Scenario-Based Testing for ADS

• Scenario-based testing is a systematic method for evaluating ADS safety through designed test scenarios that mimic real-world traffic complexities.
• It employs a hierarchical scenario taxonomy—functional, logical, and concrete—to ensure comprehensive coverage and reproducibility in simulation tests.
• The approach integrates statistical risk assessment and dynamic scenario selection to achieve rigorous pass/fail evaluations aligned with evolving safety regulations.

Scenario-based testing of Automated Driving Systems (ADS) is a systematic, data-driven, and increasingly standardized methodology for evaluating the behavioral safety of autonomous vehicles in complex environments. This approach entails the design, generation, execution, and objective evaluation of a diverse set of test scenarios—each defined by explicit parameters governing dynamic traffic, environmental, and behavioral factors—chosen to reflect both routine and critical driving situations encountered across an ADS’s operational design domain (ODD). Scenario-based testing enables the efficient exploration of rare, safety-critical edge cases and supports rigorous, automated pass/fail evaluation aligned with regulatory frameworks, thereby providing a viable substitute for impractical large-scale on-road validation.

1. Fundamental Concepts and Scenario Taxonomy

Scenario-based testing for ADS is grounded in the explicit construction, execution, and evaluation of scenarios. A scenario in this context is a formalized description of a specific traffic situation, typically represented at three abstraction levels:

• Functional Scenario: High-level qualitative description of a class of traffic situation (e.g., "urban T-intersection with a pedestrian crossing").
• Logical Scenario: Parameterized representation, specifying variable ranges for actors, behaviors, and environmental conditions (e.g., vehicle approach speed 15–40 km/h, pedestrian crossing probability 0.1–0.9).
• Concrete Scenario: Fully instantiated realization with fixed parameter values (e.g., a specific T-intersection at 17:35, ego vehicle at 23 km/h, pedestrian crossing at 12 m ahead).

The scenario-based approach achieves comprehensive assessment by combining:

• Prescriptive rules (absolute requirements not to be violated, such as “never cause a collision” in a scenario with reasonable external behavior) and
• Risk-based rules (performance metrics and severity gradings for non-binary phenomena, aggregating outcomes statistically over multiple scenario executions) (Myers et al., 2020).

This taxonomy rigorously separates scenario intent, parameterization, and instantiation, facilitating systematic coverage and reproducibility across the simulation-test-execution pipeline (Weber et al., 2021, Zhong et al., 2021, Schuldes et al., 3 Apr 2024).

2. Evaluation Criteria and Outcome Scoring

Behavioral safety for ADS under scenario-based testing is defined by top-level evaluation criteria, typically as follows (Myers et al., 2020, Kusano et al., 2022):

1. Never cause a collision: Prescriptive, zero-tolerance in reasonable behavior scenarios.
2. Drive to mitigate risks from others’ unreasonable behavior: Risk-based, aggregating performance across stochastic actor behaviors.
3. Obey traffic rules: Mixed, with prescriptive application when all others behave reasonably and risk-based assessment for edge-case violations.
4. Leave reasonable safety margins: Risk-based, judging the ability to maintain adequate buffers even under emergent situations.
5. Behave considerately toward other road users: Risk-based, penalizing disruptions to flow or causing confusion, assessed on a gradated severity scale.

Outcome scoring is realized through executable “scoring rules”—stored code scripts (e.g., in Python) attached to scenario definitions (Myers et al., 2020). Scoring rules are divided into two formal types:

• Prescriptive scoring rules: Any violation concludes immediate failure for the scenario.
• Risk-based scoring rules: Each scenario execution is assigned an ordinal severity grade (e.g., S0–S3), with pass/fail status determined via statistical aggregation over the observed rate of severe outcomes.

A key quantitative construct for risk evaluation is the tolerable occurrence rate:

$l_{i,n}^{(acceptable)} = \lambda_i \cdot e_n$

where $\lambda_i$ is the per-severity-level tolerability threshold and $e_n$ is the scenario’s exposure (frequency of occurrence). This links regulatory standards to measurable scenario outcomes, providing an operational metric for scenario selection and evaluation (Myers et al., 2020).

3. Scenario Generation, Selection, and Database Management

Comprehensive testing of ADS necessitates extensive scenario databases with formalized search, filtering, and execution capabilities. Recent development of such databases, exemplified by scenario.center (Schuldes et al., 3 Apr 2024) and the MUSICC project (Myers et al., 2020), features:

• Unified input data schemas (e.g., OMEGA format following a 6-layer scenario model) supporting ingestion from heterogeneous sources—real traffic data, synthetic simulations, or standardized scenario definitions.
• Automated event and scenario extraction: Algorithms for base scenario/event detection (e.g., via time-to-collision or headway metrics), semantic tagging (actors, interactions), and multi-modal data alignment (trajectories, decision points).
• Flexible querying and selection: Graph-based query interfaces and sequence filters, which allow extraction of precise scenario subsets (e.g., multi-actor right-of-way violations within particular ODD segments).
• Scenario execution methods: Support for replay-to-simulation (direct trajectory reproduction), adaptive replay with driver models (modulate for safety if ADS deviates), and fully parameterized scenario generation via probabilistic sampling and hybrid graph representations (Schuldes et al., 3 Apr 2024).

Further, integration of metadata such as exposure rates, conditional occurrence probabilities, and parameter distributions facilitates both coverage assessment and regulatory reporting (Myers et al., 2020, Schuldes et al., 3 Apr 2024, Pathrudkar et al., 2023).

4. Statistical Frameworks, Pass-Fail Decisions, and Hypothesis Testing

For risk-based criteria, individual scenario executions do not yield definitive pass/fail decisions due to the stochastic nature of traffic events and limitations of finite test samples. The framework, therefore, employs statistical aggregation (Myers et al., 2020, Kusano et al., 2022), using the empirical rates of severity occurrences $l_{i,n}^{(actual)}$ measured over $N$ tests for each functional scenario:

• Hypothesis testing: To decide acceptance, formal hypotheses are defined:
  • $H_a$: "Severe outcomes occur at a rate $\geq$ threshold"
  • $H_b$: "Observed rates are below threshold"

Sample variability, especially in rare-event regimes, results in wide confidence intervals; thus, scenario selection must prioritize challenging yet representative cases to ensure statistical power (Myers et al., 2020).

• Statistical decision rule: Aggregated results are compared to $l_{i,n}^{(acceptable)}$; test counts and scenario selection strategies (e.g., maximizing test informativity for rare but severe outcomes) are designed to achieve sufficient confidence.

Advanced approaches include clustering scenario outcomes (e.g., using Dynamic Time Warping and kernel PCA/DBSCAN) to eliminate redundant test cases and partition scenarios into high-, medium-, and low-criticality groups, further improving resource efficiency and regulatory compliance (Schütt et al., 2023).

5. Scenario Manipulation, Stress Testing, and Co-Simulation

Traditional scenario-based methods risk insufficient coverage of rare yet safety-critical cases. Stress testing frameworks such as the STM (Nalic et al., 2020) actively manipulate simulation participants—via injected driver models, DLL interfaces, or external traffic simulators—to provoke critical maneuvers grounded in statistical accident data. Key features include:

• Lane-dependent event matrices: Structured triggers for emergency deceleration or abrupt lane changes calibrated to real-world accident statistics (e.g., Austria highway clusters).
• Co-simulation: Tight integration between vehicle dynamics simulators (e.g., IPG CarMaker) and stochastic traffic flow simulation models (e.g., PTV Vissim), coordinated via Matlab/Simulink, to capture both ego vehicle responses and complex multi-agent traffic.
• Quantitative impact: Demonstrable increases in collision/near-collision events per test kilometer, enabling effective validation of edge-case performance and facilitating faster development iteration cycles (Nalic et al., 2020).

6. From Simulation to Real-World Assurance and Regulatory Integration

High-fidelity simulation is central for photo-realistic, sensor-accurate evaluation of ADS under diverse scenarios (Zhong et al., 2021). Robustness of the simulation-to-real transfer is recognized as a critical challenge, with ongoing research into:

• Simulator selection and fidelity assessment: Empirical validation of simulation environments (e.g., CARLA, SVL, Vissim, CarMaker) and integration of sensor noise, latency, and module execution times to replicate real-world conditions (Kusano et al., 2022).
• Standardization and quality metrics: Use of open formats (OpenSCENARIO/OpenDRIVE), consistent scenario identification language and metadata, and alignment with regulatory initiatives such as UNECE WP.29 NATM (Camp et al., 30 Jul 2025).
• Iterative scenario database curation: Real-world on-road and test-track data directly inform scenario databases; discrepancies or “unknown-unknowns” discovered during deployments feed back into scenario expansion, improving test coverage over time.
• Federated scenario databases: As in the SUNRISE and SYNERGIES Horizon Europe projects, bringing together knowledge-based and data-driven scenario sources via a common federation interface, allowing enhanced traceability, representativeness, and ODD completeness evaluation (Camp et al., 30 Jul 2025).

Safety risk quantification increasingly relies on measured scenario risk, exposure, and consequence integration:

$$R = \int_s p_{crash}(s) \cdot C(s) \cdot E(s) \, ds$$

where $p_{crash}(s)$ is the crash probability in scenario $s$, $C(s)$ the consequence severity, and $E(s)$ the scenario’s real-world exposure (Camp et al., 30 Jul 2025).

7. Impact, Limitations, and Ongoing Challenges

Scenario-based ADS testing frameworks have enabled scalable, automated, and objective type approval processes, reflected in tools such as the MUSICC scenario database and high-throughput co-simulation setups (Myers et al., 2020, Nalic et al., 2020).

Nonetheless, several challenges persist:

• Statistical power for rare events: Even with large simulation sets, rare event rates remain difficult to estimate precisely, necessitating advanced sampling, scenario selection, and risk aggregation strategies (Myers et al., 2020, Schütt et al., 2023).
• Simulation-to-reality gap: Ensuring that scenario responses under simulation correlate with real-world outcomes remains a critical open research area, influencing both scenario design and performance evaluation (Zhong et al., 2021).
• Scenario diversity and coverage balance: Clustering and archetype selection methods are necessary to avoid redundant scenario proliferation while achieving sufficient behavioral coverage, particularly under new regulatory regimes mandating minimum scenario sets (Schütt et al., 2023, Camp et al., 30 Jul 2025).
• Operationalization and traceability: Integrated safety assessment frameworks require documentation, traceability of scenario selection, and ongoing quality metrics to be accepted by type approval authorities (Camp et al., 30 Jul 2025).

The scenario-based testing paradigm, with its formalized criteria, automation-friendly architecture, statistical grounding, and focus on real-world coverage, has become central to rigorous ADS verification and regulatory compliance, shaping the practices of both industrial deployment and global safety standardization.