CARLA

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

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

Replay a recorded CARLA simulation in full or over a selected time range. Change playback speed, follow a particular vehicle, restore recorded weather, or regenerate sensor data to examine the run again.

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• Watching a recorded simulation again
• Following a vehicle in slow motion
• Reviewing a selected time range at double speed

Turn a written traffic situation into a Scenic file, which describes possible scenes using placement rules and constraints. Select suitable maps and actors, then check that the file compiles and can generate at least one scene.

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• Create a scenario with a car cutting in from the right
• Describe a pedestrian crossing ahead of the main vehicle
• Build an unsignalized intersection scenario

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.

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• Creating an agent with a front camera and lidar
• Checking sensor limits and configuration errors
• Porting a Leaderboard 1.0 agent to 2.1

Set up a CARLA driving evaluation environment for Leaderboard 1.0, 2.0, or 2.1. Match the simulator and scenario_runner versions, configure Python paths, and verify that the required components load correctly.

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• Setting up an environment to evaluate a driving agent
• Checking version differences behind inconsistent scores
• Recreating the Leaderboard 1.0 evaluation environment

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.

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• Checking pedestrian navigation after importing a map
• Investigating pedestrians that stand still
• Visualizing the area pedestrians can walk through

Run individual or grouped traffic scenarios in CARLA, a driving simulator, and collect pass/fail reports or recordings. Check which scenarios match the current branch and map, and investigate runs that fail or show no movement.

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• Check a CARLA setup with a lead-vehicle scenario
• Run all control-loss scenarios and collect pass/fail results
• Find scenarios available on the current map

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

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

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

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

Set up ScenarioRunner, CARLA’s scenario engine, with a branch matched to your simulator version. Install dependencies, configure Python search paths, and verify imports to address setup failures before running scenarios.

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• Install ScenarioRunner for a specific CARLA version
• Switch an existing setup to the UE5 branch
• Fix imports failing because the agents package is missing

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

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

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

Run Scenic traffic scenarios on a CARLA server. First test whether a valid scene can be generated, then simulate with limits on runs and steps, record the outcome, and restore the world's asynchronous setting afterward.

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• Check a CARLA setup with carlaChallenge1
• Diagnose scenarios that reject every generated scene
• Check which challenge scenarios can run

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.

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• Driving a vehicle to specified coordinates
• Navigating to a chosen junction
• Planning and drawing a route without driving

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.

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• Understand a map's road and lane layout
• Compare the road network overlay with visible roads
• Locate the four-way junction nearest the map center

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

Import FBX 3D models into CARLA as props that can be placed through its Python API. Handles category tags, measured dimensions, asset registration, and spawn checks for individual files or folders of models.

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• Add custom benches and signs to CARLA
• Import a folder of models grouped by tag
• Check model dimensions and spawning

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

Create and edit CARLA driving routes by placing waypoints and scenario triggers in the simulator. Inspect routes visually, review summary tables, check XML structure and trigger positions, and convert older route files to the 2.x format.

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• Create a short route for testing one scenario
• Investigate scenarios that never trigger
• Review route lengths and scenario types

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