import numpy as np
from dataclasses import dataclass
from typing import List, Tuple

@dataclass
class TakeoverEvent:
    """接管事件数据结构"""
    mr_time: float       # 监控请求时间(s)
    tr_time: float       # 接管请求时间(s)
    takeover_time: float # 实际接管时间(s)
    response_time: float # 响应延迟(s)
    ndrt_type: str       # 非驾驶任务类型
    lane_deviation: float # 车道偏差(m)
    speed_deviation: float # 速度偏差(km/h)
    collision: bool      # 是否碰撞

class L3TakeoverAnalyzer:
    """
    L3自动驾驶接管质量分析器
    
    论文: Trans. Safety and Environment, 2026
    核心发现: MR-TR间隔≥8s时接管质量显著提升
    """
    
    # 接管质量评估阈值
    THRESHOLDS = {
        'response_time_good': 2.5,   # <2.5s为良好
        'response_time_acceptable': 4.0,  # <4.0s可接受
        'lane_dev_good': 0.3,  # <0.3m良好
        'lane_dev_critical': 0.75,  # >0.75m危险
        'speed_dev_good': 5,  # <5km/h良好
    }
    
    def analyze_takeover_quality(self, events: List[TakeoverEvent]) -> dict:
        """
        分析不同MR-TR间隔下的接管质量
        
        Args:
            events: 接管事件列表
            
        Returns:
            dict: 按间隔分组的分析结果
        """
        intervals = {}
        
        for event in events:
            mr_tr_gap = event.tr_time - event.mr_time
            
            # 分组
            if mr_tr_gap == 0:
                group = 'no_mr'
            elif mr_tr_gap <= 4:
                group = '4s'
            elif mr_tr_gap <= 8:
                group = '8s'
            else:
                group = '12s'
            
            if group not in intervals:
                intervals[group] = []
            intervals[group].append(event)
        
        # 按组统计
        results = {}
        for group, group_events in intervals.items():
            n = len(group_events)
            
            response_times = [e.response_time for e in group_events]
            lane_devs = [e.lane_deviation for e in group_events]
            speed_devs = [e.speed_deviation for e in group_events]
            collisions = sum(1 for e in group_events if e.collision)
            
            results[group] = {
                'n': n,
                'response_time_mean': np.mean(response_times),
                'response_time_std': np.std(response_times),
                'lane_dev_mean': np.mean(lane_devs),
                'lane_dev_std': np.std(lane_devs),
                'speed_dev_mean': np.mean(speed_devs),
                'collision_rate': collisions / n * 100,
                'quality': self._assess_quality(response_times, lane_devs, collisions)
            }
        
        return results
    
    def _assess_quality(self, response_times, lane_devs, collisions):
        """评估接管质量等级"""
        rt_mean = np.mean(response_times)
        ld_mean = np.mean(lane_devs)
        collision_rate = len(collisions) / len(response_times)
        
        if rt_mean < self.THRESHOLDS['response_time_good'] and \
           ld_mean < self.THRESHOLDS['lane_dev_good'] and \
           collision_rate == 0:
            return 'good'
        elif rt_mean < self.THRESHOLDS['response_time_acceptable'] and \
             ld_mean < self.THRESHOLDS['lane_dev_critical']:
            return 'acceptable'
        else:
            return 'poor'


# 论文核心结果
if __name__ == "__main__":
    print("=== L3接管质量 vs MR-TR间隔 ===\n")
    
    print("| MR-TR间隔 | 响应时间(s) | 车道偏差(m) | 碰撞率 | 质量 |")
    print("|-----------|------------|------------|--------|------|")
    print("| 无MR(0s)  | 4.2±1.3    | 0.52±0.21  | 12.5%  | 差   |")
    print("| 4s        | 3.1±0.9    | 0.38±0.15  | 8.3%   | 可接受|")
    print("| 8s        | 2.3±0.7    | 0.25±0.10  | 0%     | 良好  |")
    print("| 12s       | 2.1±0.6    | 0.22±0.09  | 0%     | 良好  |")
    
    print(f"\n关键发现:")
    print(f"1. MR-TR≥8s: 接管质量良好，碰撞率0%")
    print(f"2. MR-TR=4s: 可接受但有8.3%碰撞风险")
    print(f"3. 无MR: 12.5%碰撞率，响应延迟1.9s")
    print(f"4. 8s和12s差异不显著→8s是最优间隔")
    
    print(f"\nNDRT类型影响:")
    print(f"| NDRT | 恢复时间(s) | 接管质量 |")
    print(f"|------|------------|---------|")
    print(f"| 视频 | 2.5±0.8    | 良好    |")
    print(f"| 阅读 | 3.2±1.1    | 可接受  |")
    print(f"| 手机 | 3.8±1.4    | 差      |")
    
    print(f"\nDMS在L3接管中的角色:")
    print(f"1. MR前: 监控驾驶员NDRT状态")
    print(f"2. MR时: 确认驾驶员是否恢复注意力")
    print(f"3. MR-TR间隔: 持续监控恢复进度")
    print(f"4. TR时: 确认驾驶员准备就绪")
    print(f"5. 接管后: 监控过渡期控制质量")

class DynamicTakeoverManager:
    """
    动态接管管理器
    
    论文发现: 8s是最优MR-TR间隔
    但个体差异大→需要动态调整
    
    策略: 
    1. 发出MR
    2. DMS持续监控恢复进度
    3. 恢复充分→提前TR（减少等待）
    4. 恢复不充分→延迟TR（如果安全允许）或紧急停车
    """
    
    def __init__(self):
        self.min_mr_tr_gap = 4.0  # 最小4秒
        self.max_mr_tr_gap = 12.0  # 最大12秒
        self.optimal_gap = 8.0  # 论文最优值
        
        # 恢复进度指标
        self.recovery_metrics = {
            'gaze_road_ratio': {'weight': 0.30, 'target': 0.7},  # 视线在道路比例
            'gaze_scan_freq': {'weight': 0.25, 'target': 2.0},  # 扫视频率(Hz)
            'hand_on_wheel': {'weight': 0.20, 'target': 1.0},   # 手在方向盘
            'foot_on_pedal': {'weight': 0.15, 'target': 1.0},   # 脚在踏板
            'sit_upright': {'weight': 0.10, 'target': 1.0},     # 坐姿端正
        }
        
    def assess_recovery(self, current_metrics: dict) -> dict:
        """
        评估驾驶员恢复进度
        
        Returns:
            dict: 恢复分数和建议
        """
        score = 0
        for metric, config in self.recovery_metrics.items():
            value = current_metrics.get(metric, 0)
            normalized = min(1.0, value / config['target'])
            score += config['weight'] * normalized
        
        if score >= 0.85:
            return {
                'recovery_level': 'ready',
                'score': score,
                'action': 'can_issue_TR',
                'suggested_delay': 0  # 立即可以TR
            }
        elif score >= 0.6:
            return {
                'recovery_level': 'partial',
                'score': score,
                'action': 'wait',
                'suggested_delay': 2  # 等2秒
            }
        else:
            return {
                'recovery_level': 'not_ready',
                'score': score,
                'action': 'extend_wait_or_emergency_stop',
                'suggested_delay': 4  # 最多再等4秒
            }