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autoware_carla_interface#

ROS 2 / Autoware Universe bridge for CARLA simulator#

Thanks to https://github.com/gezp for ROS 2 Humble support for CARLA Communication. This ros package enables communication between Autoware and CARLA for autonomous driving simulation.

Supported Environment#

ubuntu ros carla autoware
22.04 humble 0.9.15 Main

Setup#

Install#

Prerequisites#

  1. Install CARLA 0.9.15: Follow the CARLA Installation Guide

  2. Install CARLA Python Package: Install CARLA 0.9.15 ROS 2 Humble communication package

    • Option A: Install the wheel using pip
    • Option B: Add the egg file to your PYTHONPATH

  3. Download CARLA Lanelet2 Maps: Get the y-axis inverted maps from CARLA Autoware Contents

Map Setup#

  1. Download the maps (y-axis inverted version) to an arbitrary location
  2. Create the map folder structure in $HOME/autoware_data/maps:
    • Rename point_cloud/Town01.pcd → $HOME/autoware_data/maps/Town01/pointcloud_map.pcd
    • Rename vector_maps/lanelet2/Town01.osm → $HOME/autoware_data/maps/Town01/lanelet2_map.osm

  3. Create $HOME/autoware_data/maps/Town01/map_projector_info.yaml with:

    yaml projector_type: Local

Build#

bash colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release

Run#

  1. Run carla, change map, spawn object if you need

    bash cd CARLA ./CarlaUE4.sh -prefernvidia -quality-level=Low -RenderOffScreen

  2. Run Autoware with CARLA

    bash ros2 launch autoware_launch e2e_simulator.launch.xml \ map_path:=$HOME/autoware_data/maps/Town01 \ vehicle_model:=sample_vehicle \ sensor_model:=carla_sensor_kit \ simulator_type:=carla

    For E2E planning with VAD:

    bash ros2 launch autoware_launch e2e_simulator.launch.xml \ map_path:=$HOME/autoware_data/maps/Town01 \ vehicle_model:=sample_vehicle \ sensor_model:=carla_sensor_kit \ simulator_type:=carla \ use_e2e_planning:=true

  3. Set initial pose (Init by GNSS)

  4. Set goal position
  5. Wait for planning
  6. Engage

Viewing Multi-Camera View in RViz#

The carla_sensor_kit includes 6 cameras providing 360-degree coverage (Front, Front-Left, Front-Right, Back, Back-Left, Back-Right). A multi-camera combiner node automatically combines all camera feeds into a single 2x3 grid view.

To view the combined camera feed in RViz:

  1. In the Displays panel (left side), click the "Add" button
  2. Select the "By topic" tab
  3. Navigate to /sensing/camera/all_cameras/image_raw
  4. Select "Image" display type
  5. Click OK

Multi-Camera View in RViz

The combined view shows all 6 cameras with labels: FL (Front-Left), F (Front), FR (Front-Right), BL (Back-Left), B (Back), BR (Back-Right).

Note: If you don't need the multi-camera combiner (to save CPU resources), you can comment out the following line in launch/autoware_carla_interface.launch.xml:

```xml

```

Following the Ego Vehicle with the CARLA Spectator Camera#

The spectator_follow script locks the CARLA spectator (free) camera to the ego vehicle, so the in-simulator view chases the car automatically instead of having to pan manually. It connects to the running CARLA server, looks up the ego actor by its role_name, and updates the spectator transform at a fixed rate.

Run it in a separate terminal while CARLA and the Autoware bridge are running:

bash ros2 run autoware_carla_interface spectator_follow

Common options (all optional):

Flag Default Description
--host localhost CARLA server host
--port 2000 CARLA server RPC port
--role ego_vehicle role_name attribute of the ego actor to follow
--distance 8.0 Meters behind the ego vehicle (use 0 for a top-down view)
--height 4.0 Meters above the ego vehicle
--pitch -15.0 Camera pitch in degrees (negative looks down)
--rate 30.0 Update rate in Hz

For a top-down view directly above the ego vehicle:

bash ros2 run autoware_carla_interface spectator_follow --distance 0 --height 30 --pitch -90

Press Ctrl+C to stop the script. It will keep retrying if the ego actor is not yet spawned, and re-acquire it if it is removed and respawned.

Inner-workings / Algorithms#

The InitializeInterface class is key to setting up both the CARLA world and the ego vehicle. It fetches configuration parameters through the autoware_carla_interface.launch.xml.

The main simulation loop runs within the carla_ros2_interface class. This loop ticks simulation time inside the CARLA simulator at fixed_delta_seconds time, where data is received and published as ROS 2 messages at frequencies defined in self.sensor_frequencies.

Ego vehicle commands from Autoware are processed through the autoware_raw_vehicle_cmd_converter, which calibrates these commands for CARLA. The calibrated commands are then fed directly into CARLA control via CarlaDataProvider.

Configurable Parameters for World Loading#

All the key parameters can be configured in autoware_carla_interface.launch.xml.

Name Type Default Value Description
host string "localhost" Hostname for the CARLA server
port int "2000" Port number for the CARLA server
timeout int 20 Timeout for the CARLA client
ego_vehicle_role_name string "ego_vehicle" Role name for the ego vehicle
vehicle_type string "vehicle.toyota.prius" Blueprint ID of the vehicle to spawn. The Blueprint ID of vehicles can be found in CARLA Blueprint ID
spawn_point string None Coordinates for spawning the ego vehicle (None is random). Format = [x, y, z, roll, pitch, yaw]
sync_mode bool True Boolean flag to set synchronous mode in CARLA
fixed_delta_seconds double 0.05 Time step for the simulation (related to client FPS)
use_traffic_manager bool False Boolean flag to set traffic manager in CARLA
max_real_delta_seconds double 0.05 Parameter to limit the simulation speed below fixed_delta_seconds
sensor_kit_name string "carla_sensor_kit_description" Name of the sensor kit package to use for sensor configuration. Should be the *_description package containing config/sensor_kit_calibration.yaml
use_light_weight_sensor_mapping bool False If True, uses sensor_mapping_light_weight.yaml instead of the default sensor_mapping.yaml to reduce simulator load. See Sensor Mapping (CARLA-specific) for details.
sensor_mapping_file string "$(find-pkg-share autoware_carla_interface)/config/sensor_mapping.yaml" Path to sensor mapping YAML configuration file. When use_light_weight_sensor_mapping is True, this defaults to config/sensor_mapping_light_weight.yaml.
config_file string "$(find-pkg-share autoware_carla_interface)/raw_vehicle_cmd_converter.param.yaml" Control mapping file to be used in autoware_raw_vehicle_cmd_converter. Current control are calibrated based on vehicle.toyota.prius Blueprints ID in CARLA. Changing the vehicle type may need a recalibration.

Sensor Configuration#

The interface uses the carla_sensor_kit which provides 6 cameras for 360-degree coverage, LiDAR, IMU, and GNSS sensors. Sensor configurations are dynamically loaded from Autoware sensor kit calibration files through two configuration files:

1. Sensor Kit Calibration (from Autoware sensor kit)#

Located in <sensor_kit_name>_description/config/sensor_kit_calibration.yaml

Defines sensor positions and orientations relative to base_link (rear axle center). Example:

yaml sensor_kit_base_link: CAM_FRONT/camera_link: x: 2.225 y: 0.000 z: 1.600 roll: 0.000 pitch: 0.000 yaw: 0.000 # Angles in radians

2. Sensor Mapping (CARLA-specific)#

Located in config/sensor_mapping.yaml

Maps Autoware sensors to CARLA sensor types and parameters. Key sections:

  • default_sensor_kit_name: Default sensor kit to use (e.g., carla_sensor_kit_description)
  • sensor_mappings: Maps each sensor to CARLA type and ROS topics
  • enabled_sensors: List of sensors to spawn in CARLA
  • vehicle_config (optional): Vehicle parameters like wheelbase

Example sensor mapping:

yaml sensor_mappings: CAM_FRONT/camera_link: carla_type: sensor.camera.rgb id: CAM_FRONT ros_config: frame_id: CAM_FRONT/camera_optical_link topic_image: /sensing/camera/CAM_FRONT/image_raw topic_info: /sensing/camera/CAM_FRONT/camera_info frequency_hz: 11 qos_profile: reliable parameters: image_size_x: 1600 image_size_y: 900 fov: 70.0

For CARLA sensor parameters, see CARLA Sensor Reference.

Light-Weight Sensor Mapping#

For machines with limited GPU/CPU resources, an alternative config/sensor_mapping_light_weight.yaml is provided to reduce simulator load. Compared to the default mapping, it:

  • Uses a single front camera (CAM_FRONT) instead of the 6-camera 360-degree setup, with a wider FOV (120°) and lower resolution (1080x720) to roughly cover the area in front of the vehicle.
  • Lowers sensor frequencies (e.g., LiDAR/camera at 10 Hz instead of 11 Hz).
  • Keeps the same LiDAR, IMU, and GNSS configuration as the default mapping.

When enabled via use_light_weight_sensor_mapping:=True, the launch file also skips the image_transport republish nodes for the disabled cameras and the multi_camera_combiner node automatically.

Example usage:

bash ros2 launch autoware_launch e2e_simulator.launch.xml \ map_path:=$HOME/autoware_map/Town01 \ vehicle_model:=sample_vehicle \ sensor_model:=carla_sensor_kit \ simulator_type:=carla \ use_light_weight_sensor_mapping:=True

Note that with this configuration, features that depend on the surround cameras (such as the multi-camera RViz view) are unavailable.

World Loading#

The carla_ros.py sets up the CARLA world:

  1. Client Connection:

    python client = carla.Client(self.local_host, self.port) client.set_timeout(self.timeout)

  2. Load the Map:

    Map loaded in CARLA world with map according to carla_map parameter.

    python client.load_world(self.map_name) self.world = client.get_world()

  3. Spawn Ego Vehicle:

    Vehicle are spawn according to vehicle_type, spawn_point, and agent_role_name parameter.

    python spawn_point = carla.Transform() point_items = self.spawn_point.split(",") if len(point_items) == 6: spawn_point.location.x = float(point_items[0]) spawn_point.location.y = float(point_items[1]) spawn_point.location.z = float(point_items[2]) + 2 spawn_point.rotation.roll = float(point_items[3]) spawn_point.rotation.pitch = float(point_items[4]) spawn_point.rotation.yaw = float(point_items[5]) CarlaDataProvider.request_new_actor(self.vehicle_type, spawn_point, self.agent_role_name)

Traffic Light Recognition#

The maps provided by the Carla Simulator (Carla Lanelet2 Maps) currently lack proper traffic light components for Autoware and have different latitude and longitude coordinates compared to the pointcloud map. To enable traffic light recognition, follow the steps below to modify the maps.

  • Options to Modify the Map

    • B. Modify the Existing Carla Lanelet2 Maps
    • Adjust the longitude and latitude of the Carla Lanelet2 Maps to align with the PCD (origin).
  • When using the TIER IV Vector Map Builder, you must convert the PCD format from binary_compressed to ascii. You can use pcl_tools for this conversion.
  • For reference, an example of Town01 with added traffic lights at one intersection can be downloaded here.

Tips#

  • Misalignment might occurs during initialization, pressing init by gnss button should fix it.
  • Changing the fixed_delta_seconds can increase the simulation tick (default 0.05 s), some sensor params in sensor_mapping.yaml need to be adjusted when it is changed (example: LIDAR rotation frequency should match the FPS).

Known Issues and Future Works#

  • Testing on procedural maps (Adv Digital Twin): Currently unable to test due to failures in creating the Adv Digital Twin map.
  • Traffic light recognition: The default CARLA Lanelet2 maps lack proper traffic light regulatory elements. See the "Traffic Light Recognition" section above for workarounds.