
destination = destinations[self.config["incoming_vehicle_destination"]]
else:
destination = self.np_random.choice(destinations)
# if other_vehicles_mandatory:
# car.destination = 1
# car.plan_route_to(destination)
# car.randomize_behavior()
# self.road.vehicles.append(car)
# road.vehicles.append(car)
# lane.vehicles.append(car)
elif 14<=num<=20:
destinations = ["exr", "sxr", "nxr"]
car = other_vehicles_type.make_on_lane(self.road, ("we", "sx", 1),
longitudinal=5 + self.np_random.randn() * position_deviation,
velocity=5 + self.np_random.randn() * velocity_deviation)
if self.config["incoming_vehicle_destination"] is not None:
destination = destinations[self.config["incoming_vehicle_destination"]]
else:
destination = self.np_random.choice(destinations)
# if other_vehicles_mandatory:
# car.destination = 1
# car.plan_route_to(destination)
# car.randomize_behavior()
# self.road.vehicles.append(car)
# road.vehicles.append(car)
# lane.vehicles.append(car)
else :
destinations = ["exr", "wxr", "nxr"]
car = other_vehicles_type.make_on_lane(self.road, ("se", "ex", 1),
longitudinal=5 + self.np_random.randn() * position_deviation,
velocity=5 + self.np_random.randn() * velocity_deviation)
if self.config["incoming_vehicle_destination"] is not None:
destination = destinations[self.config["incoming_vehicle_destination"]]
else:
destination = self.np_random.choice(destinations)
# if other_vehicles_mandatory:
# car.destination = 1
# car.plan_route_to(destination)
# car.randomize_behavior()
# self.road.vehicles.append(car)
# road.vehicles.append(car)
# lane.vehicles.append(car)
"""
car.plan_route_to(destination)
car.randomize_behavior()
self.road.vehicles.append(car)
# road.vehicles.append(car)
lane.vehicles.append(car)
# if other_vehicles_mandatory:
# car.destination = 1
# road.vehicles.append(car)
# l.vehicles.append(car)
num += 1
# print("make_vehicle finish")
for i in range(self.road.network.LANES_NUMBER):
lane = road.network.get_lane(self.road.network.LANES[i])
# print("lane:", lane.LANEINDEX, "\n")
lane.vehicles = sorted(
lane.vehicles,
key=lambda x: lane.local_coordinates(x.position)[0])
# print("len of lane.vehicles:", len(lane.vehicles), "\n")
for j, v in enumerate(lane.vehicles):
# print("i:",i,"\n")
v.vehicle_index_in_line = j
def _make_vehicles(self,
given_ini=None,
auto_vehicle=(0, global_val.initial_CAV_position,
global_val.initial_CAV_speed)):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
given_ini: [[Lane 0], [Lane 1], [Lane 2]], Each item [Lane i] = [[x,velocity],..., [x,velocity]]
"""
ego_vehicle = MDPVehicle(self.road,
self.road.network.get_lane(
("a", "b", auto_vehicle[0])).position(
auto_vehicle[1], 0),
velocity=auto_vehicle[2])
# ego_vehicle = IDMVehicle_CAV(self.road, self.road.network.get_lane(("a", "b", auto_vehicle[0])).position(auto_vehicle[1], 0),
# velocity=auto_vehicle[2])
# print("aaaa", ego_vehicle.COMFORT_ACC_MAX)
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
other_vehicles_type = Pure_NDDVehicle
ini_data = None
if given_ini:
assert self.generate_vehicle_mode == "Given_ini"
if self.generate_vehicle_mode == "Test":
if len(self.presum_list_forward) == 0 or len(
self.presum_list_backward) == 0:
# If it is the first time generate by NDD, preprocess the data first
self._preprocess_CF_data()
print(
"================Generate CF data presum_list finished!================"
)
lane_list = [0, 1, 2, 2] #[1, 1, 1, 2, 2, 2] # [1, 1, 1, 2, 2, 2]
position_list = [
412, 430, 420, 440
] #, 105, 120, 90, 105, 120] # [90, 105, 120, 90, 105, 120]
velocity_list = [
20, 26, 30, 30
] #, 32, 28, 36, 34, 32] # [34, 32, 28, 36, 34, 32]
for i in range(len(lane_list)):
v = other_vehicles_type(
self.road,
self.road.network.get_lane(
("a", "b",
lane_list[i])).position(position_list[i], 0), 0,
velocity_list[i])
self.road.vehicles.append(v)
if self.generate_vehicle_mode == "NDD":
if len(self.presum_list_forward) == 0 or len(
self.presum_list_backward) == 0:
# If it is the first time generate by NDD, preprocess the data first
self._preprocess_CF_data()
print(
"================Generate CF data presum_list finished!================"
)
vehicle_list = [
] # each list in this container is for vehicles in each lane (without CAV)
lane_num = len(self.vehicle.road.network.graph["a"]["b"])
for lane_idx in range(lane_num):
generate_forward = True
generate_finish = False
vehicle_forward_list_one_lane, vehicle_backward_list_one_lane = [], []
if lane_idx == 0:
back_vehicle_speed, front_vehicle_speed = auto_vehicle[
2], auto_vehicle[2]
back_vehicle_position, front_vehicle_position = auto_vehicle[
1], auto_vehicle[1]
else:
rand_speed, rand_position, _ = self._gen_NDD_veh()
back_vehicle_speed, front_vehicle_speed = rand_speed, rand_speed
back_vehicle_position, front_vehicle_position = rand_position, rand_position
vehicle_forward_list_one_lane.append(
(rand_position, rand_speed))
while generate_finish is False:
if generate_forward is True:
# print(back_vehicle_speed)
if back_vehicle_speed < global_val.v_low:
presum_list = self.presum_list_forward[
global_val.v_to_idx_dic[global_val.v_low]]
else:
presum_list = self.presum_list_forward[
global_val.v_to_idx_dic[int(
back_vehicle_speed)]]
# decide CF or FF
random_number_CF = np.random.uniform()
if random_number_CF > self.CF_percent: # FF
rand_speed, rand_position, _ = self._gen_NDD_veh(
) # self._gen_one_random_veh()
v_generate = rand_speed
pos_generate = back_vehicle_position + self.ff_dis + rand_position + global_val.LENGTH
else: # CF
random_number = np.random.uniform()
r_idx, rr_idx = divmod(
bisect.bisect_left(presum_list, random_number),
self.num_rr)
try:
r, rr = global_val.r_to_idx_dic.inverse[
r_idx], global_val.rr_to_idx_dic.inverse[
rr_idx]
except:
# print("back_vehicle_speed:", back_vehicle_speed)
if back_vehicle_speed > 35:
r, rr = 50, -2
else:
r, rr = 50, 2
# Accelerated training for initialization
r = r - global_val.Initial_range_adjustment_AT + global_val.Initial_range_adjustment_SG
if r <= global_val.Initial_range_adjustment_SG:
r = r + global_val.r_high
v_generate = back_vehicle_speed + rr
pos_generate = back_vehicle_position + r + global_val.LENGTH
vehicle_forward_list_one_lane.append(
(pos_generate, v_generate))
back_vehicle_speed = v_generate
back_vehicle_position = pos_generate
v = other_vehicles_type(
self.road,
self.road.network.get_lane(
('a', 'b',
lane_idx)).position(pos_generate, 0), 0,
v_generate)
self.road.vehicles.append(v)
if back_vehicle_position >= self.gen_length:
generate_forward = False
generate_finish = True
vehicle_list_each_lane = vehicle_backward_list_one_lane + vehicle_forward_list_one_lane
vehicle_list.append(vehicle_list_each_lane)
if self.generate_vehicle_mode == "Given_ini":
for lane_idx in range(self.max_lane + 1):
ini_one_lane = given_ini[lane_idx]
for i in range(len(ini_one_lane)):
veh_data = ini_one_lane[i]
x, velocity = veh_data[0], veh_data[1]
v = other_vehicles_type(
self.road,
self.road.network.get_lane(
("a", "b", lane_idx)).position(x, 0), 0, velocity)
self.road.vehicles.append(v)
return ini_data
def _make_vehicles(self,
given_ini=None,
auto_vehicle=(0, global_val.initial_CAV_position,
global_val.initial_CAV_speed)):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:return: the ego-vehicle
given_ini: [[Lane 0], [Lane 1], [Lane 2]], Each item [Lane i] = [[x,velocity],..., [x,velocity]]
"""
ego_vehicle = MDPVehicle(self.road,
self.road.network.get_lane(
("a", "b", auto_vehicle[0])).position(
auto_vehicle[1], 0),
velocity=auto_vehicle[2])
self.road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
# other_vehicles_type = NDD_heuristic_Vehicle
# other_vehicles_type = IDMVehicle
# other_vehicles_type = IDM_heuristic_Vehicle
other_vehicles_type = NDDVehicle
# other_vehicles_type = NDD_heuristic_Vehicle
# # !!!IDMVehicle NDDVehicle
ini_data = None
if given_ini:
assert self.generate_vehicle_mode == "Given_ini"
if self.generate_vehicle_mode == "Test":
if len(self.presum_list_forward) == 0 or len(
self.presum_list_backward) == 0:
# If it is the first time generate by NDD, preprocess the data first
self._preprocess_CF_data()
print(
"================Generate CF data presum_list finished!================"
)
lane_list = [1, 2, 1, 1, 2, 2,
2] #[1, 1, 1, 2, 2, 2] # [1, 1, 1, 2, 2, 2]
position_list = [
398, 398, 380, 346, 341, 488, 302
] #, 105, 120, 90, 105, 120] # [90, 105, 120, 90, 105, 120]
velocity_list = [
28, 21, 31, 27, 24, 27, 28
] #, 32, 28, 36, 34, 32] # [34, 32, 28, 36, 34, 32]
for i in range(len(lane_list)):
v = other_vehicles_type(
self.road,
self.road.network.get_lane(
("a", "b",
lane_list[i])).position(position_list[i], 0), 0,
velocity_list[i])
self.road.vehicles.append(v)
if self.generate_vehicle_mode == "Random":
for _ in range(self.config["vehicles_count"]):
new_vehicle = self.create_random_acc_training(
other_vehicles_type)
if new_vehicle:
self.road.vehicles.append(new_vehicle)
if self.generate_vehicle_mode == "NDD":
if len(self.presum_list_forward) == 0 or len(
self.presum_list_backward) == 0:
# If it is the first time generate by NDD, preprocess the data first
self._preprocess_CF_data()
print(
"================Generate CF data presum_list finished!================"
)
vehicle_list = [
] # each list in this container is for vehicles in each lane (without CAV)
lane_num = len(self.vehicle.road.network.graph["a"]["b"])
for lane_idx in range(lane_num):
generate_forward = True
generate_finish = False
vehicle_forward_list_one_lane, vehicle_backward_list_one_lane = [], []
if lane_idx == 0:
back_vehicle_speed, front_vehicle_speed = auto_vehicle[
2], auto_vehicle[2]
back_vehicle_position, front_vehicle_position = auto_vehicle[
1], auto_vehicle[1]
else:
rand_speed, rand_position, _ = self._gen_NDD_veh()
back_vehicle_speed, front_vehicle_speed = rand_speed, rand_speed
back_vehicle_position, front_vehicle_position = rand_position, rand_position
vehicle_forward_list_one_lane.append(
(rand_position, rand_speed))
while generate_finish is False:
if generate_forward is True:
# print(back_vehicle_speed)
if back_vehicle_speed < global_val.v_low:
presum_list = self.presum_list_forward[
global_val.v_to_idx_dic[global_val.v_low]]
else:
presum_list = self.presum_list_forward[
global_val.v_to_idx_dic[int(
back_vehicle_speed)]]
# decide CF or FF
random_number_CF = np.random.uniform()
if random_number_CF > self.CF_percent: # FF
rand_speed, rand_position, _ = self._gen_NDD_veh(
) # self._gen_one_random_veh()
v_generate = rand_speed
pos_generate = back_vehicle_position + self.ff_dis + rand_position + global_val.LENGTH
else: # CF
random_number = np.random.uniform()
r_idx, rr_idx = divmod(
bisect.bisect_left(presum_list, random_number),
self.num_rr)
try:
r, rr = global_val.r_to_idx_dic.inverse[
r_idx], global_val.rr_to_idx_dic.inverse[
rr_idx]
except:
# print("back_vehicle_speed:", back_vehicle_speed)
if back_vehicle_speed > 35:
r, rr = 50, -2
else:
r, rr = 50, 2
# Accelerated training for initialization
r = r - global_val.Initial_range_adjustment_AT + global_val.Initial_range_adjustment_SG
if r <= global_val.Initial_range_adjustment_SG:
r = r + global_val.r_high
v_generate = back_vehicle_speed + rr
pos_generate = back_vehicle_position + r + global_val.LENGTH
vehicle_forward_list_one_lane.append(
(pos_generate, v_generate))
back_vehicle_speed = v_generate
back_vehicle_position = pos_generate
v = other_vehicles_type(
self.road,
self.road.network.get_lane(
('a', 'b',
lane_idx)).position(pos_generate, 0), 0,
v_generate)
self.road.vehicles.append(v)
if back_vehicle_position >= self.gen_length:
generate_forward = False
generate_finish = True
vehicle_list_each_lane = vehicle_backward_list_one_lane + vehicle_forward_list_one_lane
vehicle_list.append(vehicle_list_each_lane)
# print(vehicle_list_each_lane)
# print('Lane idx:', str(lane_idx+1), " ", vehicle_list_each_lane, len(vehicle_list_each_lane))
# print("\n")
if self.generate_vehicle_mode == "NDD_with_exposure_frequency":
if len(self.presum_list_forward) == 0 or len(
self.presum_list_backward) == 0:
# If it is the first time generate by NDD, preprocess the data first
self._preprocess_CF_data()
print(
"================Generate CF data presum_list finished!================"
)
vehicle_list = [
] # each list in this container is for vehicles in each lane (without CAV)
lane_num = len(self.vehicle.road.network.graph["a"]["b"])
exposure_freq_list = [
] # exposure frequency for each lines initialization
vehicle_data_list = []
for lane_idx in range(lane_num):
generate_forward = True
generate_finish = False
vehicle_forward_list_one_lane, vehicle_backward_list_one_lane = [], []
exposure_freq_list_one_lane, vehicle_data_list_one_lane = [], []
exposure_freq_one_lane = 1
if lane_idx == 0:
back_vehicle_speed, front_vehicle_speed = auto_vehicle[
2], auto_vehicle[2]
back_vehicle_position, front_vehicle_position = auto_vehicle[
1], auto_vehicle[1]
else:
rand_speed, rand_position, exposure_freq = self._gen_NDD_veh(
)
back_vehicle_speed, front_vehicle_speed = rand_speed, rand_speed
back_vehicle_position, front_vehicle_position = rand_position, rand_position
if global_val.critical_ini_start <= back_vehicle_position <= global_val.critical_ini_end:
exposure_freq_one_lane *= exposure_freq
exposure_freq_list_one_lane.append(exposure_freq)
vehicle_data_list_one_lane.append(
[None, None, None]) # For the first BV
vehicle_forward_list_one_lane.append(
(rand_position, rand_speed))
while generate_finish is False:
if generate_forward is True:
# print(back_vehicle_speed)
if back_vehicle_speed < global_val.v_low:
presum_list = self.presum_list_forward[
global_val.v_to_idx_dic[global_val.v_low]]
else:
back_vehicle_speed = int(back_vehicle_speed)
presum_list = self.presum_list_forward[
global_val.v_to_idx_dic[back_vehicle_speed]]
# decide CF or FF
random_number_CF = np.random.uniform()
if random_number_CF > self.CF_percent or back_vehicle_speed == 40: # FF
rand_speed, rand_position, exposure_freq = self._gen_NDD_veh(
) # self._gen_one_random_veh()
v_generate = rand_speed
pos_generate = back_vehicle_position + self.ff_dis + rand_position + global_val.LENGTH
if global_val.critical_ini_start <= pos_generate <= global_val.critical_ini_end:
exposure_freq_one_lane *= exposure_freq
exposure_freq_list_one_lane.append(
exposure_freq)
vehicle_data_list_one_lane.append([
back_vehicle_speed, self.ff_dis +
rand_position + global_val.LENGTH,
rand_speed - back_vehicle_speed
]) # !!! Need discuss
else: # CF
random_number = np.random.uniform()
idx = bisect.bisect_left(presum_list,
random_number)
r_idx, rr_idx = divmod(idx, self.num_rr)
try:
r, rr = global_val.r_to_idx_dic.inverse[
r_idx], global_val.rr_to_idx_dic.inverse[
rr_idx]
except:
print("back_vehicle_speed:",
back_vehicle_speed)
if back_vehicle_speed > 35:
# back_vehicle_speed = 39
# presum_list = self.presum_list_forward[global_val.v_to_idx_dic[back_vehicle_speed]]
# random_number = np.random.uniform()
# idx = bisect.bisect_left(presum_list, random_number)
# r_idx, rr_idx = divmod(idx, self.num_rr)
# r, rr = global_val.r_to_idx_dic.inverse[r_idx], global_val.rr_to_idx_dic.inverse[rr_idx]
r, rr = 50, -2
else:
r, rr = 50, 2
# assert("Back_vehicle_speed problem")
v_generate = back_vehicle_speed + rr
pos_generate = back_vehicle_position + r + global_val.LENGTH
if global_val.critical_ini_start <= pos_generate <= global_val.critical_ini_end:
exposure_freq = presum_list[idx] - presum_list[
idx - 1] if idx >= 1 else presum_list[idx]
exposure_freq_one_lane *= exposure_freq
exposure_freq_list_one_lane.append(
exposure_freq)
vehicle_data_list_one_lane.append([
back_vehicle_speed, r + global_val.LENGTH,
rr
])
back_vehicle_speed = v_generate
back_vehicle_position = pos_generate
if back_vehicle_position >= self.gen_length:
generate_forward = False
generate_finish = True
continue
vehicle_forward_list_one_lane.append(
(pos_generate, v_generate))
v = other_vehicles_type(
self.road,
self.road.network.get_lane(
('a', 'b',
lane_idx)).position(pos_generate, 0), 0,
v_generate)
self.road.vehicles.append(v)
vehicle_list_each_lane = vehicle_backward_list_one_lane + vehicle_forward_list_one_lane
vehicle_list.append(vehicle_list_each_lane)
# exposure_freq_list.append(exposure_freq_one_lane)
exposure_freq_list.append(exposure_freq_list_one_lane)
vehicle_data_list.append(vehicle_data_list_one_lane)
# print(vehicle_list_each_lane)
# print('Lane idx:', str(lane_idx+1), " ", vehicle_list_each_lane, len(vehicle_list_each_lane))
# print("\n")
ini_data = {
"data": vehicle_data_list,
"exposure_frequency": exposure_freq_list,
"All_initial_info": vehicle_list
}
if self.generate_vehicle_mode == "Given_ini":
for lane_idx in range(self.max_lane + 1):
ini_one_lane = given_ini[lane_idx]
for i in range(len(ini_one_lane)):
veh_data = ini_one_lane[i]
x, velocity = veh_data[0], veh_data[1]
v = other_vehicles_type(
self.road,
self.road.network.get_lane(
("a", "b", lane_idx)).position(x, 0), 0, velocity)
self.road.vehicles.append(v)
return ini_data
def make_vehicles(self, other_vehicles_mandatory=False):
"""
Populate a road with several vehicles on the highway and on the merging lane, as well as an ego-vehicle.
:param other_vehicles_mandatory: if the lane changing maneuvers of other vehicles are mandatory
:return: None
"""
max_l = 500
road = self.road
other_vehicles_type = utils.class_from_path(
self.config["other_vehicles_type"])
car_number_each_lane = 15
# reset_position_range = (30, 40)
reset_position_range = (20, 30)
# reset_lane = random.choice(road.lanes)
reset_lane = road.lanes[0]
for l in road.lanes[:3]:
cars_on_lane = car_number_each_lane
reset_position = None
if l is reset_lane:
cars_on_lane += 1
reset_position = random.choice(range(5, 6))
# reset_position = 2
for i in range(cars_on_lane):
if i == reset_position:
if self.switch:
ego_vehicle = MDPVehicle(
road,
l.position(
(i + 1) *
np.random.randint(*reset_position_range), 0),
velocity=20,
max_length=max_l)
else:
ego_vehicle = IDMVehicle(
road,
l.position(
(i + 1) *
np.random.randint(*reset_position_range), 0),
velocity=20,
max_length=max_l)
ego_vehicle.destination = 1
ego_vehicle.id = 0
road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
l.vehicles.append(ego_vehicle)
else:
car = other_vehicles_type(
road,
l.position(
(i + 1) * np.random.randint(*reset_position_range),
0),
velocity=np.random.randint(18, 25),
dst=3,
max_length=max_l)
if other_vehicles_mandatory:
car.destination = 1
road.vehicles.append(car)
l.vehicles.append(car)
for l in [road.lanes[3]]:
cars_on_lane = car_number_each_lane
reset_position = None
if l is reset_lane:
cars_on_lane += 1
reset_position = random.choice(range(5, 6))
# reset_position = 2
for i in range(cars_on_lane):
if i < 8:
continue
if i == reset_position:
# ego_vehicle = MDPVehicle(road, l.position((i+1) * np.random.randint(*reset_position_range), 0),
# velocity=20, max_length=max_l)
ego_vehicle = IDMVehicle(
road,
l.position(
(i + 1) * np.random.randint(*reset_position_range),
0),
velocity=20,
max_length=max_l)
ego_vehicle.destination = 1
ego_vehicle.id = 0
road.vehicles.append(ego_vehicle)
self.vehicle = ego_vehicle
l.vehicles.append(ego_vehicle)
else:
car = other_vehicles_type(
road,
l.position(
(i + 1) * np.random.randint(*reset_position_range),
0),
velocity=np.random.randint(18, 25),
dst=3,
max_length=max_l)
if other_vehicles_mandatory:
car.destination = 1
road.vehicles.append(car)
l.vehicles.append(car)
for lane in road.lanes:
lane.vehicles = sorted(
lane.vehicles,
key=lambda x: lane.local_coordinates(x.position)[0])
for i, v in enumerate(lane.vehicles):
v.vehicle_index_in_line = i
