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Define the AI feature and where it is supposed to work
“AI controlling a car” can refer to many different functions, from lane keeping to an automated driving system (ADS) that handles a broader driving task. Start by naming the specific feature under test and describing what it is expected to do. Do not treat a result for one feature or condition as proof that the whole vehicle is safe.
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Write down the feature’s operational design domain (ODD): the conditions in which it is intended to operate. Depending on the feature, that description may cover road types, speed ranges, weather, lighting, traffic, road markings, and other limits. Also specify the expected behavior when a condition is outside that domain, such as whether the system should decline to engage or request a handoff. NIST’s September 2024 report, IR 8534, recommends a structured feature description, behavior specification, metrics, and scenario-based assessment.
- Feature: What function is being tested?
- Expected behavior: What should the system do in each relevant situation?
- ODD: Under what conditions is that behavior intended?
- Measures: What observable outcomes and decision-quality measures will reveal failures or weaknesses?
Start with repeatable simulation scenarios
Use a controlled simulator to run the same scenario repeatedly while changing relevant conditions. This makes it easier to reproduce a failure, compare software changes, and explore combinations that would be difficult or risky to stage first with a physical vehicle. NIST IR 8534 describes high-fidelity, physics-based simulation as a way to generate measurement data and identify potential risks and edge cases before real-world testing.
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A simulation is useful only to the extent that its modeled environment, sensors, vehicle dynamics, and interactions are relevant to the feature being assessed. Record the simulator configuration and scenario conditions so results can be interpreted and reproduced. Treat a successful run as evidence about that modeled case—not as a safety verdict for roads the simulator has not represented.
Measure decisions, not just collisions
A test that records only whether a crash occurred misses important information about how the system reached its outcome. NIST’s Measurement Science for Automated Vehicles project describes work on surrogate safety metrics such as time-to-collision, using forward simulation to estimate possible outcomes, comparing an action with counterfactual baselines, and searching across scenarios for systematic weaknesses.
These methods are intended to examine whether a system made a sound choice in context, not merely whether a particular run ended in a collision. NIST summarizes the distinction this way: “Current evaluation methods only answer ‘did a crash happen?’ This project’s products answer the harder question: ‘Did the decision-making system make the best available choice?’” The project describes measurement concepts under development; they are not a universal safety certification or a numeric pass threshold.
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Check scenario and input-space coverage
Automated systems can face a very large range of inputs. A small set of clean, familiar drives may leave important combinations untested. NIST’s autonomous-systems assurance work explains why test-environment coverage needs to be measured rather than assumed.
Build scenario variations around the feature’s ODD and plausible hazards. Depending on the system, these may include:
- Lighting changes, rain, or fog
- Pedestrians, animals, and other vehicles
- Missing, worn, or obscured road markings
- Traffic signs and unusual road layouts
- Interactions between environmental conditions and road users
Track which conditions and combinations have been exercised, which produced failures or near-conflicts, and where coverage remains thin. Coverage is a way to find gaps in testing; it does not by itself prove safe behavior.
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Use a staged path from models to physical tests
NHTSA describes an ADS testing framework that spans modeling, simulation, track testing, and open-road testing. NIST also emphasizes validating whether metrics derived from simulation are meaningful on physical systems. The stages answer different questions, and passing one stage does not automatically settle the next.
| Stage | What it can help assess | Key limitation |
|---|---|---|
| Modeling and simulation | Repeatability, broad scenario variation, and early discovery of potential risks | Results depend on how faithfully the model represents relevant physical systems and conditions |
| Track testing | Behavior with a physical vehicle and controlled, deliberately selected scenarios | A controlled track does not represent every open-road condition |
| Open-road testing | Behavior in the intended real-world context, subject to applicable restrictions and oversight | Conditions are less controlled, and testing must follow requirements for the project and jurisdiction |
Before advancing, examine the failures and uncertainties from the current stage, and determine whether the next stage can safely and appropriately address them. In particular, compare simulation-derived measures with physical-system behavior rather than assuming that a simulator’s score transfers directly to a vehicle.
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A test setup may combine a physics-based simulation engine, scenario and traffic management, and communications middleware. Specialized simulators can be added when the test needs to model features such as network communications. NIST IR 8534 lists examples including CARLA, AWSIM, CarSim, Scenario Runner, Eclipse SUMO, ROS 2, ns-3, and OMNeT++.
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One example systems-interaction testbed in NIST IR 8527, published in June 2024, uses CARLA for driving scenarios and environments, Autoware for automated driving functions, ROS for messaging, and ns-3 for vehicle-to-everything (V2X) communications. This is an example architecture, not a required software stack. NIST IR 8527 also distinguishes component testing from full-system testing: a component can behave correctly on its own while interactions among components create problems, so select the scope that matches the claim you are evaluating.
Treat vehicle trials as a separate safety and regulatory step
Testing a physical vehicle introduces hazards and obligations that simulation does not. NHTSA’s automated-vehicle safety overview says current automated-vehicle testing and deployment take place in limited, restricted, and designated locations and conditions, and that the agency monitors safety through its Standing General Order. Exact legal and site requirements depend on the project and jurisdiction; a general overview cannot substitute for identifying the applicable rules and controls for a particular test.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsDo not treat permission to simulate, or a favorable simulator result, as permission to test on a track or public road. Determine the relevant requirements for the vehicle, location, and jurisdiction before physical testing, and plan the next stage around the hazards and uncertainties that remain.
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