'simulator' 검색 결과

Draws points, lines, arrows, boxes, and text over a running CARLA world to visually check positions, paths, and objects. Sets colors and display lifetimes for temporary debugging overlays, without creating persistent actors or output files.

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• Marking a world position for inspection
• Visualizing paths and waypoints
• Adding a box and label around a target

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.

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• Publish obstacle sensor data to ROS 2
• Add a topic for vehicle speed commands
• Integrate another communication middleware

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.

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• Validate and run an .xosc file from another tool
• Compare runs with different speed parameters
• Explore bundled OpenSCENARIO 2.0 examples

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.

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• Test a driving agent on a specific route
• Find routes containing accident or obstacle scenarios
• Run Leaderboard scenarios without its scoring

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.

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• Smoke-test a Leaderboard setup on one route
• Evaluate an agent across validation routes
• Resume an evaluation after a crash

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.

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• Check a recording's map, duration, frames, and participants
• Find collisions between vehicles and pedestrians
• Identify vehicles that remained stuck

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.

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• Check whether a CARLA feature has a verified procedure
• Review known failures before following a UE4 tutorial
• Inspect build flags for ROS 2 or RSS support

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.

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• Getting CARLA for the first time
• Downloading an older version to reproduce a bug
• Fetching additional maps

Read and change a running CARLA server’s timing, physics substeps, and rendering settings. Switch between client-controlled steps and automatic progression, keep Traffic Manager synchronization aligned, and read back the settings to verify each change.

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• Setting a fixed simulation rate for sensor capture
• Keeping Traffic Manager aligned with synchronous mode
• Disabling rendering or adjusting physics substeps

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.

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• Build a Docker image for Leaderboard submission
• Investigate missing egg files in make_docker.sh
• Package a ROS agent with its bridge

Read CARLA Leaderboard result files to understand driving scores, route completion, and penalties. Investigate low scores, compare two runs, merge split results, or recalculate scores under version 2.0 or 2.1 rules without rerunning the simulation.

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• Understand driving scores and entry eligibility
• Investigate low scores using route details
• Recalculate results under another version's scoring rules

Control street, building, and vehicle light sources in CARLA. Switch groups on or off, adjust their color and brightness, and enable automatic nighttime lighting to prepare illuminated city scenes or blackouts.

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• Preparing an illuminated nighttime city
• Adjusting streetlight colors and brightness
• Creating a citywide blackout

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.

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• Checking whether CARLA is publishing ROS 2 data
• Inspecting sensor data contents and publishing rates
• Viewing lidar data and road markers in RViz2

Read and display CARLA object bounding boxes, then project them onto a camera image as 2D rectangles. Export an annotated PNG and box coordinates in JSON for object detection annotation work.

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• Adding vehicle boxes to a camera image
• Exporting box coordinates for detection annotations
• Checking vehicle dimensions

Add pedestrians to a CARLA map and let them walk autonomously. Adjust crowd size, walking speed, road-crossing frequency, and placement seeds, create stationary crowds, and remove pedestrians together with their movement controllers in the correct order.

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• Adding pedestrians who walk around a map
• Reusing pedestrian placement with a fixed seed
• Adjusting walking speeds and road crossings

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.

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• Preparing Scenic to work with CARLA
• Diagnosing scenario model import failures
• Checking missing vehicle definition tables

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.

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• Following the ego vehicle from behind
• Viewing a Toyota Prius from the driver’s seat
• Watching a vehicle from above

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.

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• Building CARLA ue4-dev from source
• Compiling CARLA with native ROS 2 support
• Building the Python client for an existing environment

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.

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• Holding every traffic light green
• Setting one junction's lights to red
• Shortening traffic-light cycles

Adjust how autopilot vehicles behave in CARLA, including speed, following distance, traffic light responses, and lane changes. Apply settings across traffic or to individual vehicles, and configure seeds and synchronization for repeatable traffic runs.

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• Adjust traffic speed and following distance
• Create traffic that ignores lights or other vehicles
• Change lane behavior for an individual autopilot vehicle

Import a four-wheel FBX vehicle into a CARLA source build, configure its physics, wheels, and animation, and register it for the Python API. Verify that the resulting vehicle can spawn, drive, and steer.

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• Importing a custom van or truck into CARLA
• Registering a vehicle for spawning through the Python API
• Checking why an imported vehicle will not drive or steer

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.

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• Manually test acceleration, steering, and braking
• Check vehicle lights and door operation
• Compare handling after changing mass or drag

Places cameras, lidar, and other sensors in a running CARLA world, attached to vehicles or fixed in place. Configures mounting positions and sensor attributes, enables supported ROS 2 data publishing, and reports sensor IDs for later data reading.

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• Mounting a dashcam on the ego vehicle
• Preparing a lidar for point cloud capture
• Adding GNSS or collision sensors

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.

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• Importing a custom RoadRunner map into CARLA
• Importing a large city split into tiles
• Rebuilding navigation areas for missing pedestrians