249 skills · sorted by relevance
Record a running CARLA simulation to a .log file for later replay or inspection. Capture object creation, movement, and traffic-light states, optionally include controls and velocities, and verify that the recording contains simulation frames.
Drives a CARLA vehicle to a specific destination using navigation agents that plan routes, avoid obstacles, and obey traffic lights. Can also plan and draw a route between two points without moving the vehicle.
Package a CARLA Leaderboard agent into a Docker image for submission. It checks required directories and Python package files, supports ROS variants, and verifies that dependencies import and the agent loads inside the image.
Creates a Python agent skeleton for CARLA Leaderboard and checks its sensor configuration offline against the installed version’s rules. Covers version-specific initialization, driving logic, and ROS1 or ROS2 agent integration.
Evaluate a driving agent on CARLA Leaderboard routes after checking the required setup. Select routes and repetitions, save driving scores and infractions, record runs, and resume interrupted evaluations from a saved checkpoint.
Validate and run OpenSCENARIO traffic scenarios through ScenarioRunner. Check file structure, maps, vehicle definitions, and supported features before simulation. Run both .xosc and .osc files, with global parameter overrides available for OpenSCENARIO 1.x.
Inspect data published by a ROS 2-enabled CARLA server using Docker, without installing ROS 2 locally. Check topic contents and rates, run a sensor-equipped vehicle demo, and view lidar data and supported map markers in RViz2.
Control traffic lights in a running CARLA simulation. Change their color, switch them off, hold a fixed state, adjust phase durations, or restore normal cycling, targeting individual lights, junctions, nearby lights, or the whole map.
Explore roads, lanes, and junctions in a CARLA simulation map through text reports and visual overlays. Locate places described in everyday language, inspect nearby lane details, and trace a path forward or backward along a lane.
Populate a running CARLA simulation with autonomous traffic, evenly spaced vehicles in one lane, or a single vehicle for control and sensors. Filter vehicle models, keep automated driving active, and remove vehicles when the scene is finished.
Downloads CARLA release packages, nightly builds, source code, or Docker images, then reports their locations and environment variables for follow-up setup. Supports extra maps and resumable downloads, helping prepare the files needed for subsequent installation or building.
Validate CARLA pedestrian navigation by sampling walkable locations and reporting their coverage. Draw the samples as debug points to inspect the walkable area after a map import or investigate pedestrians that will not move.
Control CARLA’s on-screen camera to view a vehicle from behind, above, the driver’s seat, or the front. Select vehicles by name, role, or ID, frame them once, or follow them as they move.
List static objects built into a CARLA map and hide or restore them by category or asset name. Adjust buildings, vegetation, fences, and other scenery, preview affected objects, and change both their visibility and collision behavior.
Read existing CARLA sensors to view live camera or lidar output, save recordings, or inspect a single reading. Display multiple sensors together and check ROS 2 topic names, delivery settings, and whether publishing is enabled.
Install Scenic, a language for describing scenarios with randomized conditions, into the Python environment used by CARLA. Check command-line tools and version compatibility, optionally download example scenarios and map assets, and save the installation paths.
Import an FBX character into a CARLA source build as a pedestrian available through the Python API. Check its GEN3 skeleton, create assets and a walker blueprint, register it, and verify spawning, ground placement, and movement.
Run CARLA routes with a driving agent through ScenarioRunner, including scenarios available only in route mode. Inspect route contents, execute one or all routes, and obtain reports on completion, collisions, traffic lights, and other criteria.
Extend CARLA’s native ROS 2 interface to publish sensor data or receive vehicle commands. Covers topic implementation, registration, data connections, and verification, plus adding middleware, the communication layer used to exchange messages.
Manually drive a vehicle in CARLA, set a target speed, operate lights and doors, and adjust mass or drag. You can also inspect the command topics used to control the main vehicle through ROS 2.
Builds CARLA's ue4-dev branch on Linux, producing the Unreal Engine 4.26 editor, server modules, and an installed Python client package. Fetches required content, optionally compiles native ROS 2 support, and verifies the build against a source-built server.
Import RoadRunner geometry and road data into a CARLA source build, then verify the map loads and supports driving. Handle tiled maps and generate and inspect pedestrian navigation areas for standard maps.
Check whether a CARLA feature is usable in the current environment and has a verified procedure. Review known failures, inspect build settings, and identify differences between client, server, and source versions before planning work.
Inspect CARLA driving simulation logs without replaying them. Get recording details, find collisions by participant category, and identify vehicles that barely moved, with times and object IDs to locate moments worth reviewing.