一个 Bug 让特斯拉撞上假人，仿真平台测试救了 1000 条命_千锋教育

import carla
import random

# 连接仿真器
client = carla.Client('localhost', 2000)
world = client.get_world()

# 生成测试车辆
blueprint_library = world.get_blueprint_library()
vehicle_bp = blueprint_library.filter('model3')[0]
spawn_point = random.choice(world.get_map().get_spawn_points())
vehicle = world.spawn_actor(vehicle_bp, spawn_point)

# 生成假人
walker_bp = blueprint_library.filter('walker.pedestrian.0001')[0]
walker_spawn = carla.Transform(
    carla.Location(x=50, y=0, z=1),
    carla.Rotation(yaw=90)
)
walker = world.spawn_actor(walker_bp, walker_spawn)

# 设置碰撞场景：假人从盲区冲出
walker_control = carla.WalkerControl()
walker_control.speed = 5.0  # 跑步速度
walker_control.direction = carla.Vector3D(1, 0, 0)  # 冲向车辆

# 触发碰撞
for _ in range(100):
    walker.apply_control(walker_control)
    world.tick()
    if vehicle.get_location().distance(walker.get_location()) < 2.0:
        print(f"碰撞发生！距离：{vehicle.get_location().distance(walker.get_location())}")
        break

import time
import numpy as np
from autopilot import AutopilotSystem  # 假设的自动驾驶系统

def test_latency(num_samples=1000):
    latencies = []
    
    for _ in range(num_samples):
        # 模拟传感器数据
        sensor_data = generate_synthetic_data()
        
        # 记录开始时间
        start_time = time.perf_counter()
        
        # 执行自动驾驶决策
        decision = AutopilotSystem.process(sensor_data)
        
        # 记录结束时间
        end_time = time.perf_counter()
        
        # 计算延迟
        latency = (end_time - start_time) * 1000  # 转换为毫秒
        latencies.append(latency)
    
    # 统计分析
    avg_latency = np.mean(latencies)
    p99_latency = np.percentile(latencies, 99)
    max_latency = np.max(latencies)
    
    print(f"平均延迟：{avg_latency:.2f}ms")
    print(f"99分位延迟：{p99_latency:.2f}ms")
    print(f"最大延迟：{max_latency:.2f}ms")
    
    # 性能阈值检查
    if p99_latency > 50:
        print("性能不达标！99%的场景延迟超过50ms")
    else:
        print("性能达标")

test_latency()

import numpy as np
from differential_privacy import LaplaceMechanism

class PrivacyPreservingSensor:
    def __init__(self, epsilon=0.1):
        self.epsilon = epsilon  # 隐私预算，越小越隐私
        self.laplace = LaplaceMechanism(epsilon)
    
    def obfuscate_location(self, true_location):
        # 对真实位置添加拉普拉斯噪声
        noise_x = self.laplace.add_noise(0, 1.0)
        noise_y = self.laplace.add_noise(0, 1.0)
        
        obfuscated_location = (
            true_location[0] + noise_x,
            true_location[1] + noise_y
        )
        return obfuscated_location
    
    def obfuscate_face(self, face_embedding):
        # 对面部特征添加噪声
        noise = np.random.laplace(0, 1.0/self.epsilon, face_embedding.shape)
        return face_embedding + noise

# 测试隐私保护效果
sensor = PrivacyPreservingSensor(epsilon=0.1)
true_location = (39.9042, 116.4074)  # 北京坐标
obfuscated_location = sensor.obfuscate_location(true_location)

print(f"真实位置：{true_location}")
print(f"模糊位置：{obfuscated_location}")
print(f"位置误差：{np.linalg.norm(np.array(true_location) - np.array(obfuscated_location)):.2f}度")

import pytest
from simulation_engine import SimulationEngine

class TestAutopilotRegression:
    def setup_method(self):
        self.engine = SimulationEngine()
        self.engine.load_scenario_library()
    
    @pytest.mark.parametrize("scenario", [
        "white_truck_blue_sky",
        "pedestrian_night_rain",
        "bicycle_occluded_by_truck",
        "construction_zone_dynamic_sign"
    ])
    def test_critical_scenarios(self, scenario):
        # 加载测试场景
        self.engine.load_scenario(scenario)
        
        # 运行仿真
        result = self.engine.run_simulation(duration=60.0)
        
        # 检查是否发生碰撞
        assert result.collisions == 0, f"场景 {scenario} 发生碰撞！"
        
        # 检查性能指标
        assert result.average_latency < 50, f"场景 {scenario} 延迟超标！"
        
        # 检查决策合理性
        assert result.decision_quality > 0.9, f"场景 {scenario} 决策质量不足！"
    
    def teardown_method(self):
        self.engine.cleanup()