Collecting realistic driving trajectories is crucial for training machine learning models that imitate human driving behavior. Most of today's autonomous driving datasets contain only a few trajectories per location and are recorded with test vehicles that are cautiously driven by trained drivers. In particular in interactive scenarios such as highway merges, the test driver's behavior significantly influences other vehicles. This influence prevents recording the whole traffic space of human driving behavior. In this work, we present a novel methodology to extract trajectories of traffic objects using infrastructure sensors. Infrastructure sensors allow us to record a lot of data for one location and take the test drivers out of the loop. We develop both a hardware setup consisting of a camera and a traffic surveillance radar and a trajectory extraction algorithm. Our vision pipeline accurately detects objects, fuses camera and radar detections and tracks them over time. We improve a state-of-the-art object tracker by combining the tracking in image coordinates with a Kalman filter in road coordinates. We show that our sensor fusion approach successfully combines the advantages of camera and radar detections and outperforms either single sensor. Finally, we also evaluate the accuracy of our trajectory extraction pipeline. For that, we equip our test vehicle with a differential GPS sensor and use it to collect ground truth trajectories. With this data we compute the measurement errors. While we use the mean error to de-bias the trajectories, the error standard deviation is in the magnitude of the ground truth data inaccuracy. Hence, the extracted trajectories are not only naturalistic but also highly accurate and prove the potential of using infrastructure sensors to extract real-world trajectories.

中文翻译：

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从基础设施摄像头和雷达传感器中提取和评估自然人类驾驶轨迹

收集真实的驾驶轨迹对于训练模仿人类驾驶行为的机器学习模型至关重要。当今的大多数自动驾驶数据集每个位置仅包含几条轨迹，并使用由受过训练的驾驶员谨慎驾驶的测试车辆进行记录。特别是在高速公路合并等交互场景中，测试驾驶员的行为会显着影响其他车辆。这种影响阻止记录人类驾驶行为的整个交通空间。在这项工作中，我们提出了一种使用基础设施传感器提取交通对象轨迹的新方法。基础设施传感器允许我们记录一个位置的大量数据，并使测试驾驶员脱离循环。我们开发了由摄像头和交通监控雷达组成的硬件设置以及轨迹提取算法。我们的视觉管道准确检测物体，融合相机和雷达检测并随着时间的推移跟踪它们。我们通过将图像坐标中的跟踪与道路坐标中的卡尔曼滤波器相结合来改进最先进的对象跟踪器。我们表明，我们的传感器融合方法成功地结合了摄像头和雷达检测的优势，并且优于任一单个传感器。最后，我们还评估了轨迹提取管道的准确性。为此，我们为我们的测试车辆配备了差分 GPS 传感器，并用它来收集地面实况轨迹。我们用这些数据计算测量误差。虽然我们使用平均误差来消除轨迹的偏差，误差标准偏差在地面实况数据不准确的范围内。因此，提取的轨迹不仅自然，而且高度准确，证明了使用基础设施传感器提取现实世界轨迹的潜力。