automotive-dfm-benchmarking

Automotive Dfm Benchmarking

Dfm Benchmarking

DFM Benchmarking — Driver Foundation Model Framework for AD Evaluation

Overview

Benchmarking framework based on the Driver Foundation Model (DFM) concept for evaluating autonomous driving systems. DFM uses large-scale naturalistic driving data (NDD) to model human driver behavior distributions, providing a human-performance baseline for AD system evaluation. This skill supports scenario generation, performance benchmarking, and safety argument construction using NDD-derived metrics.

DFM Concept

驾驶员基础模型 (Driver Foundation Model) 概念
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Core Idea:
  Human drivers provide a safety baseline:
  - Average driver: ~1 fatality per 10^8 km (developed countries)
  - Good driver: ~10x safer than average
  - AD must be at least as safe as good human driver

DFM Approach:
  1. Collect large-scale NDD (7.5M+ aerial trajectories)
  2. Model human driving behavior distributions
  3. Extract scenario-specific performance baselines
  4. Benchmark AD systems against human baselines
  5. Quantify relative safety improvement

DFM as Foundation Model:
  ├── Pre-trained on massive NDD
  ├── Captures diverse driving styles and conditions
  ├── Fine-tunable for specific scenarios/regions
  ├── Provides probabilistic behavior predictions
  └── Serves as benchmark generator and evaluator
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Data Foundation

NDD Collection & Processing

# NDD Processing Pipeline for DFM
class NDDProcessor:
    """
    Process Naturalistic Driving Data for DFM benchmarking.
    Supports aerial trajectory data (drone-based) and fleet data.
    """

    def __init__(self, data_source: str):
        """
        data_source options:
        - "aerial": Drone-based trajectory extraction (7.5M+ trajectories)
        - "fleet": Vehicle-mounted sensor data
        - "hybrid": Combined aerial + fleet data
        """
        self.source = data_source

    def extract_driving_primitives(self, trajectories):
        """
        Extract fundamental driving behaviors from trajectory data.

        Driving primitives:
        - Car-following (跟车)
        - Lane-changing (换道)
        - Merging (汇入)
        - Diverging (分流)
        - Crossing (交叉)
        - Free-driving (自由行驶)
        """
        primitives = {
            "car_following": self.extract_car_following(trajectories),
            "lane_change": self.extract_lane_changes(trajectories),
            "merge": self.extract_merges(trajectories),
            "diverge": self.extract_diverges(trajectories),
            "crossing": self.extract_crossings(trajectories),
            "free_driving": self.extract_free_driving(trajectories),
        }
        return primitives

    def build_behavior_distributions(self, primitives):
        """
        Build statistical distributions of driving behaviors.

        For car-following:
        - Time headway distribution: P(THW)
        - TTC distribution: P(TTC)
        - Speed distribution: P(v | context)
        - Acceleration distribution: P(a | context)
        - Lane offset distribution: P(offset | context)
        """
        distributions = {}
        for primitive_type, data in primitives.items():
            distributions[primitive_type] = {
                "thw": fit_distribution(data.thw_values),
                "ttc": fit_distribution(data.ttc_values),
                "speed": conditional_distribution(data.speeds, data.contexts),
                "acceleration": conditional_distribution(data.accels, data.contexts),
                "lateral_offset": fit_distribution(data.offsets),
                "jerk": fit_distribution(data.jerks),
            }
        return distributions

    def generate_benchmark_scenarios(self, distributions, n_scenarios=1000):
        """
        Generate benchmark scenarios by sampling from behavior distributions.

        Importance sampling: over-sample from tail (critical) regions
        """
        scenarios = []
        for i in range(n_scenarios):
            # Sample scenario type based on exposure
            scenario_type = sample_weighted(distributions.keys(),
                                           weights=exposure_weights)
            # Sample parameters from distribution
            params = sample_from_distribution(
                distributions[scenario_type],
                sampling="importance",  # over-sample tails
                criticality_weight=2.0
            )
            scenarios.append(BenchmarkScenario(
                type=scenario_type,
                parameters=params,
                human_baseline=distributions[scenario_type],
            ))
        return scenarios