Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach
Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach
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DOI：
10.3390/ijgi9110652
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Hao Cheng;S. Zourlidou;Monika Sester
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作者：
Hao Cheng;S. Zourlidou;Monika Sester
路口交通管理信息的准确性对城市交通导航和驾驶具有重要意义。然而，由于高成本，这些信息在数字地图中经常缺失、不完整或不是最新的，例如，时间和金钱，用于数据采集和更新。在这项研究中，我们提出了一个众包的方法，利用重量轻的GPS轨道从通勤车辆作为交通管制检测的嵌入式地理信息（VGI）。我们探讨了新的想法，检测交通监管机构通过学习车辆的运动模式在监管的位置。车辆的运动行为被编码的速度剖面的形式，其中速度值和它们的顺序在运动发展过程中被用作一个最常见的交通管制的三个类别的分类问题的特征：交通灯，优先标志和非受控路口。该方法提供了一个平均加权函数和多数表决方案，以容忍错误的VGI数据。序列到序列框架不需要额外的数据处理开销，这使得该方法适用于现实世界的交通调节器检测任务。结果表明，深度学习分类器Conditional Variational Autoencoder可以以90%的准确率预测调节器，优于使用运动汇总统计数据作为特征的随机森林分类器（88%的准确率）。在我们未来的工作中，可以利用图像和增强技术来概括该方法的能力，用于对各种各样的交通管制类进行分类。
Accurate information of traffic regulators at junctions is important for navigating and driving in cities. However, such information is often missing, incomplete or not up-to-date in digital maps due to the high cost, e.g., time and money, for data acquisition and updating. In this study we propose a crowdsourced method that harnesses the light-weight GPS tracks from commuting vehicles as Volunteered Geographic Information (VGI) for traffic regulator detection. We explore the novel idea of detecting traffic regulators by learning the movement patterns of vehicles at regulated locations. Vehicles’ movement behavior was encoded in the form of speed-profiles, where both speed values and their sequential order during movement development were used as features in a three-class classification problem for the most common traffic regulators: traffic-lights, priority-signs and uncontrolled junctions. The method provides an average weighting function and a majority voting scheme to tolerate the errors in the VGI data. The sequence-to-sequence framework requires no extra overhead for data processing, which makes the method applicable for real-world traffic regulator detection tasks. The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. In our future work images and augmentation techniques can be leveraged to generalize the method’s ability for classifying a greater variety of traffic regulator classes.