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| import numpy as np
from dataclasses import dataclass
from typing import List, Tuple
"""
Euro NCAP 2026 仿真-实车关联框架
参考:
- AB Dynamics Euro NCAP 2026 半年回顾
- 2026 协议首次接受仿真数据
"""
@dataclass
class TestScenario:
"""测试场景"""
scenario_id: str
name: str
base_speed_kmh: float
overlap_pct: float
braking_severity: float
target_type: str
def generate_variations(self, speed_range: Tuple[float, float],
overlap_range: Tuple[float, float],
braking_range: Tuple[float, float]) -> List['TestScenario']:
"""生成鲁棒性测试变体"""
variations = []
speeds = np.arange(speed_range[0], speed_range[1] + 5, 5)
overlaps = np.arange(overlap_range[0], overlap_range[1] + 25, 25)
brakings = np.arange(braking_range[0], braking_range[1] + 0.1, 0.1)
for s in speeds:
for o in overlaps:
for b in brakings:
variations.append(TestScenario(
scenario_id=f"{self.scenario_id}_{s}_{o}_{b:.1f}",
name=f"{self.name} (v={s}km/h, overlap={o}%, brake={b:.1f}g)",
base_speed_kmh=s,
overlap_pct=o,
braking_severity=b,
target_type=self.target_type
))
return variations
@dataclass
class SimulationResult:
"""仿真结果"""
scenario_id: str
ttc_s: float
collision_avoided: bool
brake_engage_s: float
deceleration_peak_g: float
simulated: bool = True
@dataclass
class TrackTestResult:
"""实车测试结果"""
scenario_id: str
ttc_s: float
collision_avoided: bool
brake_engage_s: float
deceleration_peak_g: float
simulated: bool = False
class EuroNCAP2026Simulator:
"""Euro NCAP 2026 仿真-实车关联引擎"""
CCRB_BASE = TestScenario(
scenario_id="CCRB-2026",
name="Car-to-Car Rear Braking",
base_speed_kmh=50,
overlap_pct=-50,
braking_severity=0.3,
target_type="vehicle"
)
def __init__(self):
self.simulation_results: dict = {}
self.track_results: dict = {}
self.correlation_report: dict = {}
def generate_ccrb_variations(self) -> List[TestScenario]:
"""生成 CCRB 鲁棒性测试变体（2026协议: 77变体）"""
return self.CCRB_BASE.generate_variations(
speed_range=(30, 80),
overlap_range=(-75, 75),
braking_range=(0.2, 0.6)
)
def simulate_scenario(self, scenario: TestScenario) -> SimulationResult:
"""仿真单个场景"""
np.random.seed(hash(scenario.scenario_id) % 2**32)
speed_ms = scenario.base_speed_kmh / 3.6
ttc = 100 / speed_ms
brake_delay = np.random.uniform(0.3, 1.5)
brake_engage = ttc - brake_delay
decel = scenario.braking_severity * 9.81
stopping_dist = speed_ms**2 / (2 * decel)
collision_avoided = stopping_dist < 100
return SimulationResult(
scenario_id=scenario.scenario_id,
ttc_s=round(ttc, 3),
collision_avoided=collision_avoided,
brake_engage_s=round(brake_engage, 3),
deceleration_peak_g=round(decel / 9.81, 2)
)
def correlate_sim_track(self, sim: SimulationResult,
track: TrackTestResult) -> dict:
"""仿真-实车关联分析"""
ttc_error = abs(sim.ttc_s - track.ttc_s)
brake_error = abs(sim.brake_engage_s - track.brake_engage_s)
decel_error = abs(sim.deceleration_peak_g - track.deceleration_peak_g)
result_match = sim.collision_avoided == track.collision_avoided
correlation_score = 100
correlation_score -= min(20, ttc_error * 10)
correlation_score -= min(20, brake_error * 10)
correlation_score -= min(20, decel_error * 5)
if not result_match:
correlation_score -= 40
return {
'scenario_id': sim.scenario_id,
'ttc_error_s': round(ttc_error, 3),
'brake_error_s': round(brake_error, 3),
'decel_error_g': round(decel_error, 2),
'result_match': result_match,
'correlation_score': round(correlation_score, 1),
'pass': correlation_score >= 80
}
def efficiency_analysis(self, scenarios: List[TestScenario]) -> dict:
"""测试效率分析"""
np.random.seed(42)
trad_setup_min = 15
trad_execute_min = 8
trad_analysis_min = 10
trad_total = (trad_setup_min + trad_execute_min + trad_analysis_min) * len(scenarios)
opt_setup_min = 3
opt_execute_min = 5
opt_analysis_min = 1
opt_total = (opt_setup_min + opt_execute_min + opt_analysis_min) * len(scenarios)
sim_time_min = 0.5
sim_total = sim_time_min * len(scenarios)
return {
'total_scenarios': len(scenarios),
'traditional_hours': round(trad_total / 60, 1),
'optimized_hours': round(opt_total / 60, 1),
'simulation_hours': round(sim_total / 60, 1),
'time_saved_pct': round((1 - opt_total / trad_total) * 100, 1),
'efficiency_gain': round(trad_total / opt_total, 1)
}
if __name__ == "__main__":
print("=" * 70)
print("Euro NCAP 2026 测试仿真-实车关联系统")
print("=" * 70)
sim_engine = EuroNCAP2026Simulator()
variations = sim_engine.generate_ccrb_variations()
print(f"\nCCRB 鲁棒性测试变体数: {len(variations)}")
print(f"2026协议目标: 77 变体")
sim_results = []
for v in variations[:20]:
result = sim_engine.simulate_scenario(v)
sim_results.append(result)
print(f"\n仿真结果 (前20个):")
print(f"{'场景ID':<35} {'TTC(s)':>8} {'制动介入':>10} {'减速度(g)':>10} {'避免碰撞':>10}")
print("-" * 75)
for r in sim_results[:10]:
print(f"{r.scenario_id:<35} {r.ttc_s:>8.3f} {r.brake_engage_s:>10.3f} "
f"{r.deceleration_peak_g:>10.2f} "
f"{'✅' if r.collision_avoided else '❌':>10}")
np.random.seed(123)
track_result = TrackTestResult(
scenario_id=sim_results[0].scenario_id,
ttc_s=sim_results[0].ttc_s + np.random.uniform(-0.05, 0.05),
collision_avoided=sim_results[0].collision_avoided,
brake_engage_s=sim_results[0].brake_engage_s + np.random.uniform(-0.02, 0.02),
deceleration_peak_g=sim_results[0].deceleration_peak_g + np.random.uniform(-0.1, 0.1)
)
corr = sim_engine.correlate_sim_track(sim_results[0], track_result)
print(f"\n仿真-实车关联分析:")
print(f" TTC 误差: {corr['ttc_error_s']:.3f}s")
print(f" 制动介入误差: {corr['brake_error_s']:.3f}s")
print(f" 减速度误差: {corr['decel_error_g']:.2f}g")
print(f" 结果一致性: {'✅' if corr['result_match'] else '❌'}")
print(f" 关联评分: {corr['correlation_score']}/100")
print(f" 通过: {'✅' if corr['pass'] else '❌'}")
print(f"\n{'='*70}")
print("测试效率分析")
print(f"{'='*70}")
eff = sim_engine.efficiency_analysis(variations)
print(f"\n CCRB 变体数: {eff['total_scenarios']}")
print(f" 传统方式: {eff['traditional_hours']} 小时")
print(f" 优化方式: {eff['optimized_hours']} 小时")
print(f" 仿真辅助: {eff['simulation_hours']} 小时")
print(f" 时间节省: {eff['time_saved_pct']}%")
print(f" 效率提升: {eff['efficiency_gain']}x")
print(f"\n{'='*70}")
print("Euro NCAP 2026 四阶段评分架构")
print(f"{'='*70}")
print(f" {'阶段':<25} {'满分':>6} {'最低阈值':>10} {'可补偿':>8}")
print(f" {'Stage 1: 安全驾驶':<25} {'100':>6} {'待公布':>10} {'❌':>8}")
print(f" {'Stage 2: 碰撞避免':<25} {'100':>6} {'待公布':>10} {'❌':>8}")
print(f" {'Stage 3: 碰撞保护':<25} {'100':>6} {'待公布':>10} {'❌':>8}")
print(f" {'Stage 4: 碰撞后安全':<25} {'100':>6} {'待公布':>10} {'❌':>8}")
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