--- name: pangzhenying2025/automotive-dfm-benchmarking source: https://app.decimal.ai/s/pangzhenying2025-automotive-dfm-benchmarking@1/SKILL.md source_sha256: a0a95a421ca5 --- # 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 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ``` ## Data Foundation ### NDD Collection & Processing ```python # 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 ``` ## Benchmarking Methodology ### Performance Metrics ``` DFM基准评测指标体系 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Safety Metrics (安全性指标): ├── Collision rate vs. human baseline ├── Near-miss rate (TTC < 1.5s events) ├── Safety-critical event rate ├── Minimum TTC distribution comparison └── Emergency braking frequency Comfort Metrics (舒适性指标): ├── Acceleration distribution vs. human ├── Jerk distribution vs. human ├── Lateral offset smoothness ├── Speed profile consistency └── Ride quality index Efficiency Metrics (效率指标): ├── Travel time vs. human baseline ├── Throughput at bottlenecks ├── Speed utilization (actual/limit ratio) └── Lane utilization efficiency Human-Likeness Metrics (类人性指标): ├── Trajectory similarity (Fréchet distance) ├── Decision timing similarity ├── Speed profile similarity (DTW distance) ├── Gap acceptance distribution similarity └── Lane change timing similarity Overall DFM Score: DFM_score = w_s × Safety + w_c × Comfort + w_e × Efficiency + w_h × HumanLikeness where: w_s > w_c > w_e > w_h (safety weighted highest) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ``` ### Benchmark Protocol ```python # DFM Benchmarking Protocol class DFMBenchmark: """ Benchmark AD system against human driver baseline using DFM. """ def __init__(self, dfm_model, ad_system): self.dfm = dfm_model # Trained DFM with human baselines self.ad = ad_system # AD system under test def run_benchmark(self, scenario_suite): """ Run complete benchmark suite. Returns: - Per-scenario comparison (AD vs. human) - Aggregate safety/comfort/efficiency scores - Failure mode analysis - Improvement recommendations """ results = [] for scenario in scenario_suite: # Get human baseline for this scenario human_baseline = self.dfm.predict_behavior(scenario) # Run AD system in same scenario ad_behavior = self.ad.simulate(scenario) # Compare comparison = self.compare_behaviors( human=human_baseline, ad=ad_behavior, scenario=scenario ) results.append(comparison) return self.aggregate_results(results) def compare_behaviors(self, human, ad, scenario): """Compare AD behavior with human baseline""" return { "scenario_id": scenario.id, "safety": { "ad_min_ttc": ad.min_ttc, "human_min_ttc_percentile": human.ttc_percentile(ad.min_ttc), "collision": ad.collision_occurred, "safety_score": self.compute_safety_score(ad, human), }, "comfort": { "ad_max_accel": ad.max_acceleration, "human_accel_percentile": human.accel_percentile(ad.max_acceleration), "ad_max_jerk": ad.max_jerk, "comfort_score": self.compute_comfort_score(ad, human), }, "human_likeness": { "trajectory_distance": frechet_distance(ad.trajectory, human.mean_trajectory), "speed_profile_dtw": dtw_distance(ad.speed_profile, human.mean_speed), "decision_timing_diff": abs(ad.decision_time - human.mean_decision_time), }, } def generate_report(self, results): """Generate benchmark report with visualizations""" report = { "overall_dfm_score": self.compute_overall_score(results), "safety_rating": self.rate_safety(results), "scenarios_worse_than_human": self.find_deficiencies(results), "scenarios_better_than_human": self.find_strengths(results), "improvement_priorities": self.prioritize_improvements(results), } return report ``` ## Application Scenarios ### 1. AD System Version Comparison ``` 版本对比评测 ├── Input: AD System v1.0, v2.0 ├── Benchmark: Same DFM scenario suite ├── Output: │ ├── Per-scenario performance delta │ ├── Regression identification (v2 worse than v1) │ ├── Improvement quantification │ └── Overall DFM score trend └── Use case: Release gate decision ``` ### 2. Cross-Platform Benchmarking ``` 跨平台评测 ├── Input: Multiple AD systems (OEM A vs. B vs. C) ├── Benchmark: Standardized DFM scenario suite ├── Output: │ ├── Comparative safety ranking │ ├── Comfort comparison │ ├── Scenario-specific strengths/weaknesses │ └── Industry positioning └── Use case: C-NCAP, IIHS, consumer testing ``` ### 3. SOTIF Evidence Generation ``` SOTIF证据生成 ├── Input: AD system + DFM human baselines ├── Analysis: Per-scenario risk comparison ├── Output: │ ├── Scenarios where AD safer than human → evidence │ ├── Scenarios where AD less safe → risk │ ├── Statistical safety argument │ └── Residual risk quantification └── Use case: ISO 21448 compliance, type approval ``` ## Deliverables 1. **DFM Benchmark Report**: Complete AD vs. human baseline comparison 2. **Scenario Suite**: NDD-derived benchmark scenarios with human baselines 3. **Safety Argument**: Statistical evidence using DFM-based risk quantification 4. **Version Comparison**: Regression analysis between AD system versions 5. **Improvement Roadmap**: Data-driven prioritization of system improvements ## References - Zhang et al., "Benchmarking Autonomous Vehicles: A Driver Foundation Model Framework" (CARS 2026) - DRIVEResearch dataset: https://www.driveresearch.tech/ ## Related Skills - `automotive-scenario-driven-testing` — Scenario-based V&V methodology - `automotive-sotif-hazard-scenario` — SOTIF scenario construction - `automotive-e2e-safety-analysis` — E2E AD safety analysis - `automotive-china-l3-ads-compliance` — L3 validation requirements