GAN 数据溯源与影响分析

GAN 版本信息

生成参数

中间层特征分布

模型关注区域

数据增强比例优化

数据增强比例优化参数

初始 GAN 比例：训练轮数：验证集比例：批次大小：

优化结果

最终 GAN 比例：0.75

验证集精度：85.2%

FID Score：25.6

对抗攻击成功率：12.3%

交叉验证结果

平均精度：84.5%

Fold 1：精度：83.2%
Fold 2：精度：85.1%
Fold 3：精度：84.8%

GAN 比例优化历史

模拟图表：显示优化过程中验证集精度的变化

核心指标

0.52

KL 散度

125

Corner Case 数量

0.04

期望校准误差(ECE)

数据库数据展示

视频流预处理实时性

Sensor Data ID: S123, Latency: 0.02 秒
Sensor Data ID: S124, Latency: 0.03 秒
Sensor Data ID: S125, Latency: 0.02 秒

GAN 数据质量监控

模拟图表：显示 GAN 数据质量指标的变化

融合模块集成测试结果

Sensor Data ID: T456, Scene: 晴天, Model: ModelA, Parameter: 0.85 度
Sensor Data ID: T457, Scene: 雨天, Model: ModelB, Parameter: 0.92 度
Sensor Data ID: T458, Scene: 雾天, Model: ModelC, Parameter: 0.78 度

Corner Case 场景覆盖度

Corner Case ID: CC001, Name: 行人突然出现, Risk Level: 高, Severity: 0.95
Corner Case ID: CC002, Name: 车辆违章变道, Risk Level: 中, Severity: 0.82
Corner Case ID: CC003, Name: 恶劣天气, Risk Level: 高, Severity: 0.98

GAN 结构验证

GAN ID: GAN001, GAN Type: CNN, Metric: 准确率, Value: 0.92
GAN ID: GAN002, GAN Type: Transformer, Metric: 精确率, Value: 0.88
GAN ID: GAN003, GAN Type: GAN, Metric: 召回率, Value: 0.95

数据偏见检测

Sensor Data ID: DB789, Data Type: 图像, Bias Type: 光照, Score: 0.75, Method: 统计分析
Sensor Data ID: DB790, Data Type: 视频, Bias Type: 遮挡, Score: 0.88, Method: 深度学习
Sensor Data ID: DB791, Data Type: 激光雷达, Bias Type: 距离, Score: 0.62, Method: 专家评估

数据溯源

Sensor Data ID: DL101, Data Type: 图像, Process: GAN生成, Timestamp: 2024-01-01 10:00:00, Parameters: {'batch_size': 32, 'learning_rate': 0.001}
Sensor Data ID: DL102, Data Type: 视频, Process: 真实采集, Timestamp: 2024-01-01 11:00:00, Parameters: {'camera_id': 'C001', 'location': '北京'}
Sensor Data ID: DL103, Data Type: 激光雷达, Process: 仿真模拟, Timestamp: 2024-01-01 12:00:00, Parameters: {'simulator': 'Carla', 'weather': '晴天'}

SQL 示例

            
                -- 获取核心指标数据
                SELECT
                    (SELECT AVG(metric_value) FROM `autonomous_driving`.`view_gan_data_quality` WHERE metric_name = 'KL散度') AS kl_divergence,
                    (SELECT COUNT(*) FROM `autonomous_driving`.`corner_case_definitions`) AS corner_case_count;

                -- 获取GAN数据质量监控数据
                SELECT
                    metric_name,
                    metric_value
                FROM
                    `autonomous_driving`.`view_gan_data_quality`
                WHERE
                    evaluation_timestamp >= DATE_SUB(NOW(), INTERVAL 1 DAY);

                -- 获取Corner Case 场景覆盖度数据
                SELECT
                    risk_level,
                    COUNT(*) AS count
                FROM
                    `autonomous_driving`.`corner_case_definitions`
                GROUP BY
                    risk_level;

                -- 创建statistics 表
                CREATE TABLE `autonomous_driving`.`statistics` (
                  `id` INT NOT NULL AUTO_INCREMENT,
                  `total_kl_divergence` DECIMAL(10,2) NULL,
                  `total_corner_cases` INT NULL,
                  `last_updated` TIMESTAMP NULL,
                  PRIMARY KEY (`id`));

                -- 创建触发器
                CREATE TRIGGER update_statistics
                AFTER INSERT ON autonomous_driving.model_evaluations
                FOR EACH ROW
                BEGIN
                  UPDATE autonomous_driving.statistics
                  SET total_kl_divergence = (SELECT AVG(metric_value) FROM autonomous_driving.view_gan_data_quality WHERE metric_name = 'KL散度'),
                      total_corner_cases = (SELECT COUNT(*) FROM autonomous_driving.corner_case_definitions),
                      last_updated = NOW()
                  WHERE id = 1;
                END;

                -- 初始数据初始化
                INSERT INTO autonomous_driving.statistics (id) VALUES (1);

API 接口示例（模拟）

            
                // 后端 API 接口（示例）
                app.get('/api/core_metrics', async (req, res) => {
                    try {
                        // 模拟数据库查询
                        const data = {
                            kl_divergence: 0.52,
                            corner_case_count: 125
                        };
                        res.json(data);
                    } catch (error) {
                        console.error(error);
                        res.status(500).json({error: 'Internal Server Error'});
                    }
                });

数据库触发器示例

                
                    -- 创建statistics 表
                    CREATE TABLE `autonomous_driving`.`statistics` (
                      `id` INT NOT NULL AUTO_INCREMENT,
                      `total_kl_divergence` DECIMAL(10,2) NULL,
                      `total_corner_cases` INT NULL,
                      `last_updated` TIMESTAMP NULL,
                      PRIMARY KEY (`id`));

                    -- 创建触发器
                    CREATE TRIGGER update_statistics
                    AFTER INSERT ON autonomous_driving.model_evaluations
                    FOR EACH ROW
                    BEGIN
                      UPDATE autonomous_driving.statistics
                      SET total_kl_divergence = (SELECT AVG(metric_value) FROM autonomous_driving.view_gan_data_quality WHERE metric_name = 'KL散度'),
                          total_corner_cases = (SELECT COUNT(*) FROM autonomous_driving.corner_case_definitions),
                          last_updated = NOW()
                      WHERE id = 1;
                    END;

                    -- 初始数据初始化
                    INSERT INTO autonomous_driving.statistics (id) VALUES (1);