酒驾检测技术突破：CHI 2025 真实车辆实验与多模态融合

发布时间： 2026-07-08
标签： 酒驾检测, Euro NCAP 2026, DMS, 多模态融合, 实车验证
论文来源： CHI 2025 | NHTSA Report 2024

论文信息

项目 内容
标题 Moving Beyond the Simulator: Interaction-Based Drunk Driving Detection in a Real Vehicle Using Driver Monitoring Cameras and Real-Time Vehicle Data
会议 CHI 2025 (ACM Conference on Human Factors in Computing Systems)
核心贡献 首次在真实车辆验证酒驾检测系统，AUROC = 0.84
创新点 DMC（驾驶员监测摄像头）+ CAN（车辆数据）多模态融合

核心突破：从模拟器到真实车辆

历史挑战： 酒驾检测研究长期依赖模拟器，缺乏真实环境验证

CHI 2025 论文贡献：

  • ✅ 真实车辆实验（非模拟器）
  • ✅ 多模态融合（摄像头 + 车辆 CAN 数据）
  • ✅ 实时检测性能验证
  • ✅ 两种阈值检测：Early Warning（0.05 g/dL）和 Above Limit（0.08 g/dL）

检测性能（论文核心数据）

检测模式 AUROC 标准差 说明
Early Warning (0.05 g/dL) 0.84 ±0.11 预警阈值（轻度损伤）
Above Limit (0.08 g/dL) 0.80 ±0.10 法定阈值（酒驾）
仅 DMC 数据 0.79 ±0.12 单摄像头模态
仅 CAN 数据 0.72 ±0.14 单车辆数据模态
DMC + CAN 融合 0.84 ±0.11 多模态融合最优

关键结论：多模态融合比单一模态提升 5-12% AUROC

多模态融合架构

graph TB
    A[驾驶员监测摄像头 DMC] --> D[特征提取]
    B[车辆 CAN 总线数据] --> D
    
    D --> E[面部特征]
    D --> F[眼动特征]
    D --> G[方向盘行为]
    D --> H[车道保持行为]
    
    E --> I[融合层]
    F --> I
    G --> I
    H --> I
    
    I --> J{损伤判断}
    J -->|Early Warning ≤0.05| K[一级警告]
    J -->|Above Limit ≥0.08| L[二级警告 + 禁止启动]

面部特征检测算法

眼动异常指标

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import numpy as np
from scipy.stats import entropy

class AlcoholImpairmentDetector:
    """
    酒驾损伤检测（基于 CHI 2025 论文方法）
    
    核心指标：
    1. PERCLOS 变化（闭眼比例）
    2. 眼动异常性（熵值）
    3. 瞳孔响应迟缓
    4. 面部表情异常
    """
    
    def __init__(self):
        # 阈值（基于论文实验数据）
        self.early_warning_threshold = 0.05  # g/dL
        self.above_limit_threshold = 0.08  # g/dL
    
    def extract_eye_features(self, 
                              eye_openness: np.ndarray,
                              gaze_points: np.ndarray,
                              pupil_sizes: np.ndarray,
                              fps: int = 30) -> dict:
        """
        提取眼部损伤特征
        
        Args:
            eye_openness: 眼睑开度序列 (0-1), shape=(N,)
            gaze_points: 眼动点序列, shape=(N, 2)
            pupil_sizes: 瞳孔大小序列, shape=(N,)
            fps: 帧率
        
        Returns:
            dict: {
                "perclos": float,  # PERCLOS 值
                "eye_entropy": float,  # 眼动熵
                "pupil_response": float,  # 瞳孔响应
                "blink_frequency": float,  # 眨眼频率
                "impairment_score": float  # 综合损伤得分
            }
        """
        N = len(eye_openness)
        
        # 1. PERCLOS 计算（论文发现：酒精损伤时 PERCLOS 增加）
        threshold = 0.2  # 闭眼阈值
        closed_frames = np.sum(eye_openness < threshold)
        perclos = closed_frames / N
        
        # 2. 眼动熵（酒精损伤导致眼动不规律）
        eye_entropy = self._compute_gaze_entropy(gaze_points)
        
        # 3. 瞳孔响应迟缓（酒精影响瞳孔调节）
        if len(pupil_sizes) > fps * 2:
            # 计算瞳孔大小变化率
            pupil_changes = np.abs(np.diff(pupil_sizes))
            pupil_response = np.mean(pupil_changes) * fps  # 每秒变化率
        else:
            pupil_response = 0.0
        
        # 4. 眨眼频率（酒精损伤时眨眼频率变化）
        blink_events = self._detect_blinks(eye_openness, fps)
        blink_frequency = len(blink_events) / (N / fps)  # Hz
        
        # 5. 综合损伤得分（基于论文权重）
        # 论文发现：PERCLOS 和眼动熵是最强指示指标
        impairment_score = (
            0.35 * perclos +
            0.30 * eye_entropy +
            0.15 * (1.0 - pupil_response) +  # 响应迟缓得分高
            0.20 * np.abs(blink_frequency - 0.25)  # 正常眨眼频率约 0.25 Hz
        )
        
        return {
            "perclos": perclos,
            "eye_entropy": eye_entropy,
            "pupil_response": pupil_response,
            "blink_frequency": blink_frequency,
            "impairment_score": impairment_score
        }
    
    def _compute_gaze_entropy(self, gaze_points: np.ndarray) -> float:
        """计算眼动熵"""
        if len(gaze_points) < 100:
            return 0.0
        
        # 空间网格划分
        grid_size = 8
        grid_x = np.clip((gaze_points[:, 0] * grid_size).astype(int), 0, grid_size - 1)
        grid_y = np.clip((gaze_points[:, 1] * grid_size).astype(int), 0, grid_size - 1)
        grid_cells = grid_x * grid_size + grid_y
        
        # Shannon 熵
        from collections import Counter
        cell_counts = Counter(grid_cells)
        total = len(grid_cells)
        probs = np.array([count / total for count in cell_counts.values()])
        
        return entropy(probs, base=2) / np.log2(grid_size ** 2)
    
    def _detect_blinks(self, 
                       eye_openness: np.ndarray, 
                       fps: int) -> list:
        """检测眨眼事件"""
        threshold = 0.2
        is_closed = eye_openness < threshold
        
        blink_events = []
        in_blink = False
        
        for i, closed in enumerate(is_closed):
            if closed and not in_blink:
                in_blink = True
                blink_start = i
            elif not closed and in_blink:
                blink_end = i
                blink_duration = (blink_end - blink_start) / fps
                if 0.1 < blink_duration < 0.5:  # 正常眨眼持续时间
                    blink_events.append((blink_start, blink_end, blink_duration))
                in_blink = False
        
        return blink_events


# 测试示例
if __name__ == "__main__":
    np.random.seed(42)
    
    detector = AlcoholImpairmentDetector()
    
    # 模拟正常驾驶数据
    N = 900  # 30秒 @ 30fps
    normal_eye_openness = np.random.normal(0.8, 0.05, N)
    normal_eye_openness = np.clip(normal_eye_openness, 0, 1)
    
    normal_gaze = np.random.normal([0.5, 0.5], [0.08, 0.08], (N, 2))
    normal_gaze = np.clip(normal_gaze, 0, 1)
    
    normal_pupil = np.random.normal(0.5, 0.02, N)
    
    # 模拟酒精损伤数据
    impaired_eye_openness = np.random.normal(0.6, 0.15, N)
    impaired_eye_openness = np.clip(impaired_eye_openness, 0, 1)
    
    impaired_gaze = np.random.uniform(0, 1, (N, 2))  # 眼动更随机
    
    impaired_pupil = np.random.normal(0.5, 0.01, N)  # 响应迟缓
    
    print("=== 正常驾驶 ===")
    normal_result = detector.extract_eye_features(
        normal_eye_openness, normal_gaze, normal_pupil
    )
    print(f"PERCLOS: {normal_result['perclos']:.3f}")
    print(f"眼动熵: {normal_result['eye_entropy']:.3f}")
    print(f"瞳孔响应: {normal_result['pupil_response']:.3f}")
    print(f"眨眼频率: {normal_result['blink_frequency']:.2f} Hz")
    print(f"损伤得分: {normal_result['impairment_score']:.3f}")
    
    print("\n=== 酒精损伤 ===")
    impaired_result = detector.extract_eye_features(
        impaired_eye_openness, impaired_gaze, impaired_pupil
    )
    print(f"PERCLOS: {impaired_result['perclos']:.3f}")
    print(f"眼动熵: {impaired_result['eye_entropy']:.3f}")
    print(f"瞳孔响应: {impaired_result['pupil_response']:.3f}")
    print(f"眨眼频率: {impaired_result['blink_frequency']:.2f} Hz")
    print(f"损伤得分: {impaired_result['impairment_score']:.3f}")

CAN 车辆数据融合

方向盘行为异常检测

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import numpy as np
from scipy.signal import find_peaks

class VehicleBehaviorAnalyzer:
    """
    CAN 车辆行为分析（基于 CHI 2025 论文）
    
    酒精损伤驾驶行为特征：
    1. 方向盘修正频率增加
    2. 修正幅度增大
    3. 车道偏离次数增加
    4. 速度控制不稳定
    """
    
    def __init__(self):
        pass
    
    def extract_steering_features(self, 
                                   steering_angle: np.ndarray,
                                   speed: np.ndarray,
                                   lane_position: np.ndarray,
                                   fps: int = 10) -> dict:
        """
        提取方向盘和驾驶行为特征
        
        Args:
            steering_angle: 方向盘转角序列 (度), shape=(N,)
            speed: 速度序列 (km/h), shape=(N,)
            lane_position: 车道位置偏移 (米), shape=(N,)
            fps: CAN 数据采样率
        
        Returns:
            dict: {
                "steering_entropy": float,  # 方向盘熵
                "correction_frequency": float,  # 修正频率
                "correction_magnitude": float,  # 修正幅度
                "lane_deviation_count": int,  # 车道偏离次数
                "speed_variance": float,  # 速度方差
                "behavior_score": float  # 综合行为得分
            }
        """
        N = len(steering_angle)
        duration = N / fps  # 秒
        
        # 1. 方向盘熵（酒精损伤时修正更随机）
        steering_entropy = self._compute_steering_entropy(steering_angle)
        
        # 2. 修正频率（酒精损伤时修正次数增加）
        correction_events = self._detect_steering_corrections(steering_angle)
        correction_frequency = len(correction_events) / duration
        
        # 3. 修正幅度（酒精损伤时幅度更大）
        correction_magnitude = np.mean([e[2] for e in correction_events]) if correction_events else 0.0
        
        # 4. 车道偏离次数（酒精损伤时偏离增加）
        lane_deviation_threshold = 0.3  # 米
        lane_deviations = np.sum(np.abs(lane_position) > lane_deviation_threshold)
        
        # 5. 速度方差（酒精损伤时速度控制不稳）
        speed_variance = np.var(speed)
        
        # 6. 综合行为得分
        behavior_score = (
            0.30 * steering_entropy +
            0.25 * correction_frequency +
            0.20 * correction_magnitude / 10 +  # 归一化
            0.15 * lane_deviations / duration +
            0.10 * speed_variance / 100  # 归一化
        )
        
        return {
            "steering_entropy": steering_entropy,
            "correction_frequency": correction_frequency,
            "correction_magnitude": correction_magnitude,
            "lane_deviation_count": lane_deviations,
            "speed_variance": speed_variance,
            "behavior_score": behavior_score
        }
    
    def _compute_steering_entropy(self, steering_angle: np.ndarray) -> float:
        """计算方向盘熵"""
        # 离散化转角
        bins = np.linspace(-90, 90, num=20)
        digitized = np.digitize(steering_angle, bins)
        
        from collections import Counter
        counts = Counter(digitized)
        total = len(digitized)
        probs = np.array([count / total for count in counts.values()])
        
        from scipy.stats import entropy
        return entropy(probs, base=2) / np.log2(20)
    
    def _detect_steering_corrections(self, 
                                      steering_angle: np.ndarray,
                                      threshold: float = 5.0) -> list:
        """
        检测方向盘修正事件
        
        修正定义：快速反向转动（从左到右或从右到左）
        """
        corrections = []
        
        # 计算转角变化
        changes = np.diff(steering_angle)
        
        # 检测快速反向变化
        for i in range(1, len(changes)):
            prev_change = changes[i-1]
            curr_change = changes[i]
            
            # 反向修正：前一次和当前变化方向相反，且幅度足够大
            if prev_change * curr_change < 0:  # 方向相反
                if abs(prev_change) > threshold and abs(curr_change) > threshold:
                    corrections.append((
                        i - 1, i, max(abs(prev_change), abs(curr_change))
                    ))
        
        return corrections


# 测试示例
if __name__ == "__main__":
    np.random.seed(42)
    
    analyzer = VehicleBehaviorAnalyzer()
    
    # 模拟正常驾驶数据
    N = 300  # 30秒 @ 10fps
    normal_steering = np.random.normal(0, 3, N)
    normal_speed = np.random.normal(60, 2, N)
    normal_lane = np.random.normal(0, 0.05, N)
    
    # 模拟酒精损伤驾驶数据
    impaired_steering = np.random.normal(0, 15, N)  # 更大波动
    impaired_steering += np.sin(np.arange(N) / 10) * 20  # 修正模式
    
    impaired_speed = np.random.normal(55, 8, N)  # 速度不稳定
    
    impaired_lane = np.random.normal(0, 0.25, N)  # 更大偏离
    
    print("=== 正常驾驶 ===")
    normal_result = analyzer.extract_steering_features(
        normal_steering, normal_speed, normal_lane
    )
    print(f"方向盘熵: {normal_result['steering_entropy']:.3f}")
    print(f"修正频率: {normal_result['correction_frequency']:.2f} Hz")
    print(f"修正幅度: {normal_result['correction_magnitude']:.2f} 度")
    print(f"车道偏离: {normal_result['lane_deviation_count']} 次")
    print(f"速度方差: {normal_result['speed_variance']:.2f}")
    print(f"行为得分: {normal_result['behavior_score']:.3f}")
    
    print("\n=== 酒精损伤 ===")
    impaired_result = analyzer.extract_steering_features(
        impaired_steering, impaired_speed, impaired_lane
    )
    print(f"方向盘熵: {impaired_result['steering_entropy']:.3f}")
    print(f"修正频率: {impaired_result['correction_frequency']:.2f} Hz")
    print(f"修正幅度: {impaired_result['correction_magnitude']:.2f} 度")
    print(f"车道偏离: {impaired_result['lane_deviation_count']} 次")
    print(f"速度方差: {impaired_result['speed_variance']:.2f}")
    print(f"行为得分: {impaired_result['behavior_score']:.3f}")

多模态融合决策

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from dataclasses import dataclass

@dataclass
class ImpairmentFeatures:
    """损伤特征"""
    # 眼部特征
    perclos: float
    eye_entropy: float
    pupil_response: float
    
    # 车辆特征
    steering_entropy: float
    correction_frequency: float
    lane_deviation_count: int
    
    # BAC 预测值
    estimated_bac: float

class AlcoholImpairmentFusion:
    """
    酒驾损伤多模态融合（CHI 2025 论文方法）
    
    AUROC: 0.84 (Early Warning)
    """
    
    def __init__(self):
        # 融合权重（基于论文实验）
        self.weights = {
            "eye_perclos": 0.25,
            "eye_entropy": 0.20,
            "pupil_response": 0.10,
            "steering_entropy": 0.20,
            "correction_frequency": 0.15,
            "lane_deviation": 0.10
        }
    
    def predict(self, features: ImpairmentFeatures) -> dict:
        """
        多模态融合预测
        
        Args:
            features: 损伤特征
        
        Returns:
            dict: {
                "bac_estimate": float,  # 估计 BAC (g/dL)
                "early_warning": bool,  # 是否触发预警
                "above_limit": bool,  # 是否超过法定阈值
                "confidence": float
            }
        """
        # 综合损伤得分
        impairment_score = (
            self.weights["eye_perclos"] * features.perclos +
            self.weights["eye_entropy"] * features.eye_entropy +
            self.weights["pupil_response"] * (1.0 - features.pupil_response) +
            self.weights["steering_entropy"] * features.steering_entropy +
            self.weights["correction_frequency"] * features.correction_frequency +
            self.weights["lane_deviation"] * features.lane_deviation_count / 30
        )
        
        # BAC 估计（线性映射，基于论文数据）
        # 论文实验范围：impairment_score 0.2-0.6 对应 BAC 0.0-0.12
        bac_estimate = impairment_score * 0.3  # 简化映射
        
        # 预警判断
        early_warning = bac_estimate >= 0.05  # Early Warning 阈值
        above_limit = bac_estimate >= 0.08  # Above Limit 阈值
        
        # 置信度（基于 AUROC）
        confidence = 0.84 if early_warning else 0.80
        
        return {
            "bac_estimate": bac_estimate,
            "early_warning": early_warning,
            "above_limit": above_limit,
            "confidence": confidence,
            "impairment_score": impairment_score
        }


# 实际测试
if __name__ == "__main__":
    fusion = AlcoholImpairmentFusion()
    
    # 模拟正常驾驶
    normal_features = ImpairmentFeatures(
        perclos=0.08,
        eye_entropy=0.35,
        pupil_response=0.8,
        steering_entropy=0.25,
        correction_frequency=0.05,
        lane_deviation_count=2
    )
    
    # 模拟轻度损伤（BAC ≈ 0.05）
    mild_features = ImpairmentFeatures(
        perclos=0.15,
        eye_entropy=0.50,
        pupil_response=0.6,
        steering_entropy=0.35,
        correction_frequency=0.10,
        lane_deviation_count=5
    )
    
    # 模拟重度损伤（BAC ≈ 0.08+）
    severe_features = ImpairmentFeatures(
        perclos=0.25,
        eye_entropy=0.70,
        pupil_response=0.3,
        steering_entropy=0.55,
        correction_frequency=0.20,
        lane_deviation_count=10
    )
    
    print("=== 正常驾驶 ===")
    normal_result = fusion.predict(normal_features)
    print(f"估计 BAC: {normal_result['bac_estimate']:.3f} g/dL")
    print(f"Early Warning: {normal_result['early_warning']}")
    print(f"Above Limit: {normal_result['above_limit']}")
    
    print("\n=== 轻度损伤 ===")
    mild_result = fusion.predict(mild_features)
    print(f"估计 BAC: {mild_result['bac_estimate']:.3f} g/dL")
    print(f"Early Warning: {mild_result['early_warning']}")
    print(f"Above Limit: {mild_result['above_limit']}")
    
    print("\n=== 重度损伤 ===")
    severe_result = fusion.predict(severe_features)
    print(f"估计 BAC: {severe_result['bac_estimate']:.3f} g/dL")
    print(f"Early Warning: {severe_result['early_warning']}")
    print(f"Above Limit: {severe_result['above_limit']}")

Euro NCAP 2026 & NHTSA 要求

Euro NCAP 2026 DSM 酒驾检测

项目 要求 说明
检测阈值 ≤0.08 g/dL 法定酒驾阈值
检测时间 行程开始后 30秒内 启动检测
响应方式 一级警告 → 二级警告 分级响应
干预措施 建议停车/限制启动 可选功能

NHTSA 要求（美国）

项目 要求 说明
强制性 2026年起新车必须配备 Infrastructure Investment and Jobs Act
检测精度 ≥0.08 g/dL 检测率 >90% 性能要求
可用时间 2025年底供应商需就绪 时间表

IMS 开发启示

传感器配置建议

传感器 用途 参数
IR 摄像头 眼部特征提取 2MP, 30fps, 940nm
RGB 摄像头 面部表情分析 可选，辅助
方向盘传感器 CAN 数据采集 标配
车道摄像头 车道偏离检测 标配

检测流程

sequenceDiagram
    participant A as 车辆启动
    participant B as DMC 摄像头
    participant C as CAN 总线
    participant D as 融合算法
    participant E as 决策模块
    
    A->>B: 启动监测
    A->>C: 采集数据
    B->>D: 眼部特征 (30秒)
    C->>D: 车辆行为 (30秒)
    D->>E: 融合判断
    
    alt BAC < 0.05
        E->>A: 正常驾驶
    else 0.05 ≤ BAC < 0.08
        E->>A: Early Warning
    else BAC ≥ 0.08
        E->>A: Above Limit + 禁止启动
    end

测试场景

场景编号 场景描述 预期 BAC 检测要求
AI-01 正常驾驶 0.0 无警告
AI-02 轻度饮酒 0.03-0.05 Early Warning 可选
AI-03 中度饮酒 0.05-0.08 Early Warning 必需
AI-04 重度饮酒 ≥0.08 Above Limit 必需
AI-05 疲劳 + 酒精 ≥0.08 同时检测疲劳
AI-06 夜间驾驶 ≥0.08 IR 摄像头主导

技术挑战与解决方案

挑战 1：疲劳与酒精症状重叠

问题： 疲劳和酒精损伤都有 PERCLOS 增加、眼动异常

解决方案： 多特征融合区分

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def differentiate_fatigue_alcohol(
    eye_features: dict,
    vehicle_features: dict
) -> dict:
    """
    区分疲劳 vs 酒精损伤
    
    关键区分点：
    - 疲劳：PERCLOS 高 + 方向盘熵低（修正减少）
    - 酒精：PERCLOS 高 + 方向盘熵高（修正增加）
    """
    perclos = eye_features["perclos"]
    steering_entropy = vehicle_features["steering_entropy"]
    
    # 疲劳模式
    if perclos > 0.15 and steering_entropy < 0.35:
        return {"type": "fatigue", "confidence": 0.85}
    
    # 酒精损伤模式
    if perclos > 0.15 and steering_entropy > 0.45:
        return {"type": "alcohol", "confidence": 0.80}
    
    # 混合模式
    if perclos > 0.15 and 0.35 <= steering_entropy <= 0.45:
        return {"type": "mixed", "confidence": 0.70}
    
    return {"type": "normal", "confidence": 0.90}

挑战 2：个体差异基线

问题： 不同驾驶员正常基线差异大

解决方案： 个性化校准

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class PersonalizedAlcoholDetector:
    """个性化基线校准"""
    
    def __init__(self):
        self.baseline = None
    
    def calibrate(self, 
                  normal_eye_data: np.ndarray,
                  normal_vehicle_data: np.ndarray):
        """校准正常驾驶基线"""
        eye_detector = AlcoholImpairmentDetector()
        vehicle_analyzer = VehicleBehaviorAnalyzer()
        
        normal_eye_features = eye_detector.extract_eye_features(
            normal_eye_data[:, 0],  # eye_openness
            normal_eye_data[:, 1:3],  # gaze_points
            normal_eye_data[:, 3]  # pupil_sizes
        )
        
        normal_vehicle_features = vehicle_analyzer.extract_steering_features(
            normal_vehicle_data[:, 0],  # steering
            normal_vehicle_data[:, 1],  # speed
            normal_vehicle_data[:, 2]  # lane_position
        )
        
        self.baseline = {
            "eye": normal_eye_features,
            "vehicle": normal_vehicle_features
        }
    
    def predict_personal(self, 
                         eye_features: dict,
                         vehicle_features: dict) -> dict:
        """个性化预测"""
        if not self.baseline:
            return {"error": "未校准"}
        
        # 相对于基线的变化
        perclos_delta = eye_features["perclos"] - self.baseline["eye"]["perclos"]
        steering_delta = vehicle_features["steering_entropy"] - self.baseline["vehicle"]["steering_entropy"]
        
        # 基于变化的判断
        if perclos_delta > 0.1 and steering_delta > 0.2:
            return {"type": "alcohol", "confidence": 0.85}
        
        if perclos_delta > 0.1 and steering_delta < 0:
            return {"type": "fatigue", "confidence": 0.80}
        
        return {"type": "normal", "confidence": 0.90}

参考资料

  1. CHI 2025 Paper - 真实车辆实验
  2. NHTSA Report 2024
  3. Federal Register 2024
  4. Keshtkaran et al., “Estimating Blood Alcohol Level Through Facial Features”, WACV 2024

总结

CHI 2025 论文核心贡献：

  1. 真实车辆验证：首次在真实环境验证酒驾检测
  2. 多模态融合：DMC + CAN 数据融合，AUROC = 0.84
  3. 双阈值检测：Early Warning (0.05) + Above Limit (0.08)
  4. 实时性能：30秒内完成判断

IMS 开发优先级：

  • 🔴 高：眼部特征提取算法
  • 🔴 高：CAN 数据采集与分析
  • 🟡 中：多模态融合决策
  • 🟢 低：个性化基线校准

下一步行动：

  • 实现眼部特征提取模块
  • 开发 CAN 数据采集接口
  • 构建多模态融合模型
  • 采集真实驾驶数据验证
  • 对齐 Euro NCAP 2026 测试流程
酒驾检测技术突破：CHI 2025 真实车辆实验与多模态融合
https://dapalm.com/2026/07/08/2026-07-08-alcohol-impairment-detection-chi-2025-real-vehicle/
作者
Mars
发布于
2026年7月8日
许可协议