Waymo开放数据集挑战赛中的E2E提交错误分析与解决方案
项目地址: https://gitcode.com/gh_mirrors/wa/waymo-open-dataset 概述
Waymo开放数据集端到端（End-to-End，E2E）驾驶挑战赛是自动驾驶领域的重要赛事，参赛者需要基于视觉输入预测车辆的未来轨迹。然而，在提交过程中，许多团队会遇到各种技术问题和错误。本文深入分析E2E提交过程中的常见错误，并提供详细的解决方案。
E2E挑战赛提交流程
常见错误类型及解决方案
1. 文件格式错误
错误现象
- 提交文件无法解析
- 服务器返回"Invalid submission format"错误
根本原因
# 错误的提交文件结构示例
submission = {
"predictions": [...] # 缺少必要的元数据字段
}
# 正确的提交文件结构
submission = wod_e2ed_submission_pb2.E2EDChallengeSubmission(
predictions=predictions,
submission_type=wod_e2ed_submission_pb2.E2EDChallengeSubmission.SubmissionType.E2ED_SUBMISSION,
account_name="your_email@domain.com",
unique_method_name="YourMethodName",
authors=["Author1", "Author2"],
affiliation="YourInstitution",
description="Method description",
method_link="paper_link",
uses_public_model_pretraining=True, # 必须字段
public_model_names=["ModelName"], # 必须字段
num_model_parameters="200K" # 必须字段
)
解决方案
使用官方提供的proto定义确保所有必填字段都已正确设置：
from waymo_open_dataset.protos import end_to_end_driving_submission_pb2 as wod_e2ed_submission_pb2
def create_valid_submission(predictions, config):
"""创建符合格式要求的提交文件"""
submission = wod_e2ed_submission_pb2.E2EDChallengeSubmission()
submission.predictions.extend(predictions)
submission.submission_type = wod_e2ed_submission_pb2.E2EDChallengeSubmission.SubmissionType.E2ED_SUBMISSION
submission.account_name = config['account_email']
submission.unique_method_name = config['method_name']
submission.authors.extend(config['authors'])
submission.affiliation = config['affiliation']
submission.description = config['description']
submission.method_link = config['paper_link']
submission.uses_public_model_pretraining = config['uses_public_model']
submission.public_model_names.extend(config['public_models'])
submission.num_model_parameters = config['model_params']
return submission
2. 轨迹数据格式错误
错误现象
- 轨迹长度不正确
- 坐标值超出合理范围
- 时间戳不匹配
轨迹规范要求
| 参数 | 要求 | 示例值 |
|---|---|---|
| 预测长度 | 5秒，4Hz频率 | 20个航点 |
| 第一个航点时间 | t+0.25秒 | - |
| 最后一个航点时间 | t+5秒 | - |
| 坐标维度 | 仅x,y坐标 | [x1, y1], [x2, y2], ... |
| 坐标单位 | 米 | 浮点数 |
验证代码
def validate_trajectory(trajectory):
"""验证轨迹数据格式"""
errors = []
# 检查航点数量
if len(trajectory.pos_x) != 20 or len(trajectory.pos_y) != 20:
errors.append("轨迹必须包含20个航点")
# 检查坐标范围（合理假设）
if max(trajectory.pos_x) > 100 or min(trajectory.pos_x) < -100:
errors.append("x坐标超出合理范围")
if max(trajectory.pos_y) > 100 or min(trajectory.pos_y) < -100:
errors.append("y坐标超出合理范围")
# 检查是否为NaN值
if any(math.isnan(x) for x in trajectory.pos_x) or any(math.isnan(y) for y in trajectory.pos_y):
errors.append("坐标包含NaN值")
return errors
def create_trajectory_prediction(pos_x, pos_y):
"""创建有效的轨迹预测"""
trajectory = wod_e2ed_submission_pb2.TrajectoryPrediction()
trajectory.pos_x.extend(pos_x.astype(np.float32).tolist())
trajectory.pos_y.extend(pos_y.astype(np.float32).tolist())
return trajectory
3. 帧名称匹配错误
错误现象
- "Frame not found"错误
- 评估结果为空
根本原因
帧名称必须与数据集中的context.name完全匹配：
# 正确获取帧名称
frame_name = data.frame.context.name # 示例: 'segment-1234567890_1234_5678_vehicle_1234'
# 错误的帧名称处理
wrong_name = "segment_1234567890" # 缺少完整上下文信息
解决方案
def extract_frame_names(dataset_path):
"""从数据集中提取所有帧名称"""
frame_names = []
filenames = tf.io.matching_files(dataset_path)
dataset = tf.data.TFRecordDataset(filenames, compression_type='')
for raw_data in dataset:
data = wod_e2ed_pb2.E2EDFrame()
data.ParseFromString(raw_data.numpy())
frame_names.append(data.frame.context.name)
return frame_names
def validate_frame_mapping(predictions, ground_truth_frames):
"""验证预测帧名称与真实帧的匹配"""
missing_frames = []
extra_frames = []
prediction_frames = {pred.frame_name for pred in predictions}
gt_frame_set = set(ground_truth_frames)
missing_frames = gt_frame_set - prediction_frames
extra_frames = prediction_frames - gt_frame_set
return missing_frames, extra_frames
4. 模型元数据错误
必填字段检查表
| 字段 | 要求 | 常见错误 |
|---|---|---|
| uses_public_model_pretraining | 必须为true或false | 未设置或设置为None |
| public_model_names | 使用列表格式 | 字符串而非列表 |
| num_model_parameters | 格式"200K"、"1.2B" | 纯数字或错误格式 |
| submission_type | 必须为E2ED_SUBMISSION | 未设置或错误类型 |
验证函数
def validate_submission_metadata(submission):
"""验证提交元数据"""
errors = []
if not submission.HasField('uses_public_model_pretraining'):
errors.append("uses_public_model_pretraining字段必须设置")
if not submission.public_model_names:
errors.append("public_model_names不能为空")
if not submission.HasField('num_model_parameters'):
errors.append("num_model_parameters字段必须设置")
else:
# 验证参数数量格式
param_str = submission.num_model_parameters
if not re.match(r'^\d+[KMBT]?$', param_str):
errors.append("num_model_parameters格式错误，应为'200K'格式")
if submission.submission_type != wod_e2ed_submission_pb2.E2EDChallengeSubmission.SubmissionType.E2ED_SUBMISSION:
errors.append("submission_type必须设置为E2ED_SUBMISSION")
return errors
5. 文件分片和打包错误
最佳实践
def create_submission_shards(predictions, output_dir, shard_size=1000):
"""创建分片提交文件"""
if not os.path.exists(output_dir):
os.makedirs(output_dir)
num_shards = math.ceil(len(predictions) / shard_size)
submission_files = []
for i in range(num_shards):
start_idx = i * shard_size
end_idx = min((i + 1) * shard_size, len(predictions))
shard_predictions = predictions[start_idx:end_idx]
submission = create_valid_submission(shard_predictions, config)
filename = os.path.join(output_dir, f'part-{i:05d}-of-{num_shards:05d}')
with tf.io.gfile.GFile(filename, 'wb') as f:
f.write(submission.SerializeToString())
submission_files.append(filename)
return submission_files
def package_submission(output_dir, submission_name):
"""打包提交文件"""
import tarfile
tar_path = f"{submission_name}.tar"
with tarfile.open(tar_path, "w") as tar:
tar.add(output_dir, arcname=os.path.basename(output_dir))
# 压缩文件
import gzip
with open(tar_path, 'rb') as f_in:
with gzip.open(f"{tar_path}.gz", 'wb') as f_out:
f_out.write(f_in.read())
return f"{tar_path}.gz"
调试和验证工具
1. 本地验证脚本
def validate_submission_locally(submission_path, dataset_path):
"""本地验证提交文件"""
# 读取提交文件
with tf.io.gfile.GFile(submission_path, 'rb') as f:
submission = wod_e2ed_submission_pb2.E2EDChallengeSubmission()
submission.ParseFromString(f.read())
# 验证元数据
metadata_errors = validate_submission_metadata(submission)
if metadata_errors:
print("元数据错误:", metadata_errors)
return False
# 验证轨迹数据
for i, prediction in enumerate(submission.predictions):
traj_errors = validate_trajectory(prediction.trajectory)
if traj_errors:
print(f"轨迹{i}错误:", traj_errors)
return False
# 验证帧匹配
gt_frames = extract_frame_names(dataset_path)
missing, extra = validate_frame_mapping(submission.predictions, gt_frames)
if missing:
print(f"缺少{len(missing)}个帧的预测")
return False
if extra:
print(f"包含{len(extra)}个额外帧的预测")
return False
print("提交文件验证通过!")
return True
2. 性能基准测试
def benchmark_submission_performance(submission_path, ground_truth_path):
"""基准测试提交性能"""
# 加载提交预测
with tf.io.gfile.GFile(submission_path, 'rb') as f:
submission = wod_e2ed_submission_pb2.E2EDChallengeSubmission()
submission.ParseFromString(f.read())
# 创建预测字典
pred_dict = {}
for pred in submission.predictions:
pred_dict[pred.frame_name] = np.stack([
pred.trajectory.pos_x,
pred.trajectory.pos_y
], axis=1)
# 加载真实数据并计算指标
ade_scores = []
fde_scores = []
filenames = tf.io.matching_files(ground_truth_path)
dataset = tf.data.TFRecordDataset(filenames, compression_type='')
for raw_data in dataset:
data = wod_e2ed_pb2.E2EDFrame()
data.ParseFromString(raw_data.numpy())
frame_name = data.frame.context.name
if frame_name in pred_dict:
gt_traj = np.stack([data.future_states.pos_x, data.future_states.pos_y], axis=1)
pred_traj = pred_dict[frame_name]
# 计算ADE（平均位移误差）
ade = np.mean(np.linalg.norm(pred_traj - gt_traj, axis=1))
ade_scores.append(ade)
# 计算FDE（最终位移误差）
fde = np.linalg.norm(pred_traj[-1] - gt_traj[-1])
fde_scores.append(fde)
return {
'ade_mean': np.mean(ade_scores),
'ade_std': np.std(ade_scores),
'fde_mean': np.mean(fde_scores),
'fde_std': np.std(fde_scores),
'coverage': len(ade_scores) / len(pred_dict)
}
常见问题排查流程
总结
Waymo E2E驾驶挑战赛的提交过程需要严格遵守数据格式和规范要求。通过本文提供的错误分析和解决方案，参赛者可以：
- 避免常见格式错误：使用官方proto定义和验证工具
- 确保数据完整性：验证轨迹长度、坐标范围和帧匹配
- 正确设置元数据：包括模型信息和作者信息
- 本地测试验证：在提交前进行充分的本地测试
遵循这些最佳实践将大大提高提交成功率，让团队能够专注于模型性能的优化而不是技术细节的处理。记住，仔细的预处理和验证是成功提交的关键。
创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
