3D 乘员姿态估计：OOP 异常姿态检测技术突破

OOP 检测背景

Euro NCAP 2026 OOP 要求：

异常姿态 描述 检测时限
躺卧 乘员躺卧在座椅上 ≤ 10s
探出车外 身体部位探出窗外 ≤ 5s
脚部抬起 脚放在仪表台 ≤ 10s
反向坐姿 面向后方 ≤ 10s

3D 姿态估计方法

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"""
3D 乘员姿态估计

方法：
1. 单目 3D 估计（深度估计）
2. 双目立体视觉
3. ToF 深度相机
4. 多摄像头三角化
"""

import numpy as np

class OccupantPostureEstimator3D:
    """3D 乘员姿态估计器"""
    
    def __init__(self):
        # 身体关键点定义（COCO 17 点）
        self.keypoint_names = [
            "nose", "left_eye", "right_eye", "left_ear", "right_ear",
            "left_shoulder", "right_shoulder", "left_elbow", "right_elbow",
            "left_wrist", "right_wrist", "left_hip", "right_hip",
            "left_knee", "right_knee", "left_ankle", "right_ankle"
        ]
        
        # 骨骼连接
        self.skeleton = [
            (0, 1), (0, 2), (1, 3), (2, 4),  # 头部
            (5, 6),  # 肩膀
            (5, 7), (7, 9),  # 左臂
            (6, 8), (8, 10),  # 右臂
            (5, 11), (6, 12),  # 躯干
            (11, 12),  # 髋部
            (11, 13), (13, 15),  # 左腿
            (12, 14), (14, 16)  # 右腿
        ]
    
    def estimate_pose_3d(self, keypoints_2d: np.ndarray, 
                         depth_map: np.ndarray = None) -> dict:
        """
        估计 3D 姿态
        
        Args:
            keypoints_2d: 2D 关键点 (17, 2)
            depth_map: 深度图（可选）
            
        Returns:
            3D 姿态结果
        """
        # 如果有深度图，直接获取深度
        if depth_map is not None:
            keypoints_3d = self._lift_with_depth(keypoints_2d, depth_map)
        else:
            # 单目 3D 估计
            keypoints_3d = self._lift_from_2d(keypoints_2d)
        
        # 计算姿态角度
        posture_angles = self._calculate_posture_angles(keypoints_3d)
        
        # 判断异常姿态
        oop_status = self._classify_posture(posture_angles)
        
        return {
            "keypoints_3d": keypoints_3d,
            "posture_angles": posture_angles,
            "oop_status": oop_status
        }
    
    def _lift_with_depth(self, kpts_2d: np.ndarray, depth: np.ndarray) -> np.ndarray:
        """使用深度图提升到 3D"""
        kpts_3d = []
        
        for x, y in kpts_2d:
            # 获取深度值
            z = depth[int(y), int(x)] if 0 <= y < depth.shape[0] and 0 <= x < depth.shape[1] else 0
            kpts_3d.append([x, y, z])
        
        return np.array(kpts_3d)
    
    def _lift_from_2d(self, kpts_2d: np.ndarray) -> np.ndarray:
        """单目 3D 估计（简化）"""
        # 使用人体测量学先验
        # 实际实现使用深度学习模型（如 VoxelPose, EpipolarPose）
        
        kpts_3d = np.zeros((len(kpts_2d), 3))
        kpts_3d[:, :2] = kpts_2d
        
        # 假设深度（简化）
        kpts_3d[:, 2] = 1.0  # 1 米
        
        return kpts_3d
    
    def _calculate_posture_angles(self, kpts_3d: np.ndarray) -> dict:
        """计算姿态角度"""
        angles = {}
        
        # 躯干角度（相对垂直线）
        left_shoulder = kpts_3d[5]
        right_shoulder = kpts_3d[6]
        left_hip = kpts_3d[11]
        right_hip = kpts_3d[12]
        
        # 脊柱向量
        spine_top = (left_shoulder + right_shoulder) / 2
        spine_bottom = (left_hip + right_hip) / 2
        spine_vector = spine_top - spine_bottom
        
        # 相对垂直线角度
        vertical = np.array([0, -1, 0])
        trunk_angle = np.arccos(
            np.dot(spine_vector[:2], vertical[:2]) / 
            (np.linalg.norm(spine_vector[:2]) * np.linalg.norm(vertical[:2]) + 1e-6)
        )
        angles["trunk_angle"] = np.degrees(trunk_angle)
        
        # 头部角度
        nose = kpts_3d[0]
        head_vector = nose - spine_top
        head_angle = np.arctan2(head_vector[0], -head_vector[1])
        angles["head_angle"] = np.degrees(head_angle)
        
        # 腿部角度
        left_knee = kpts_3d[13]
        left_ankle = kpts_3d[15]
        leg_vector = left_ankle - left_knee
        leg_angle = np.arctan2(leg_vector[1], leg_vector[2])
        angles["leg_angle"] = np.degrees(leg_angle)
        
        return angles
    
    def _classify_posture(self, angles: dict) -> dict:
        """分类异常姿态"""
        oop_events = []
        
        # 躺卧检测
        if angles.get("trunk_angle", 0) > 45:
            oop_events.append({
                "type": "reclined",
                "severity": "medium",
                "angle": angles["trunk_angle"]
            })
        
        # 头部异常
        if abs(angles.get("head_angle", 0)) > 60:
            oop_events.append({
                "type": "abnormal_head",
                "severity": "low",
                "angle": angles["head_angle"]
            })
        
        return {
            "has_oop": len(oop_events) > 0,
            "events": oop_events
        }


# 测试
if __name__ == "__main__":
    estimator = OccupantPostureEstimator3D()
    
    # 模拟 2D 关键点
    kpts_2d = np.array([
        [500, 200],  # nose
        [480, 180],  # left_eye
        [520, 180],  # right_eye
        [450, 190],  # left_ear
        [550, 190],  # right_ear
        [420, 350],  # left_shoulder
        [580, 350],  # right_shoulder
        [380, 480],  # left_elbow
        [620, 480],  # right_elbow
        [350, 600],  # left_wrist
        [650, 600],  # right_wrist
        [440, 650],  # left_hip
        [560, 650],  # right_hip
        [420, 850],  # left_knee
        [580, 850],  # right_knee
        [400, 1050], # left_ankle
        [600, 1050]  # right_ankle
    ], dtype=np.float32)
    
    result = estimator.estimate_pose_3d(kpts_2d)
    print(f"躯干角度: {result['posture_angles']['trunk_angle']:.1f}°")
    print(f"异常姿态: {result['oop_status']['has_oop']}")

Euro NCAP 合规

OOP 测试场景

场景 检测方法 通过标准
躺卧 躯干角度 > 45° ≤ 10s
脚抬起 脚踝高度 > 50cm ≤ 10s
探出窗外 身体边界超车窗 ≤ 5s

总结

3D OOP 检测关键要点：

  1. 多传感器选择：深度相机 > 双目 > 单目
  2. 姿态角度计算：躯干、头部、腿部
  3. 阈值设置：基于人体测量学
  4. 实时性要求：≤ 10s 检测时间
  5. Euro NCAP 合规：2026 开始要求

参考资料：

  1. Three-Dimensional Posture Estimation (Sensors 2024)
  2. Seeing Machines 3D Cabin Mapping (CES 2026)
  3. Euro NCAP 2026 OOP Protocol