class LearningAgent(Agent):
"""
BasicAgent implements a basic agent that navigates scenes to reach a given
target destination. This agent respects traffic lights and other vehicles.
"""
def __init__(self, world):
"""
:param vehicle: actor to apply to local planner logic onto
"""
super(LearningAgent, self).__init__(world.player)
self._world_obj = world
# Learning Model
self._model = Model()
self._THW = None
self._target_speed = None
self._sin_param = None
self._poly_param = None
# Local plannar
self._local_planner = LocalPlanner(world.player)
self.update_parameters()
# Global plannar
self._proximity_threshold = 10.0 # meter
self._state = AgentState.NAVIGATING
self._hop_resolution = 0.2
self._path_seperation_hop = 2
self._path_seperation_threshold = 0.5
self._grp = None # global route planar
# Behavior planning
self._hazard_detected = False
self._blocked_time = None
self._perform_lane_change = False
self._front_r = []
self._left_front_r = []
self._left_back_r = []
# Update personalized parameters from model
def update_parameters(self):
self._THW = self._model.get_parameter("safe_distance")["THW"]
self._target_speed = self._model.get_parameter("target_speed") * 3.6
self._sin_param = self._model.get_parameter("sin_param")
self._poly_param = self._model.get_parameter("poly_param")
CONTROLLER_TYPE = 'PID' # options:MPC, PID, STANLEY
args_lateral_dict = {'K_P': 1.0, 'K_I': 0.4, 'K_D': 0.01, 'control_type': CONTROLLER_TYPE}
args_longitudinal_dict = {'K_P': 0.3, 'K_I': 0.2, 'K_D': 0.002}
self._local_planner.init_controller(opt_dict={'target_speed': self._target_speed,
'lateral_control_dict': args_lateral_dict,
'longitudinal_control_dict': args_longitudinal_dict})
# Start learning by collecting data
def collect(self):
# State for each step
personalization_param = []
# Time stamp
personalization_param.extend([pygame.time.get_ticks()])
# Collect vehicle position
t = self._vehicle.get_transform()
personalization_param.extend([t.location.x,
t.location.y,
t.location.z,
t.rotation.yaw])
# Collect vehicle velocity and speed
v = self._vehicle.get_velocity()
personalization_param.extend([v.x, v.y, v.z, self._get_speed()])
# Collect radar information
front_dis = 100
front_vel = 50
left_front_dis = 100
left_front_vel = 50
left_back_dis = -100
left_back_vel = 0
if self._front_r:
front_dis = self._front_r[1][0]
front_vel = self._front_r[2][0]
if self._left_front_r:
left_front_dis = self._left_front_r[1][0]
left_front_vel = self._left_front_r[2][0]
if self._left_back_r:
left_back_dis = self._left_back_r[1][0]
left_back_vel = self._left_back_r[2][0]
personalization_param.extend([front_dis, left_front_dis, left_back_dis,
front_vel, left_front_vel, left_back_vel])
self._model.collect(personalization_param)
# End collection
def end_collect(self):
self._model.end_collect()
# Train model
def train_model(self):
self._model.train_new_model()
# Set global destination and get global waypoints
def set_destination(self, location):
"""
This method creates a list of waypoints from agent's position to destination location
based on the route returned by the global router
"""
start_waypoint = self._map.get_waypoint(self._vehicle.get_location())
end_waypoint = self._map.get_waypoint(carla.Location(location[0], location[1], location[2]))
route_trace = self._trace_route(start_waypoint, end_waypoint)
assert route_trace
self._local_planner.set_global_plan(route_trace)
# Get global waypoints
def _trace_route(self, start_waypoint, end_waypoint):
"""
This method sets up a global router and returns the optimal route
from start_waypoint to end_waypoint
"""
# Setting up global router
if self._grp is None:
dao = GlobalRoutePlannerDAO(self._vehicle.get_world().get_map(), self._hop_resolution)
grp = GlobalRoutePlanner(dao)
grp.setup()
self._grp = grp
# Obtain route plan
route = self._grp.trace_route(start_waypoint.transform.location, end_waypoint.transform.location)
return route
# Get vehicle speed
def _get_speed(self):
v = self._vehicle.get_velocity()
ego_speed = math.sqrt(v.x**2 + v.y**2 + v.z**2)
return ego_speed
# Run step
def run_step(self, debug=False):
"""
Execute one step of navigation.
:return: carla.VehicleControl
"""
## Update Environment ##
# Check all the radars
if self._world_obj.front_radar.detected:
if abs(self._world_obj.front_radar.rel_pos[1]) < 1:
self._front_r = [pygame.time.get_ticks(), self._world_obj.front_radar.rel_pos,
self._world_obj.front_radar.rel_vel]
self._world_obj.front_radar.detected = False
if self._world_obj.left_front_radar.detected:
if self._world_obj.left_front_radar.rel_pos[1] < -1:
self._left_front_r =[pygame.time.get_ticks(), self._world_obj.left_front_radar.rel_pos,
self._world_obj.left_front_radar.rel_vel]
self._world_obj.left_front_radar.detected = False
if self._world_obj.left_back_radar.detected:
if self._world_obj.left_back_radar.rel_pos[1] < -1:
self._left_back_r = [pygame.time.get_ticks(), self._world_obj.left_back_radar.rel_pos,
self._world_obj.left_back_radar.rel_vel]
self._world_obj.left_back_radar.detected = False
# Remove radar data if not detected again in 0.5 second
if self._front_r and (pygame.time.get_ticks() - self._front_r[0] > 5000):
self._front_r = []
if self._left_front_r and (pygame.time.get_ticks() - self._left_front_r[0] > 5000):
self._left_front_r = []
if self._left_back_r and (pygame.time.get_ticks() - self._left_back_r[0] > 5000):
self._left_back_r = []
# Detect vehicles in front
self._hazard_detected = False
if self._front_r and (self._front_r[1][0] < 20.0):
self._hazard_detected = True
# update hazard existing time
if self._hazard_detected:
if self._blocked_time is None:
self._blocked_time = pygame.time.get_ticks()
hazard_time = 0
else:
hazard_time = pygame.time.get_ticks() - self._blocked_time
else:
self._blocked_time = None
# Get a safe_distance
safe_distance = self._THW * self._get_speed()
'''
# retrieve relevant elements for safe navigation, i.e.: traffic lights
actor_list = self._world.get_actors()
lights_list = actor_list.filter("*traffic_light*")
# check for the state of the traffic lights
light_state, traffic_light = self._is_light_red(lights_list)
if light_state:
if debug:
print('=== RED LIGHT AHEAD [{}])'.format(traffic_light.id))
self._state = AgentState.BLOCKED_RED_LIGHT
self._hazard_detected = True
'''
#print(self._state)
# Finite State Machine
# 1, Navigating
if self._state == AgentState.NAVIGATING:
if self._hazard_detected:
self._state = AgentState.BLOCKED_BY_VEHICLE
# 2, Blocked by Vehicle
elif self._state == AgentState.BLOCKED_BY_VEHICLE:
if not self._hazard_detected:
self._state = AgentState.NAVIGATING
# The vehicle is driving at a certain speed
# There is enough space
else:
if hazard_time > 5000 and \
190 > self._vehicle.get_location().x > 10 and \
10 > self._vehicle.get_location().y > 7:
self._state = AgentState.PREPARE_LANE_CHANGING
# 4, Prepare Lane Change
elif self._state == AgentState.PREPARE_LANE_CHANGING:
if not (self._front_r and self._front_r[1][0] < safe_distance) and \
not (self._left_front_r and self._left_front_r[1][0] < safe_distance) and \
not (self._left_back_r and self._left_back_r[1][0] > -10):
print(self._front_r)
print(self._left_front_r)
print(self._left_back_r)
self._state = AgentState.LANE_CHANGING
self._perform_lane_change = True
# 5, Lane Change
elif self._state == AgentState.LANE_CHANGING:
if abs(self._vehicle.get_velocity().y) < 0.5 and \
self._vehicle.get_location().y < 7.0:
self._state = AgentState.NAVIGATING
# 6, Emergency Brake
emergency_distance = safe_distance *3/5
emergency_front_speed = 1.0
if self._front_r and (self._front_r[1][0] < emergency_distance or
self._front_r[2][0] < emergency_front_speed):
self._state = AgentState.EMERGENCY_BRAKE
# Local Planner Behavior according to states
if self._state == AgentState.NAVIGATING or self._state == AgentState.LANE_CHANGING:
control = self._local_planner.run_step(debug=debug)
elif self._state == AgentState.PREPARE_LANE_CHANGING:
if self._left_front_r and self._left_front_r[1][0] < safe_distance or \
self._front_r and self._front_r[1][0] < safe_distance:
control = self._local_planner.empty_control(debug=debug)
else:
control = self._local_planner.run_step(debug=debug)
elif self._state == AgentState.BLOCKED_BY_VEHICLE:
# ACC
front_dis = self._front_r[1][0]
front_vel = self._front_r[2][0]
ego_speed = self._get_speed()
desired_speed = front_vel - (ego_speed-front_vel)/front_dis
if ego_speed > 1:
desired_speed += 2*(front_dis/ego_speed - self._THW)
control = self._local_planner.run_step(debug=debug, target_speed=desired_speed*3.6)
elif self._state == AgentState.EMERGENCY_BRAKE:
control = self._local_planner.brake()
if self._front_r:
if self._front_r[1][0] >= emergency_distance and \
self._front_r[2][0] > emergency_front_speed:
self._state = AgentState.NAVIGATING
elif self._state == AgentState.BLOCKED_RED_LIGHT:
control = self._local_planner.empty_control(debug=debug)
# When performing a lane change
if self._perform_lane_change:
# Record original destination
destination = self._local_planner.get_global_destination()
# Get lane change start location
ref_location = self._world_obj.player.get_location()
ref_yaw = self._world_obj.player.get_transform().rotation.yaw
if self._local_planner.waypoint_buffer:
waypoint = self._local_planner.waypoint_buffer[-1][0]
ref_location = waypoint.transform.location
wait_dist = 0.0 # need some time to plan
ref = [ref_location.x + wait_dist, ref_location.y, ref_yaw]
# Replace current plan with a lane change plan
DL = self._left_front_r[1][0] if self._left_front_r else 100
DH = self._left_back_r[1][0] if self._left_back_r else 100
GMM_v = [[self._vehicle.get_velocity().x, self._sin_param["lat_dis"], DL, DH]]
lane_changer = SinLaneChange(self._world_obj, self._sin_param, np.array(GMM_v))
lane_change_plan = lane_changer.get_waypoints(ref)
self._local_planner.set_local_plan(lane_change_plan)
'''
lane_changer = PolyLaneChange(self._world_obj, self._poly_param)
lane_change_plan = lane_changer.get_waypoints(ref)
self._local_planner.set_local_plan(lane_change_plan)
'''
# replan globally with new vehicle position after lane changing
new_start = self._map.get_waypoint(lane_change_plan[-1][0].transform.location)
route_trace = self._trace_route(new_start, destination)
assert route_trace
self._local_planner.add_global_plan(route_trace)
self._perform_lane_change = False
print("perform lane change")
return control
def done(self):
"""
Check whether the agent has reached its destination.
:return bool
"""
return self._local_planner.done()
class AutonomousAgent(Agent):
"""
BasicAgent implements a basic agent that navigates scenes to reach a given
target destination. This agent respects traffic lights and other vehicles.
"""
def __init__(self, world):
"""
:param vehicle: actor to apply to local planner logic onto
"""
super(AutonomousAgent, self).__init__(world.player)
self._world_obj = world
self._THW = 2
self._target_speed = None
# Local plannar
self._local_planner = LocalPlanner(world.player)
self.update_parameters()
# Global plannar
self._proximity_threshold = 10.0 # meter # Distance between waypoints
self._state = AgentState.NAVIGATING
self._hop_resolution = 0.2
self._path_seperation_hop = 2
self._path_seperation_threshold = 0.5
self._grp = None # global route planar
# Behavior planning
self._hazard_detected = False
self._blocked_time = None
self._perform_lane_change = False
self._front_r = []
self._left_front_r = []
self._left_back_r = []
# Turns positions
self.right_positions = None
self.left_positions = None
# Turn flags
self.right_turn = False
self.left_turn = False
self.temp_flag = True
self.left_positions = None
def update_parameters(self):
self._THW = 2
self._target_speed = 30
CONTROLLER_TYPE = 'PID' # options:MPC, PID, STANLEY
args_lateral_dict = {
'K_P': 1.0,
'K_I': 0.4,
'K_D': 0.01,
'control_type': CONTROLLER_TYPE
}
args_longitudinal_dict = {'K_P': 0.3, 'K_I': 0.2, 'K_D': 0.002}
self._local_planner.init_controller(
opt_dict={
'target_speed': self._target_speed,
'lateral_control_dict': args_lateral_dict,
'longitudinal_control_dict': args_longitudinal_dict
})
# Set global destination and get global waypoints
def set_destination(self, location):
"""
This method creates a list of waypoints from agent's position to destination location
based on the route returned by the global router
"""
start_waypoint = self._map.get_waypoint(self._vehicle.get_location())
end_waypoint = self._map.get_waypoint(
carla.Location(location[0], location[1], location[2]))
route_trace = self._trace_route(start_waypoint, end_waypoint)
assert route_trace
self._local_planner.set_global_plan(route_trace)
# Get global waypoints
def _trace_route(self, start_waypoint, end_waypoint):
"""
This method sets up a global router and returns the optimal route
from start_waypoint to end_waypoint
"""
# Setting up global router
if self._grp is None:
dao = GlobalRoutePlannerDAO(self._vehicle.get_world().get_map(),
self._hop_resolution)
grp = GlobalRoutePlanner(dao)
grp.setup()
self._grp = grp
# Obtain route plan
route = self._grp.trace_route(start_waypoint.transform.location,
end_waypoint.transform.location)
self.turn_positions_getter(route, RoadOption.RIGHT)
self.turn_positions_getter(route, RoadOption.LEFT)
return route
def turn_positions_getter(self, route, state):
"""
Returns list of all Left and right turns waypoints
"""
count_flag = False
temp_list = []
list_of_turn_waypoints = []
for i, j in route:
if j == state:
count_flag = True
temp_list.append(i)
continue
if count_flag:
start_waypoint = temp_list[0]
end_waypoint = temp_list[-1]
list_of_turn_waypoints.append((start_waypoint, end_waypoint))
temp_list = []
count_flag = False
if state == RoadOption.RIGHT:
self.right_positions = list_of_turn_waypoints
else:
self.left_positions = list_of_turn_waypoints
# Get vehicle speed
def _get_speed(self):
v = self._vehicle.get_velocity()
ego_speed = math.sqrt(v.x**2 + v.y**2 + v.z**2)
return ego_speed
# Run step
def run_step(self, debug=False):
"""
Execute one step of navigation.
:return: carla.VehicleControl
"""
## Update Environment ##
# Check all the radars
try:
if self._state == AgentState.EMERGENCY_BRAKE:
pass
pass
except:
pass
if self._world_obj.front_radar.detected:
if abs(self._world_obj.front_radar.rel_pos[1]) < 1:
self._front_r = [
pygame.time.get_ticks(),
self._world_obj.front_radar.rel_pos,
self._world_obj.front_radar.rel_vel
]
self._world_obj.front_radar.detected = False
if self._world_obj.left_front_radar.detected:
if self._world_obj.left_front_radar.rel_pos[1] < -1:
self._left_front_r = [
pygame.time.get_ticks(),
self._world_obj.left_front_radar.rel_pos,
self._world_obj.left_front_radar.rel_vel
]
self._world_obj.left_front_radar.detected = False
if self._world_obj.left_back_radar.detected:
if self._world_obj.left_back_radar.rel_pos[1] < -1:
self._left_back_r = [
pygame.time.get_ticks(),
self._world_obj.left_back_radar.rel_pos,
self._world_obj.left_back_radar.rel_vel
]
self._world_obj.left_back_radar.detected = False
# Remove radar data if not detected again in 0.5 second
if self._front_r and (pygame.time.get_ticks() - self._front_r[0] >
5000):
self._front_r = []
if self._left_front_r and (
pygame.time.get_ticks() - self._left_front_r[0] > 5000):
self._left_front_r = []
if self._left_back_r and (
pygame.time.get_ticks() - self._left_back_r[0] > 5000):
self._left_back_r = []
# Detect vehicles in front
self._hazard_detected = False
if self._front_r and (self._front_r[1][0] < 20.0):
self._hazard_detected = True
# update hazard existing time
if self._hazard_detected:
if self._blocked_time is None:
self._blocked_time = pygame.time.get_ticks()
hazard_time = 0
else:
hazard_time = pygame.time.get_ticks() - self._blocked_time
else:
self._blocked_time = None
# Get a safe_distance
safe_distance = self._THW * self._get_speed()
try:
i = self.right_positions[0][0]
j = self.right_positions[0][1]
loc_start = i.transform.location
loc_start_yaw = i.transform.rotation.yaw
loc = loc_start
loc_end_yaw = j.transform.rotation.yaw
loc_end = j.transform.location
if (abs(loc.x-self._vehicle.get_location().x)+\
abs(loc.y-self._vehicle.get_location().y)+\
abs(loc.z-self._vehicle.get_location().z))<=10:
self.right_turn = True
self.temp_flag = False
except:
pass
try:
i = self.left_positions[0][0]
j = self.left_positions[0][1]
loc2_start = i.transform.location
loc2_start_yaw = i.transform.rotation.yaw
loc2 = loc2_start
loc2_end = j.transform.location
loc2_end_yaw = j.transform.rotation.yaw
if (abs(loc2.x-self._vehicle.get_location().x)+\
abs(loc2.y-self._vehicle.get_location().y)+\
abs(loc2.z-self._vehicle.get_location().z))<=10:
self.left_turn = True
self.temp_flag = False
except:
pass
# Finite State Machine
# 1, Navigating
if self._state == AgentState.NAVIGATING:
if self._hazard_detected:
self._state = AgentState.BLOCKED_BY_VEHICLE
# 2, Blocked by Vehicle
elif self._state == AgentState.BLOCKED_BY_VEHICLE:
if not self._hazard_detected:
self._state = AgentState.NAVIGATING
# The vehicle is driving at a certain speed
# There is enough space
else:
if hazard_time > 5000 and \
190 > self._vehicle.get_location().x > 10 and \
10 > self._vehicle.get_location().y > 7:
self._state = AgentState.PREPARE_LANE_CHANGING
# 4, Prepare Lane Change
elif self._state == AgentState.PREPARE_LANE_CHANGING:
if not (self._front_r and self._front_r[1][0] < safe_distance) and \
not (self._left_front_r and self._left_front_r[1][0] < safe_distance) and \
not (self._left_back_r and self._left_back_r[1][0] > -10):
self._state = AgentState.LANE_CHANGING
self._perform_lane_change = True
# 5, Lane Change
elif self._state == AgentState.LANE_CHANGING:
if abs(self._vehicle.get_velocity().y) < 0.5 and \
self._vehicle.get_location().y < 7.0:
self._state = AgentState.NAVIGATING
# 6, Emergency Brake
emergency_distance = safe_distance * 3 / 5
emergency_front_speed = 1.0
if self._front_r and (self._front_r[1][0] < emergency_distance
or self._front_r[2][0] < emergency_front_speed):
self._state = AgentState.EMERGENCY_BRAKE
# Local Planner Behavior according to states
if self._state == AgentState.NAVIGATING or self._state == AgentState.LANE_CHANGING:
control = self._local_planner.run_step(debug=debug)
elif self._state == AgentState.PREPARE_LANE_CHANGING:
if self._left_front_r and self._left_front_r[1][0] < safe_distance or \
self._front_r and self._front_r[1][0] < safe_distance:
control = self._local_planner.empty_control(debug=debug)
else:
control = self._local_planner.run_step(debug=debug)
elif self._state == AgentState.BLOCKED_BY_VEHICLE:
# ACC
front_dis = self._front_r[1][0]
front_vel = self._front_r[2][0]
ego_speed = self._get_speed()
desired_speed = front_vel - (ego_speed - front_vel) / front_dis
if ego_speed > 1:
desired_speed += 2 * (front_dis / ego_speed - self._THW)
control = self._local_planner.run_step(debug=debug,
target_speed=desired_speed *
3.6)
elif self._state == AgentState.EMERGENCY_BRAKE:
control = self._local_planner.brake()
if self._front_r:
if self._front_r[1][0] >= emergency_distance and \
self._front_r[2][0] > emergency_front_speed:
self._state = AgentState.NAVIGATING
elif self._state == AgentState.BLOCKED_RED_LIGHT:
control = self._local_planner.empty_control(debug=debug)
# When performing a lane change
if self._perform_lane_change:
# Record original destination
destination = self._local_planner.get_global_destination()
# Get lane change start location
ref_location = self._world_obj.player.get_location()
ref_yaw = self._world_obj.player.get_transform().rotation.yaw
if self._local_planner.waypoint_buffer:
waypoint = self._local_planner.waypoint_buffer[-1][0]
ref_location = waypoint.transform.location
wait_dist = 0.0 # need some time to plan
ref = [ref_location.x + wait_dist, ref_location.y, ref_yaw]
# Replace current plan with a lane change plan
overtake = BezierOverTake(self._world_obj)
overtake_plan = overtake.get_waypoints(ref)
self._local_planner.set_local_plan(overtake_plan)
# replan globally with new vehicle position after lane changing
new_start = self._map.get_waypoint(
overtake_plan[-1][0].transform.location)
route_trace = self._trace_route(new_start, destination)
assert route_trace
self._local_planner.add_global_plan(route_trace)
self._perform_lane_change = False
print("overtake")
if self.right_turn or self.left_turn:
# Record original destination
destination = self._local_planner.get_global_destination()
# Get lane change start location
ref_location = self._world_obj.player.get_location()
ref_yaw = self._world_obj.player.get_transform().rotation.yaw
if self._local_planner.waypoint_buffer:
waypoint = self._local_planner.waypoint_buffer[-1][0]
ref_location = waypoint.transform.location
if self.right_turn:
ref1 = [loc_start.x, loc_start.y, loc_start_yaw]
ref2 = [loc_end.x, loc_end.y, loc_end_yaw]
turner = BezierTurn(self._world_obj, True)
turn_plan = turner.get_waypoints(ref1, ref2)
self.right_turn = False
print('Right Turn')
elif self.left_turn:
ref1 = [loc2_start.x, loc2_start.y, loc2_start_yaw]
ref2 = [loc2_end.x, loc2_end.y, loc2_end_yaw]
turner = BezierTurn(self._world_obj, False)
turn_plan = turner.get_waypoints(ref1, ref2)
self.left_turn = False
print('Left turn')
self._local_planner.set_local_plan(turn_plan)
# replan globally with new vehicle position after lane changing
new_start = self._map.get_waypoint(
turn_plan[-1][0].transform.location)
route_trace = self._trace_route(new_start, destination)
assert route_trace
self._local_planner.add_global_plan(route_trace)
return control
def done(self):
"""
Check whether the agent has reached its destination.
:return bool
"""
return self._local_planner.done()
