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Test an AI driving agent in simulation before connecting it to any real vehicle: run CARLA in a controlled software environment, give the agent only the observations and controls the test requires, and make each scenario reproducible. A simulation pass demonstrates performance only in the scenarios and configuration you ran—not general road safety.
What a safe driving-agent sandbox should contain
A software-only setup can keep experiments away from public roads and physical vehicles. CARLA uses a client-server architecture: its server handles the simulated world, physics, and sensor rendering, while clients use Python or C++ APIs to set conditions and control actors. It includes maps, configurable actors, weather, and OpenDRIVE road descriptions. See CARLA’s introduction.
Safety depends on keeping the boundary between agent and simulator clear. Decide what the agent can observe, which actions it can issue, and where its code can read or write. Treat system isolation as your responsibility: the CARLA documentation describes simulator and integration capabilities, not a certified operating-system sandbox specification.
Plan the test before installing the integration
Choose a narrow objective
Start with a defined task, such as lane keeping, route following, traffic-light response, or collision avoidance. State the expected behavior and pass/fail conditions before running the agent. Decide whether it will use sensor-like data or privileged simulator state; report those as separate test types, since access to full simulator state is not equivalent to driving from sensors.
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Limit what the agent can reach
- Run agent code in a separate, disposable environment where practical.
- Grant access only to required code, configuration, and output directories.
- Set resource limits appropriate to the host, including CPU, memory, and GPU where available.
- Do not expose real vehicle controls or external services unless the experiment requires them and that access has been reviewed.
These are engineering precautions, not CARLA-prescribed host-hardening settings. Choose controls suited to your operating system and threat model rather than treating a generic container command as a universal safety standard.
Choose and pin a CARLA release
Select a specific CARLA release, then use its matching documentation and compatible integrations. CARLA’s latest documentation may describe the development branch and in-development features, so do not assume every page applies to every release. Record the simulator release, operating system, GPU and driver details, and the versions of Python, ROS, and any bridge or interface used.
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Keep a test manifest with those details alongside the agent build, map, sensor configuration, scenario files, and relevant parameters. Pinning the setup makes it possible to investigate a failure or determine whether a later result changed because of the agent or the environment.
Connect the agent through a defined interface
Choose native ROS or the ROS Bridge by compatibility
For ROS-based agents, CARLA’s ROS Bridge sends simulator sensor and object data through ROS topics and translates ROS messages into simulator commands. Documented data includes camera, lidar, radar, GNSS, and IMU information, as well as vehicle and simulation controls. See the ROS Bridge documentation.
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| Option | Best fit | Trade-off |
|---|---|---|
| CARLA native ROS interface | A CARLA release and ROS environment that support it | CARLA’s latest/dev ROS page recommends the native interface for performance and lower latency; support depends on release compatibility. CARLA ROS documentation. |
| CARLA ROS Bridge | ROS 1 or ROS 2 integration that needs the separate bridge | Supports ROS 1 and ROS 2 but adds latency. Bridge documentation and CARLA ROS documentation. |
CARLA’s 0.10.0 release announcement, dated 2024-12-19, describes the native ROS 2 interface as a release feature. That does not establish support for every earlier release or ROS distribution; check the documentation for the exact combination you install. See the 0.10.0 release announcement.
Specify observations and actions
Keep the interface narrow and write down its conventions. For image-based input, specify sensor placement, resolution, update rate, and coordinate conventions. Record what control commands the agent can send and how those commands are interpreted. If a test supplies privileged state, label it explicitly rather than presenting it as a sensor-driven test.
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Build repeatable traffic and scenario tests
Open-ended cruising is not enough to show how an agent handles particular situations. CARLA’s Traffic Manager can control registered simulated vehicles, helping populate traffic or adjust actor behavior. Scenario Runner provides predefined and custom scenarios, including Python and OpenSCENARIO 1.0 workflows; it is installed separately from CARLA. The traffic simulation overview also describes analyzing recordings with bespoke metrics.
Create a scenario matrix that reflects the agent’s intended operating conditions. For each case, record the initial state, map, weather, other actors, and success or failure conditions. Include routine driving, interactions with other road users, traffic controls, and relevant edge cases. Increase difficulty systematically and retain failing scenario files so you can replay regressions.
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Run, record, and diagnose each test
- Save the configuration: Record the agent build identifier, CARLA and integration versions, scenario file, map, sensor setup, parameters, and random seeds if used.
- Run the defined scenario: Keep the agent within its intended interface and note timeouts or interventions as well as the outcome.
- Capture relevant metrics: Depending on the task, track route completion, collisions, lane departures, traffic-rule violations, and intervention or timeout events. These are useful candidate measures, not a universal CARLA scoring rubric.
- Replay and isolate failures: Use saved scenarios or recordings to inspect what happened. When comparing runs, change one controlled factor at a time where possible.
- Describe coverage precisely: Report the scenarios and configuration tested, and give a run count only if it was actually measured. Note known simulator limitations and avoid claiming tests or results that were not recorded.
Interpret simulation results narrowly
A successful run is evidence that the agent completed a specified simulated scenario under a specified setup. It does not prove that the agent is safe on public roads or that the simulated traffic and sensor conditions fully represent the real world. CARLA’s documentation establishes simulation, interface, and scenario capabilities; it does not establish real-world safety validation.
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