Test an AI driving agent in simulation before connecting it to any real vehicle: define what it can observe and control, run it against a pinned CARLA release, and replay documented scenarios in an isolated environment. Treat each result as evidence about the specific simulated conditions—not as proof that the agent is safe on public roads.
What makes a driving-agent sandbox safe?
A useful sandbox keeps the experiment inside a controlled simulation and makes the agent’s permissions and test conditions explicit. Start with a software-only setup: CARLA runs the simulated world, while a client or integration sends observations and receives control commands. CARLA’s server handles sensor rendering, physics, world-state updates, and simulated actors; clients can control actors and conditions through Python or C++ APIs. CARLA’s introduction describes the architecture, maps, and configurable actors.
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Safety also depends on the boundary around agent execution. Where feasible, run the agent in a separate, disposable environment. Limit its access to the code, configuration, and output directories it needs; constrain CPU, memory, and GPU use; and block access to real vehicle controls and external services unless the test specifically requires them and has been reviewed. These are engineering precautions, not a CARLA-prescribed or certified operating-system hardening standard.
Define the test boundary before connecting the agent
Choose a narrow initial goal—such as lane keeping, route following, traffic-light response, or collision avoidance—and write down expected behavior and pass/fail conditions before the first run. Specify which inputs the agent may use and which outputs it may issue.
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- Sensor-driven tests: Give the agent only the observations it would receive in the intended setup. Record sensor placement, resolution, update rate, and coordinate conventions.
- Privileged-state tests: If the agent reads simulator state unavailable to a real vehicle, label those runs separately. They test a different interface from a sensor-driven system.
- Control limits: Document which vehicle-control commands are available and keep the integration pointed at simulated actors, not real controls.
Choose and pin a CARLA release
Pick a stable CARLA release and use documentation that matches it. The CARLA documentation site’s latest pages may describe the development branch and features still in development, so do not assume their instructions apply to an older installation. Record the CARLA version, operating system, GPU and driver details, Python or ROS versions, and integration version in a run manifest.
Pin the other parts of the experiment as well: map, sensor configuration, scenario files, agent build, parameters, and any random seeds. This reproducibility checklist is recommended practice; CARLA’s documentation describes the tools, not a complete policy for repeatable experiments.
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Connect the agent through a documented interface
For a ROS-based agent, CARLA’s ROS Bridge carries simulator sensor and object data to ROS topics and converts ROS messages into simulator commands. Its documented examples include camera, lidar, radar, GNSS, and IMU data, as well as vehicle control and simulation controls. See the ROS Bridge documentation.
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CARLA’s 0.10.0 release announcement, dated 2024-12-19, lists a native ROS 2 interface as a release feature. That does not establish support for every earlier CARLA version or every ROS distribution. Check the 0.10.0 release notes against the version you plan to use.
Build repeatable traffic and scenario tests
Use defined situations instead of relying only on open-ended cruising. CARLA’s Traffic Manager can register and control simulated vehicles, helping populate traffic or adjust actor behavior. Scenario Runner supplies predefined situations and supports custom scenarios in Python or OpenSCENARIO 1.0; it is installed separately from the main CARLA package. The traffic simulation overview describes these tools and the use of recordings for bespoke metrics.
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Build a compact scenario matrix that reflects the agent’s intended operating conditions. For each case, fix the initial state, map, weather, other actors, and success or failure criteria. Include ordinary driving as well as relevant interactions with road users and traffic controls; add edge cases that matter to the test goal. Increase difficulty systematically, and preserve failing scenario files so a regression can be replayed.
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Choose the CARLA tools that fit the experiment
| Option | When it fits | Trade-off |
|---|---|---|
| Native ROS interface | The chosen CARLA release and ROS environment support it. | CARLA’s latest ecosystem documentation says it offers lower latency; support depends on release and ROS compatibility. Source; see also the 0.10.0 release announcement. |
| ROS Bridge | The agent needs ROS 1 or a legacy ROS integration. | Supports ROS 1 and ROS 2 according to CARLA’s documentation, but adds latency as a separate package. Source; comparison. |
| Traffic Manager | The test needs configurable surrounding traffic. | It can populate traffic and adjust simulated vehicle behavior; scenario usefulness still depends on the models and setup. Source. |
| Scenario Runner | The test needs named, repeatable situations or custom scenarios. | It is installed separately and supports Python and OpenSCENARIO 1.0 workflows in the cited overview. Source. |
Choose based on release compatibility, ROS distribution, latency needs, scenario expressiveness, repeatability, observability, and the agent interface being evaluated. No one combination is universally safer or more realistic.
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Record runs and investigate failures
Keep enough information with every run to reproduce it: agent build identifier, simulator and integration versions, scenario file, map, sensor setup, parameters, applicable seeds, and outcomes. Select metrics that match the goal; useful candidates include collisions, lane departures, traffic-rule violations, route completion, intervention events, and timeouts. These are suggested metrics, not a universal CARLA scoring rubric.
- Run the pinned scenario with the documented agent build and configuration.
- Save logs, recordings, and outcome metrics alongside the run manifest.
- Replay failures and change one controlled variable at a time when comparing behavior.
- Report coverage by naming the scenarios and configuration tested, and state the run count only if it was actually measured.
CARLA’s simulation capabilities do not establish that a passing result generalizes to public roads. Describe an outcome as a pass on the specified scenarios and configuration, and identify known simulator limitations. Do not present an unrun or unrecorded result as a test finding.
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