Enhancing Deep Neural Network-Based Trajectory Prediction: Fine-Tuning and Inherent Movement-Driven Post-Processing
Enhancing Deep Neural Network-Based Trajectory Prediction: Fine-Tuning and Inherent Movement-Driven Post-Processing
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DOI：
10.1061/9780784482872.008
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Daeho Kim;Houtan Jebelli;Sang Hyun Lee;V. Kamat
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文献类型：
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作者：
Daeho Kim;Houtan Jebelli;Sang Hyun Lee;V. Kamat
作为预防建筑中的撞击事故的主动手段，许多研究已经提出了使用无线传感器的邻近监测应用（例如，RFID、UWB和GPS）或计算机视觉方法。大多数先前的研究强调接近检测而不是预测。然而，预测对于接触驱动的事故预防可能更有效和更重要，特别是考虑到工人（例如，设备操作员和步行工人）被告知他们彼此接近，他们就越有可能避免即将发生的碰撞。在早期的研究中，作者提出了一种利用深度神经网络的轨迹预测方法，以研究接近预测在现实世界应用中的可行性。在本研究中，我们提高现有的轨迹预测精度。具体来说，我们通过使用施工数据调整其预训练的权重参数来改进轨迹预测模型。此外，固有的运动驱动的后处理算法的开发，以细化的轨迹预测的目标根据其固有的运动模式，如最终的位置，主要方向，和平均速度。在对现场操作数据的测试中，该方法的准确性得到了提高：在5.28秒的预测时间内，平均位移误差为0.39米，比之前的方法（0.84米）提高了51.43%。改进的轨迹预测方法可以支持提前预测潜在的接触驱动的危险，这可以允许及时的反馈（例如，视觉、声音和振动警报）提供给步行的设备操作员和工人。主动干预可以引导工人迅速采取规避行动，从而减少即将发生碰撞的机会。
As a proactive means of preventing struck-by accidents in construction, many studies have presented proximity monitoring applications using wireless sensors (e.g., RFID, UWB, and GPS) or computer vision methods. Most prior research has emphasized proximity detection rather than prediction. However, prediction can be more effective and important for contact-driven accident prevention, particularly given that the sooner workers (e.g., equipment operators and workers on foot) are informed of their proximity to each other, the more likely they are to avoid the impending collision. In earlier studies, the authors presented a trajectory prediction method leveraging a deep neural network to examine the feasibility of proximity prediction in real-world applications. In this study, we enhance the existing trajectory prediction accuracy. Specifically, we improve the trajectory prediction model by tuning its pre-trained weight parameters with construction data. Moreover, inherent movement-driven post-processing algorithm is developed to refine the trajectory prediction of a target in accordance with its inherent movement patterns such as the final position, predominant direction, and average velocity. In a test on real-site operations data, the proposed approach demonstrates the improvement in accuracy: for 5.28 seconds’ prediction, it achieves 0.39 meter average displacement error, improved by 51.43% as compared with the previous one (0.84 meters). The improved trajectory prediction method can support to predict potential contact-driven hazards in advance, which can allow for prompt feedback (e.g., visible, acoustic, and vibration alarms) to equipment operators and workers on foot. The proactive intervention can lead the workers to take prompt evasive action, thereby reducing the chance of an impending collision.