Actors not only include vehicles and walkers, but also sensors, traffic signs, traffic lights, and the spectator. It is crucial to have fully understanding on how to operate on them.
This section will cover spawning, destruction, types, and how to manage them. However, the possibilities are almost endless. Experiment, take a look at the __tutorials__ in this documentation and share doubts and ideas in the [CARLA forum](https://forum.carla.org/).
These layouts allow the user to smoothly incorporate new actors into the simulation. They are already-made models with animations and a series of attributes. Some of these are modifiable and others are not. These attributes include, among others, vehicle color, amount of channels in a lidar sensor, a walker's speed, and much more.
Available blueprints are listed in the [blueprint library](bp_library.md), along with their attributes.
The [carla.BlueprintLibrary](python_api.md#carla.BlueprintLibrary) class contains a list of [carla.ActorBlueprint](python_api.md#carla.ActorBlueprint) elements. It is the world object who can provide access to it.
Blueprints have an ID to identify them and the actors spawned with it. The library can be read to find a certain ID, choose a blueprint at random, or filter results using a [wildcard pattern](https://tldp.org/LDP/GNU-Linux-Tools-Summary/html/x11655.htm).
Besides that, each [carla.ActorBlueprint](python_api.md#carla.ActorBlueprint) has a series of [carla.ActorAttribute](python_api.md#carla.ActorAttribute) that can be _get_ and _set_.
Attributes have an [carla.ActorAttributeType](python_api.md#carla.ActorAttributeType) variable. It states its type from a list of enums. Also, modifiable attributes come with a __list of recommended values__.
Users can create their own vehicles. Check the __Tutorials (assets)__ to learn on that. Contributors can [add their new content to CARLA](tuto_D_contribute_assets.md).
This section mentions different methods regarding actors. The Python API provides for __[commands](python_api.md#command.SpawnActor)__ to apply batches of the most common ones, in just one frame.
__The world object is responsible of spawning actors and keeping track of these.__ Spawning only requires a blueprint, and a [carla.Transform](python_api.md#carla.Transform) stating a location and rotation for the actor.
The actor will not be spawned in case of collision at the specified location. No matter if this happens with a static object or another actor. It is possible to try avoiding these undesired spawning collisions.
An actor can be attached to another one when spawned. Actors follow the parent they are attached to. This is specially useful for sensors. The attachment can be rigid or eased. It is defined by the helper class [carla.AttachmentType](python_api.md#carla.AttachmentType).
Most of the methods send requests to the simulator asynchronously. The simulator has a limited amount of time each update to parse them. Flooding the simulator with _set()_ methods will accumulate a significant lag.
Sensors are actors that produce a stream of data. They have their own section, [4th. Sensors and data](core_sensors.md). For now, let's just take a look at a common sensor spawning cycle.
This example spawns a camera sensor, attaches it to a vehicle, and tells the camera to save the images generated to disk.
* Sensors __listen__ to data. When data is received, they call a function described with a __[Lambda expression](https://docs.python.org/3/reference/expressions.html)__ <small>(6.13 in the link provided)</small>.
Placed by Unreal Engine to provide an in-game point of view. It can be used to move the view of the simulator window. The following example would move the spectator actor, to point the view towards a desired vehicle.
Only stops, yields and traffic lights are considered actors in CARLA so far. The rest of the OpenDRIVE signs are accessible from the API as [__carla.Landmark__](python_api.md#carla.Landmark). Their information is accessible using these instances, but they do no exist in the simulation as actors. Landmarks are explained more in detail in the following step, __3rd. Maps and navigation__.
When the simulation starts, stop, yields and traffic light are automatically generated using the information in the OpenDRIVE file. __None of these can be found in the blueprint library__ and thus, cannot be spawned.
!!! Note
CARLA maps do not have traffic signs nor lights in the OpenDRIVE file. These are manually placed by developers.
[__Traffic signs__](python_api.md#carla.TrafficSign) are not defined in the road map itself, as explained in the following page. Instead, they have a [carla.BoundingBox](python_api.md#carla.BoundingBox) to affect vehicles inside of it.
[__Traffic lights__](python_api.md#carla.TrafficLight) are found in junctions. They have their unique ID, as any actor, but also a `group` ID for the junction. To identify the traffic lights in the same group, a `pole` ID is used.
The traffic lights in the same group follow a cycle. The first one is set to green while the rest remain frozen in red. The active one spends a few seconds in green, yellow and red, so there is a period of time where all the lights are red. Then, the next traffic light starts its cycle, and the previous one is frozen with the rest.
The state of a traffic light can be set using the API. So does the seconds spent on each state. Possible states are described with [carla.TrafficLightState](python_api.md#carla.TrafficLightState) as a series of enum values.
[__carla.Vehicle__](python_api.md#carla.Vehicle) are a special type of actor. They are remarkable for having better physics. This is achieved applying four types of different controls.
* __[carla.VehiclePhysicsControl](python_api.md#carla.VehiclePhysicsControl)__ defines physical attributes of the vehicle. Besides many different attribute, this controller contains two more controllers. [carla.GearPhysicsControl](python_api.md#carla.GearPhysicsControl) for the gears. The other is a list of [carla.WheelPhysicsControl](python_api.md#carla.WheelPhysicsControl), that provide specific control over the different wheels.
Vehicles include other functionalities unique to them.
* The __autopilot mode__ will subscribe them to the [Traffic manager](adv_traffic_manager.md), and simulate real urban conditions. This module is hard-coded, not based on machine learning.
* __Vehicle lights__ have to be turned on/off by the user. Each vehicle has a set of lights listed in [__carla.VehicleLightState__](python_api.md#carla.VehicleLightState). So far, not all vehicles have lights integrated. Here is a list of those that are available by the time of writing.
*__Bikes.__ All of them have a front and back position light.
*__Motorcycles.__ Yamaha and Harley Davidson models.
*__Cars.__ Audi TT, Chevrolet, Dodge (the police car), Etron, Lincoln, Mustang, Tesla 3S, Wolkswagen T2 and the new guests coming to CARLA.
The lights of a vehicle can be retrieved and updated anytime using the methods [carla.Vehicle.get_light_state](python_api.md#carla.Vehicle.get_light_state) and [carla.Vehicle.set_light_state](#python_api.md#carla.Vehicle.set_light_state). These use binary operations to customize the light setting.
* [__carla.WalkerControl__](python_api.md#carla.WalkerControl) moves the pedestrian around with a certain direction and speed. It also allows them to jump.
* [__carla.WalkerBoneControl__](python_api.md#carla.WalkerBoneControl) provides control over the 3D skeleton. [This tutorial](tuto_G_control_walker_skeletons.md) explains how to control it.
Walkers can be AI controlled. They do not have an autopilot mode. The [__carla.WalkerAIController__](python_api.md#carla.WalkerAIController) actor moves around the actor it is attached to.
The AI controller is bodiless and has no physics. It will not appear on scene. Also, location `(0,0,0)` relative to its parent will not cause a collision.
__Each AI controller needs initialization, a goal and, optionally, a speed__. Stopping the controller works in the same manner.
When a walker reaches the target location, they will automatically walk to another random point. If the target point is not reachable, walkers will go to the closest point from their current location.
