class NARRATEDataset:
    """NARRATE 数据集结构"""
    
    def __init__(self):
        self.stats = {
            'total_events': 2050,
            'drivers': 35,
            'driver_types': 'experienced drivers + driving instructors',
            'location': 'Australia (public roads)',
            'modalities': ['visual', 'localization', 'motion', 'LiDAR'],
            'annotations': ['action_labels', 'scenario_context', 'SA_spans'],
            'explanations': ['in-vehicle (real-time)', 'post-drive (recall)']
        }
        
        # 场景类别
        self.scenario_categories = {
            'high_level': 6,      # 6 个高层级类别
            'fine_grained': 32    # 32 个细粒度类别
        }
        
        # 情景感知三层结构
        self.sa_levels = {
            'perception': '感知层：识别环境中的人、车、路标',
            'comprehension': '理解层：理解当前场景的含义和风险',
            'projection': '预测层：预测接下来可能发生什么'
        }
    
    def get_benchmark_tasks(self):
        """四个基准任务"""
        return {
            'sa_task': {
                'name': 'Situation Awareness Classification',
                'input': '多模态传感器数据',
                'output': 'Perception/Comprehension/Projection 标签',
                'evaluation': 'F1 score'
            },
            'scenario_task': {
                'name': 'Scenario-Context Recognition',
                'input': '多模态传感器数据',
                'output': '32 个细粒度场景类别',
                'evaluation': 'Accuracy + Macro F1'
            },
            'action_task': {
                'name': 'Driver Action Classification',
                'input': '多模态传感器数据',
                'output': '动作标签',
                'evaluation': 'Accuracy'
            },
            'explanation_task': {
                'name': 'Explanation Generation',
                'input': '多模态传感器数据',
                'output': '自然语言解释',
                'evaluation': 'BLEU + ROUGE + BERTScore'
            }
        }

def generate_sample_event():
    """NARRATE 数据样本示例"""
    
    sample = {
        'event_id': 'NARRATE_0142',
        'driver_id': 'D023',
        'driver_type': 'driving_instructor',
        
        # 传感器数据（同步）
        'visual': 'front_camera_1280x720_30fps',
        'localization': 'GPS_IMU_100Hz',
        'motion': 'vehicle_CAN_bus_100Hz',
        'lidar': '32_channel_360deg_20Hz',
        
        # 标注
        'action_label': 'lane_change_left',
        'scenario_high_level': 'highway_merging',
        'scenario_fine_grained': 'highway_on-ramp_merge_left_lane',
        
        # 情景感知标注（span-level）
        'sa_perception': 'I see a truck in the right lane approaching at higher speed',
        'sa_comprehension': 'The truck is closing the gap, making it unsafe to merge right now',
        'sa_projection': 'If I wait for the truck to pass, I can merge safely behind it',
        
        # 驾驶员解释（实时）
        'in_vehicle_explanation': 'I'm holding position because there's a truck coming up fast on the right. I'll merge after it passes.',
        
        # 驾驶员解释（事后）
        'post_drive_explanation': 'I noticed the truck was going much faster than me, so I decided to wait. Merging before it passed would have been risky because the gap was closing quickly.'
    }
    
    return sample

class DriverIntentPredictor:
    """基于 NARRATE SA 框架的驾驶员意图预测"""
    
    def __init__(self):
        self.sa_levels = ['perception', 'comprehension', 'projection']
        
    def predict_intent(self, sensor_data, driver_state):
        """
        三层 SA 框架用于 IMS 意图预测
        
        Perception: 传感器 + DMS → 环境和驾驶员状态
        Comprehension: 融合理解 → 当前场景风险等级
        Projection: 预测 → 驾驶员接下来 3-5 秒可能行为
        """
        # Perception: 检测环境要素
        perception = {
            'vehicles': self._detect_vehicles(sensor_data),
            'road_features': self._detect_road(sensor_data),
            'driver_gaze': driver_state.get('gaze_direction'),
            'driver_fatigue': driver_state.get('perclos'),
        }
        
        # Comprehension: 理解场景
        comprehension = {
            'risk_level': self._assess_risk(perception),
            'driver_awareness': self._check_awareness(perception, driver_state),
            'scenario_type': self._classify_scenario(perception),
        }
        
        # Projection: 预测行为
        projection = {
            'likely_actions': self._predict_actions(comprehension),
            'time_horizon': '3-5 seconds',
            'confidence': self._estimate_confidence(comprehension),
        }
        
        return projection