-Snippet for carla.World.enable_environment_objects +Snippet for carla.World.unload_map_layer
-
+
-
```py
-# This recipe turn visibility off and on for two specifc buildings on the map
+# This recipe toggles off several layers in our "_Opt" maps
-# Get the buildings in the world
-world = client.get_world()
-env_objs = world.get_environment_objects(carla.CityObjectLabel.Buildings)
+# Load town one with minimum layout (roads, sidewalks, traffic lights and traffic signs)
+# as well as buildings and parked vehicles
+world = client.load_world('Town01_Opt', carla.MapLayer.Buildings | carla.MapLayer.ParkedVehicles)
-# Access individual building IDs and save in a set
-building_01 = env_objs[0]
-building_02 = env_objs[1]
-objects_to_toggle = {building_01.id, building_02.id}
+# Toggle all buildings off
+world.unload_map_layer(carla.MapLayer.Buildings)
-# Toggle buildings off
-world.enable_environment_objects(objects_to_toggle, False)
-# Toggle buildings on
-world.enable_environment_objects(objects_to_toggle, True)
+# Toggle all parked vehicles off
+world.unload_map_layer(carla.MapLayer.ParkedVehicles)
```
-
+
@@ -4257,206 +4259,37 @@ while True:
+
+
-Snippet for carla.World.unload_map_layer +Snippet for carla.ActorBlueprint.set_attribute
-
-
-```py
-
-# This recipe toggles off several layers in our "_Opt" maps
-
-# Load town one with minimum layout (roads, sidewalks, traffic lights and traffic signs)
-# as well as buildings and parked vehicles
-world = client.load_world('Town01_Opt', carla.MapLayer.Buildings | carla.MapLayer.ParkedVehicles)
-
-# Toggle all buildings off
-world.unload_map_layer(carla.MapLayer.Buildings)
-
-# Toggle all parked vehicles off
-world.unload_map_layer(carla.MapLayer.ParkedVehicles)
-
-
-```
-
- -
-
-- -
-
-Snippet for carla.Vehicle.set_wheel_steer_direction -
-
-
-```py
-
-# Sets the appearance of the vehicles front wheels to 40°. Vehicle physics will not be affected.
-
-vehicle.set_wheel_steer_direction(carla.VehicleWheelLocation.FR_Wheel, 40.0)
-vehicle.set_wheel_steer_direction(carla.VehicleWheelLocation.FL_Wheel, 40.0)
-
-
-```
-
- -
-
-- -
-
-Snippet for carla.Client.__init__ -
-
+
```py
-# This recipe shows in every script provided in PythonAPI/Examples
-# and it is used to parse the client creation arguments when running the script.
-
- argparser = argparse.ArgumentParser(
- description=__doc__)
- argparser.add_argument(
- '--host',
- metavar='H',
- default='127.0.0.1',
- help='IP of the host server (default: 127.0.0.1)')
- argparser.add_argument(
- '-p', '--port',
- metavar='P',
- default=2000,
- type=int,
- help='TCP port to listen to (default: 2000)')
- argparser.add_argument(
- '-s', '--speed',
- metavar='FACTOR',
- default=1.0,
- type=float,
- help='rate at which the weather changes (default: 1.0)')
- args = argparser.parse_args()
-
- speed_factor = args.speed
- update_freq = 0.1 / speed_factor
-
- client = carla.Client(args.host, args.port)
-
-
-
-```
-
- -
-
-- -
-
-Snippet for carla.Map.get_waypoint -
-
-
-```py
-
-
-# This recipe shows the current traffic rules affecting the vehicle.
-# Shows the current lane type and if a lane change can be done in the actual lane or the surrounding ones.
+# This recipe changes attributes of different type of blueprint actors.
# ...
-waypoint = world.get_map().get_waypoint(vehicle.get_location(),project_to_road=True, lane_type=(carla.LaneType.Driving | carla.LaneType.Shoulder | carla.LaneType.Sidewalk))
-print("Current lane type: " + str(waypoint.lane_type))
-# Check current lane change allowed
-print("Current Lane change: " + str(waypoint.lane_change))
-# Left and Right lane markings
-print("L lane marking type: " + str(waypoint.left_lane_marking.type))
-print("L lane marking change: " + str(waypoint.left_lane_marking.lane_change))
-print("R lane marking type: " + str(waypoint.right_lane_marking.type))
-print("R lane marking change: " + str(waypoint.right_lane_marking.lane_change))
+walker_bp = world.get_blueprint_library().filter('walker.pedestrian.0002')
+walker_bp.set_attribute('is_invincible', True)
+
+# ...
+# Changes attribute randomly by the recommended value
+vehicle_bp = wolrd.get_blueprint_library().filter('vehicle.bmw.*')
+color = random.choice(vehicle_bp.get_attribute('color').recommended_values)
+vehicle_bp.set_attribute('color', color)
+
+# ...
+
+camera_bp = world.get_blueprint_library().filter('sensor.camera.rgb')
+camera_bp.set_attribute('image_size_x', 600)
+camera_bp.set_attribute('image_size_y', 600)
# ...
```
-
- - -
-
-
-
-- - -
-
-
-
-Snippet for carla.World.spawn_actor -
-
-
-```py
-
-
-# This recipe attaches different camera / sensors to a vehicle with different attachments.
-
-# ...
-camera = world.spawn_actor(rgb_camera_bp, transform, attach_to=vehicle, attachment_type=Attachment.Rigid)
-# Default attachment: Attachment.Rigid
-gnss_sensor = world.spawn_actor(sensor_gnss_bp, transform, attach_to=vehicle)
-collision_sensor = world.spawn_actor(sensor_collision_bp, transform, attach_to=vehicle)
-lane_invasion_sensor = world.spawn_actor(sensor_lane_invasion_bp, transform, attach_to=vehicle)
-# ...
-
-
-```
-
- -
-
-- -
-
-Snippet for carla.WalkerAIController.stop -
-
-
-```py
-
-
-#To destroy the pedestrians, stop them from the navigation, and then destroy the objects (actor and controller).
-
-# stop pedestrians (list is [controller, actor, controller, actor ...])
-for i in range(0, len(all_id), 2):
- all_actors[i].stop()
-
-# destroy pedestrian (actor and controller)
-client.apply_batch([carla.command.DestroyActor(x) for x in all_id])
-
-
-```
-
- -
-
-- -
-
+-Snippet for carla.DebugHelper.draw_box -
-
-
-```py
-
-
-# This recipe shows how to draw traffic light actor bounding boxes from a world snapshot.
-
-# ....
-debug = world.debug
-world_snapshot = world.get_snapshot()
-
-for actor_snapshot in world_snapshot:
- actual_actor = world.get_actor(actor_snapshot.id)
- if actual_actor.type_id == 'traffic.traffic_light':
- debug.draw_box(carla.BoundingBox(actor_snapshot.get_transform().location,carla.Vector3D(0.5,0.5,2)),actor_snapshot.get_transform().rotation, 0.05, carla.Color(255,0,0,0),0)
-# ...
-
-
-
-```
-
- - -
+
@@ -4520,6 +4353,67 @@ camera.listen(lambda image: image.save_to_disk('output/%06d.png' % image.frame,
- - -
+
+
+Snippet for carla.World.load_map_layer +
+
+
+```py
+
+# This recipe toggles on several layers in our "_Opt" maps
+
+# Load town one with only minimum layout (roads, sidewalks, traffic lights and traffic signs)
+world = client.load_world('Town01_Opt', carla.MapLayer.None)
+
+# Toggle all buildings on
+world.load_map_layer(carla.MapLayer.Buildings)
+
+# Toggle all foliage on
+world.load_map_layer(carla.MapLayer.Foliage)
+
+# Toggle all parked vehicles on
+world.load_map_layer(carla.MapLayer.ParkedVehicles)
+
+
+```
+
+ +
+
++ +
+
-
+Snippet for carla.Map.get_waypoint +
+
+
+```py
+
+
+# This recipe shows the current traffic rules affecting the vehicle.
+# Shows the current lane type and if a lane change can be done in the actual lane or the surrounding ones.
+
+# ...
+waypoint = world.get_map().get_waypoint(vehicle.get_location(),project_to_road=True, lane_type=(carla.LaneType.Driving | carla.LaneType.Shoulder | carla.LaneType.Sidewalk))
+print("Current lane type: " + str(waypoint.lane_type))
+# Check current lane change allowed
+print("Current Lane change: " + str(waypoint.lane_change))
+# Left and Right lane markings
+print("L lane marking type: " + str(waypoint.left_lane_marking.type))
+print("L lane marking change: " + str(waypoint.left_lane_marking.lane_change))
+print("R lane marking type: " + str(waypoint.right_lane_marking.type))
+print("R lane marking change: " + str(waypoint.right_lane_marking.lane_change))
+# ...
+
+
+```
+
+ + +
+
+
+
+ + +
+
+Snippet for carla.TrafficLight.set_state @@ -4566,37 +4460,77 @@ if vehicle_actor.is_at_traffic_light():
+
-Snippet for carla.ActorBlueprint.set_attribute +Snippet for carla.Vehicle.set_wheel_steer_direction
-
+
+
+```py
+
+# Sets the appearance of the vehicles front wheels to 40°. Vehicle physics will not be affected.
+
+vehicle.set_wheel_steer_direction(carla.VehicleWheelLocation.FR_Wheel, 40.0)
+vehicle.set_wheel_steer_direction(carla.VehicleWheelLocation.FL_Wheel, 40.0)
+
+
+```
+
+ +
+
++ +
+
+Snippet for carla.DebugHelper.draw_box +
+
```py
-# This recipe changes attributes of different type of blueprint actors.
+# This recipe shows how to draw traffic light actor bounding boxes from a world snapshot.
-# ...
-walker_bp = world.get_blueprint_library().filter('walker.pedestrian.0002')
-walker_bp.set_attribute('is_invincible', True)
-
-# ...
-# Changes attribute randomly by the recommended value
-vehicle_bp = wolrd.get_blueprint_library().filter('vehicle.bmw.*')
-color = random.choice(vehicle_bp.get_attribute('color').recommended_values)
-vehicle_bp.set_attribute('color', color)
+# ....
+debug = world.debug
+world_snapshot = world.get_snapshot()
+for actor_snapshot in world_snapshot:
+ actual_actor = world.get_actor(actor_snapshot.id)
+ if actual_actor.type_id == 'traffic.traffic_light':
+ debug.draw_box(carla.BoundingBox(actor_snapshot.get_transform().location,carla.Vector3D(0.5,0.5,2)),actor_snapshot.get_transform().rotation, 0.05, carla.Color(255,0,0,0),0)
# ...
-camera_bp = world.get_blueprint_library().filter('sensor.camera.rgb')
-camera_bp.set_attribute('image_size_x', 600)
-camera_bp.set_attribute('image_size_y', 600)
-# ...
```
-
+
+ + +
+
+
+
++
+ + +
+
+
+
-+Snippet for carla.WalkerAIController.stop +
+
+
+```py
+
+
+#To destroy the pedestrians, stop them from the navigation, and then destroy the objects (actor and controller).
+
+# stop pedestrians (list is [controller, actor, controller, actor ...])
+for i in range(0, len(all_id), 2):
+ all_actors[i].stop()
+
+# destroy pedestrian (actor and controller)
+client.apply_batch([carla.command.DestroyActor(x) for x in all_id])
+
+
+```
+
@@ -4673,31 +4607,103 @@ for i in range(0, len(all_actors), 2):
+
-Snippet for carla.World.load_map_layer +Snippet for carla.World.enable_environment_objects
-
+
```py
-# This recipe toggles on several layers in our "_Opt" maps
+# This recipe turn visibility off and on for two specifc buildings on the map
-# Load town one with only minimum layout (roads, sidewalks, traffic lights and traffic signs)
-world = client.load_world('Town01_Opt', carla.MapLayer.None)
+# Get the buildings in the world
+world = client.get_world()
+env_objs = world.get_environment_objects(carla.CityObjectLabel.Buildings)
-# Toggle all buildings on
-world.load_map_layer(carla.MapLayer.Buildings)
+# Access individual building IDs and save in a set
+building_01 = env_objs[0]
+building_02 = env_objs[1]
+objects_to_toggle = {building_01.id, building_02.id}
-# Toggle all foliage on
-world.load_map_layer(carla.MapLayer.Foliage)
-
-# Toggle all parked vehicles on
-world.load_map_layer(carla.MapLayer.ParkedVehicles)
+# Toggle buildings off
+world.enable_environment_objects(objects_to_toggle, False)
+# Toggle buildings on
+world.enable_environment_objects(objects_to_toggle, True)
```
-
+
+ +
+
++
+ +
+
+Snippet for carla.Client.__init__ +
+
+
+```py
+
+
+# This recipe shows in every script provided in PythonAPI/Examples
+# and it is used to parse the client creation arguments when running the script.
+
+ argparser = argparse.ArgumentParser(
+ description=__doc__)
+ argparser.add_argument(
+ '--host',
+ metavar='H',
+ default='127.0.0.1',
+ help='IP of the host server (default: 127.0.0.1)')
+ argparser.add_argument(
+ '-p', '--port',
+ metavar='P',
+ default=2000,
+ type=int,
+ help='TCP port to listen to (default: 2000)')
+ argparser.add_argument(
+ '-s', '--speed',
+ metavar='FACTOR',
+ default=1.0,
+ type=float,
+ help='rate at which the weather changes (default: 1.0)')
+ args = argparser.parse_args()
+
+ speed_factor = args.speed
+ update_freq = 0.1 / speed_factor
+
+ client = carla.Client(args.host, args.port)
+
+
+
+```
+
+ +
+
++ +
+
+Snippet for carla.World.spawn_actor +
+
+
+```py
+
+
+# This recipe attaches different camera / sensors to a vehicle with different attachments.
+
+# ...
+camera = world.spawn_actor(rgb_camera_bp, transform, attach_to=vehicle, attachment_type=Attachment.Rigid)
+# Default attachment: Attachment.Rigid
+gnss_sensor = world.spawn_actor(sensor_gnss_bp, transform, attach_to=vehicle)
+collision_sensor = world.spawn_actor(sensor_collision_bp, transform, attach_to=vehicle)
+lane_invasion_sensor = world.spawn_actor(sensor_lane_invasion_bp, transform, attach_to=vehicle)
+# ...
+
+
+```
+
@@ -4737,4 +4743,4 @@ for (let i = 0; i < buttons.length; i++) {
buttons[i].addEventListener("click",function(){ButtonAction(buttons[i].id);},true);
}
window.onresize = WindowResize;
-
+
\ No newline at end of file
diff --git a/Docs/start_introduction.md b/Docs/start_introduction.md
index 5ba1ab1ba..afccf4b85 100644
--- a/Docs/start_introduction.md
+++ b/Docs/start_introduction.md
@@ -6,7 +6,7 @@
This document refers to the latest version of CARLA. For documentation of previous versions, select the required version in the bottom right hand corner where you see this button: 
-CARLA is an open-source autonomous driving simulator. It was built from scratch to serve as a modular and flexible API to address a range of tasks involved in the problem of autonomous driving. One of the main goals of CARLA is to help democratize autonomous driving R&D, serving as a tool that can be easily accessed and customized by users. To do so, the simulator has to meet the requirements of different use cases within the general problem of driving (e.g. learning driving policies, training perception algorithms, etc.). CARLA is grounded on Unreal Engine to run the simulation and uses the OpenDRIVE standard (1.4 as today) to define roads and urban settings. Control over the simulation is granted through an API handled in Python and C++ that is constantly growing as the project does.
+CARLA is an open-source autonomous driving simulator. It was built from scratch to serve as a modular and flexible API to address a range of tasks involved in the problem of autonomous driving. One of the main goals of CARLA is to help democratize autonomous driving R&D, serving as a tool that can be easily accessed and customized by users. To do so, the simulator has to meet the requirements of different use cases within the general problem of driving (e.g. learning driving policies, training perception algorithms, etc.). CARLA is grounded on Unreal Engine to run the simulation and uses the ASAM OpenDRIVE standard (1.4 as today) to define roads and urban settings. Control over the simulation is granted through an API handled in Python and C++ that is constantly growing as the project does.
In order to smooth the process of developing, training and validating driving systems, CARLA evolved to become an ecosystem of projects, built around the main platform by the community. In this context, it is important to understand some things about how does CARLA work, so as to fully comprehend its capabilities.
diff --git a/PythonAPI/docs/client.yml b/PythonAPI/docs/client.yml
index 71c076bee..45adf1a5c 100644
--- a/PythonAPI/docs/client.yml
+++ b/PythonAPI/docs/client.yml
@@ -65,7 +65,7 @@
- param_name: opendrive
type: str
doc: >
- Content of an OpenDRIVE file as `string`, __not the path to the `.xodr`__.
+ Content of an ASAM OpenDRIVE file as `string`, __not the path to the `.xodr`__.
- param_name: parameters
type: carla.OpendriveGenerationParameters
default: (2.0, 50.0, 1.0, 0.6, true, true)
@@ -732,7 +732,7 @@
- class_name: OpendriveGenerationParameters
# - DESCRIPTION ------------------------
doc: >
- This class defines the parameters used when generating a world using an OpenDRIVE file.
+ This class defines the parameters used when generating a world using an ASAM OpenDRIVE file.
# - PROPERTIES -------------------------
instance_variables:
- var_name: vertex_distance
diff --git a/README.md b/README.md
index 0ab8fc1a1..8589994b0 100644
--- a/README.md
+++ b/README.md
@@ -54,7 +54,7 @@ Repositories associated with the CARLA simulation platform:
* [**Conditional Imitation-Learning**](https://github.com/felipecode/coiltraine): Training and testing Conditional Imitation Learning models in CARLA
* [**AutoWare AV stack**](https://github.com/carla-simulator/carla-autoware): Bridge to connect AutoWare AV stack to CARLA
* [**Reinforcement-Learning**](https://github.com/carla-simulator/reinforcement-learning): Code for running Conditional Reinforcement Learning models in CARLA
-* [**RoadRunner**](https://www.mathworks.com/products/roadrunner.html): MATLAB GUI based application to create road networks in OpenDrive format
+* [**RoadRunner**](https://www.mathworks.com/products/roadrunner.html): MATLAB GUI based application to create road networks in the ASAM OpenDRIVE format
* [**Map Editor**](https://github.com/carla-simulator/carla-map-editor): Standalone GUI application to enhance RoadRunner maps with traffic lights and traffic signs information
diff --git a/mkdocs.yml b/mkdocs.yml
index 40a876c9e..c2a5aae00 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -42,7 +42,7 @@ nav:
- 'Chrono': 'tuto_G_chrono.md'
- 'MathWorks': 'large_map_roadrunner.md'
- 'NVIDIA SimReady': 'ecosys_simready.md'
- - 'OpenDRIVE': 'adv_opendrive.md'
+ - 'ASAM OpenDRIVE': 'adv_opendrive.md'
- 'PTV Vissim': 'adv_ptv.md'
- 'RSS': 'adv_rss.md'
- 'ROS': https://carla.readthedocs.io/projects/ros-bridge/en/latest/