automotive-scenario-driven-testing

Automotive Scenario Driven Testing

Scenario Driven Testing

Scenario-Driven Testing & Evaluation — Methodology for ADAS/ADS V&V

Overview

Comprehensive methodology for scenario-driven testing and evaluation of ADAS/ADS systems. Integrates naturalistic driving data analysis, scenario extraction, simulation-track-road combined testing, and statistical evidence generation. This approach bridges the gap between traditional mileage-based testing and systematic scenario-based validation.

The Scenario-Driven V&V Paradigm

场景驱动测试评价范式
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Traditional Approach (传统方法):
  Drive millions of km → Count incidents → Statistically argue safety
  Problem: 10^8 km needed for L3, impractical

Scenario-Driven Approach (场景驱动方法):
  1. Extract scenarios from NDD/accidents/standards
  2. Parameterize and generate variations
  3. Test systematically across parameter space
  4. Quantify risk per scenario type
  5. Aggregate to overall safety argument

  Advantage: 10^3 scenarios × 10^3 variations = comprehensive coverage
             with 10^4 - 10^5 km equivalent testing
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Scenario Sources & Extraction

Source 1: Naturalistic Driving Data (NDD)

# NDD-based scenario extraction pipeline
class NaturalisticDrivingScenarioExtractor:
    """
    Extract test scenarios from naturalistic driving data.
    Based on DRIVEResearch methodology with 7.5M+ aerial trajectories.
    """

    def __init__(self, dataset_path: str):
        self.dataset = load_trajectory_dataset(dataset_path)

    def extract_critical_events(self,
                                 ttc_threshold: float = 3.0,
                                 thw_threshold: float = 1.5,
                                 decel_threshold: float = -4.0):
        """
        Extract safety-critical events from trajectory data.

        Criticality indicators:
        - TTC (Time-to-Collision) < threshold
        - THW (Time Headway) < threshold
        - Hard braking (deceleration < threshold)
        - Near-miss events
        """
        critical_events = []
        for trajectory in self.dataset:
            for timestep in trajectory:
                if (timestep.ttc < ttc_threshold or
                    timestep.thw < thw_threshold or
                    timestep.acceleration < decel_threshold):
                    critical_events.append(
                        self.extract_scenario_context(trajectory, timestep)
                    )
        return critical_events

    def cluster_scenarios(self, events, method="spectral"):
        """
        Cluster similar events into scenario types.
        Methods: k-means, DBSCAN, spectral, GMM
        """
        features = self.extract_features(events)
        clusters = cluster_algorithm(features, method)
        return self.create_scenario_templates(clusters)

    def parameterize_scenario(self, template):
        """
        Create parameterized scenario from template.
        Output: OpenSCENARIO 2.0 compatible definition
        """
        return {
            "scenario_type": template.type,
            "parameters": {
                "ego_speed": Distribution(template.ego_speed_stats),
                "target_speed": Distribution(template.target_speed_stats),
                "relative_distance": Distribution(template.distance_stats),
                "lateral_offset": Distribution(template.offset_stats),
            },
            "criticality_distribution": template.criticality_dist,
            "exposure_frequency": template.occurrence_rate,
        }