Vehicle trajectory prediction is essential for enabling safety-critical intelligent transportation systems (ITS) applications used in management and operations. While there have been some promising advances in the field, there is a need for modern deep learning algorithms that allow real-time trajectory prediction on embedded IoT devices. This article presents DeepTrack, a novel deep learning algorithm customized for real-time vehicle trajectory prediction and monitoring applications in arterial management, freeway management, traffic incident management, and work zone management for high-speed incoming traffic. In contrast to previous methods, the vehicle dynamics are encoded using Temporal Convolutional Networks (TCNs) to provide more robust time prediction with less computation. DeepTrack also uses depthwise convolution, which reduces the complexity of models compared to existing approaches in terms of model size and operations. Overall, our experimental results demonstrate that DeepTrack achieves comparable accuracy to state-of-the-art trajectory prediction models but with smaller model sizes and lower computational complexity, making it more suitable for real-world deployment.
翻译:车辆轨迹预测对于在管理和作业中使用安全临界智能运输系统(ITS)应用至关重要。虽然在外地取得了一些有希望的进展,但需要现代深层次的学习算法,以便能够对嵌入的IoT装置进行实时轨迹预测。本文章介绍了DeepTrack,这是一部全新的深层次的学习算法,专门为车辆实时轨迹预测设计,并监测在动脉管理、高速公路管理、交通事故管理和高速进港交通工作区管理方面的应用。与以前的方法不同,车辆动态是使用Temal Convolution Network(TCNs)编码的,以便以较少的计算方式提供更稳健的时间预测。DeepTracrack还使用了深度的共变法,它比模型大小和操作的现有方法减少了模型的复杂性。总体而言,我们的实验结果表明,DeepTracrack取得了与最新轨迹预测模型相似的精确度,但模型尺寸较小,计算复杂性较低,因此更适合实际部署。