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maneuver_opposite_direction.py
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#!/usr/bin/env python
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
Vehicle Maneuvering In Opposite Direction:
Vehicle is passing another vehicle in a rural area, in daylight, under clear
weather conditions, at a non-junction and encroaches into another
vehicle traveling in the opposite direction.
"""
from six.moves.queue import Queue # pylint: disable=relative-import,bad-option-value
import math # pylint: disable=wrong-import-order
import py_trees
import carla
from srunner.scenariomanager.carla_data_provider import CarlaDataProvider
from srunner.scenariomanager.scenarioatomics.atomic_behaviors import (ActorTransformSetter,
ActorDestroy,
ActorSource,
ActorSink,
WaypointFollower)
from srunner.scenariomanager.scenarioatomics.atomic_criteria import CollisionTest
from srunner.scenariomanager.scenarioatomics.atomic_trigger_conditions import DriveDistance
from srunner.scenarios.basic_scenario import BasicScenario
from srunner.tools.scenario_helper import get_waypoint_in_distance
class ManeuverOppositeDirection(BasicScenario):
"""
"Vehicle Maneuvering In Opposite Direction" (Traffic Scenario 06)
This is a single ego vehicle scenario
"""
def __init__(self, world, ego_vehicles, config, randomize=False, debug_mode=False, criteria_enable=True,
obstacle_type='barrier', timeout=120):
"""
Setup all relevant parameters and create scenario
obstacle_type -> flag to select type of leading obstacle. Values: vehicle, barrier
"""
self._world = world
self._map = CarlaDataProvider.get_map()
self._first_vehicle_location = 50
self._second_vehicle_location = self._first_vehicle_location + 60
self._ego_vehicle_drive_distance = self._second_vehicle_location * 2
self._start_distance = self._first_vehicle_location * 0.9
self._opposite_speed = 5.56 # m/s
self._source_gap = 40 # m
self._reference_waypoint = self._map.get_waypoint(config.trigger_points[0].location)
self._source_transform = None
self._sink_location = None
self._blackboard_queue_name = 'ManeuverOppositeDirection/actor_flow_queue'
self._queue = py_trees.blackboard.Blackboard().set(self._blackboard_queue_name, Queue())
self._obstacle_type = obstacle_type
self._first_actor_transform = None
self._second_actor_transform = None
self._third_actor_transform = None
# Timeout of scenario in seconds
self.timeout = timeout
super(ManeuverOppositeDirection, self).__init__(
"ManeuverOppositeDirection",
ego_vehicles,
config,
world,
debug_mode,
criteria_enable=criteria_enable)
def _initialize_actors(self, config):
"""
Custom initialization
"""
first_actor_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._first_vehicle_location)
second_actor_waypoint, _ = get_waypoint_in_distance(self._reference_waypoint, self._second_vehicle_location)
second_actor_waypoint = second_actor_waypoint.get_left_lane()
first_actor_transform = carla.Transform(
first_actor_waypoint.transform.location,
first_actor_waypoint.transform.rotation)
if self._obstacle_type == 'vehicle':
first_actor_model = 'vehicle.nissan.micra'
else:
first_actor_transform.rotation.yaw += 90
first_actor_model = 'static.prop.streetbarrier'
second_prop_waypoint = first_actor_waypoint.next(2.0)[0]
position_yaw = second_prop_waypoint.transform.rotation.yaw + 90
offset_location = carla.Location(
0.50 * second_prop_waypoint.lane_width * math.cos(math.radians(position_yaw)),
0.50 * second_prop_waypoint.lane_width * math.sin(math.radians(position_yaw)))
second_prop_transform = carla.Transform(
second_prop_waypoint.transform.location + offset_location, first_actor_transform.rotation)
second_prop_actor = CarlaDataProvider.request_new_actor(first_actor_model, second_prop_transform)
second_prop_actor.set_simulate_physics(True)
first_actor = CarlaDataProvider.request_new_actor(first_actor_model, first_actor_transform)
first_actor.set_simulate_physics(True)
second_actor = CarlaDataProvider.request_new_actor('vehicle.audi.tt', second_actor_waypoint.transform)
self.other_actors.append(first_actor)
self.other_actors.append(second_actor)
if self._obstacle_type != 'vehicle':
self.other_actors.append(second_prop_actor)
self._source_transform = second_actor_waypoint.transform
sink_waypoint = second_actor_waypoint.next(1)[0]
while not sink_waypoint.is_junction:
sink_waypoint = sink_waypoint.next(1)[0]
self._sink_location = sink_waypoint.transform.location
self._first_actor_transform = first_actor_transform
self._second_actor_transform = second_actor_waypoint.transform
self._third_actor_transform = second_prop_transform
def _create_behavior(self):
"""
The behavior tree returned by this method is as follows:
The ego vehicle is trying to pass a leading vehicle in the same lane
by moving onto the oncoming lane while another vehicle is moving in the
opposite direction in the oncoming lane.
"""
# Leaf nodes
actor_source = ActorSource(
['vehicle.audi.tt', 'vehicle.tesla.model3', 'vehicle.nissan.micra'],
self._source_transform, self._source_gap, self._blackboard_queue_name)
actor_sink = ActorSink(self._sink_location, 10)
ego_drive_distance = DriveDistance(self.ego_vehicles[0], self._ego_vehicle_drive_distance)
waypoint_follower = WaypointFollower(
self.other_actors[1], self._opposite_speed,
blackboard_queue_name=self._blackboard_queue_name, avoid_collision=True)
# Non-leaf nodes
parallel_root = py_trees.composites.Parallel(policy=py_trees.common.ParallelPolicy.SUCCESS_ON_ONE)
# Building tree
parallel_root.add_child(ego_drive_distance)
parallel_root.add_child(actor_source)
parallel_root.add_child(actor_sink)
parallel_root.add_child(waypoint_follower)
scenario_sequence = py_trees.composites.Sequence()
scenario_sequence.add_child(ActorTransformSetter(self.other_actors[0], self._first_actor_transform))
scenario_sequence.add_child(ActorTransformSetter(self.other_actors[1], self._second_actor_transform))
scenario_sequence.add_child(ActorTransformSetter(self.other_actors[2], self._third_actor_transform))
scenario_sequence.add_child(parallel_root)
scenario_sequence.add_child(ActorDestroy(self.other_actors[0]))
scenario_sequence.add_child(ActorDestroy(self.other_actors[1]))
scenario_sequence.add_child(ActorDestroy(self.other_actors[2]))
return scenario_sequence
def _create_test_criteria(self):
"""
A list of all test criteria will be created that is later used
in parallel behavior tree.
"""
criteria = []
collision_criterion = CollisionTest(self.ego_vehicles[0])
criteria.append(collision_criterion)
return criteria
def __del__(self):
"""
Remove all actors upon deletion
"""
self.remove_all_actors()