Traffic systems play a key role in modern society. However, these systems are increasingly suffering from problems, such as congestions. A well-known way to efficiently reduce this kind of problem is to perform traffic light control intelligently through reinforcement learning (RL) algorithms. In this context, extracting relevant features from the traffic environment to support decision-making becomes a central concern. Examples of such features include vehicle counting on each queue, identification of vehicles’ origins and destinations, among others. Recently, the advent of deep learning has paved to way to efficient methods for extracting some of the aforementioned features. However, the problem of identifying vehicles and their origins and destinations within an intersection has not been fully addressed in the literature, even though such information has shown to play a role in RL-based traffic signal control. Building against this background, in this work we propose a deep learning pipeline for extracting relevant features from intersections based on traffic scenes. Our pipeline comprises three main steps: (i) a YOLO-based object detector fine-tuned using the UAVDT dataset, (ii) a tracking algorithm to keep track of vehicles along their trajectories, and (iii) an origin-destination identification algorithm. Using this pipeline, it is possible to identify vehicles as well as their origins and destinations within a given intersection. In order to assess our pipeline, we evaluated each of its modules separately as well as the pipeline as a whole. The object detector model obtained 98.2% recall and 79.5% precision, on average. The tracking algorithm obtained a MOTA of 72.6% and a MOTP of 74.4%. Finally, the complete pipeline obtained an average error rate of 3.065% in terms of origin and destination counts.

中文翻译：

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用于从交通场景中提取决策支持特征的机器学习管道 1

交通系统在现代社会中发挥着关键作用。然而，这些系统越来越多地受到拥堵等问题的困扰。有效减少此类问题的一种众所周知的方法是通过强化学习（RL）算法智能地执行交通灯控制。在这种背景下，从交通环境中提取相关特征以支持决策成为人们关注的焦点。此类功能的示例包括每个队列的车辆计数、车辆起点和目的地的识别等。最近，深度学习的出现为提取上述一些特征的有效方法铺平了道路。然而，识别交叉路口内车辆及其起点和目的地的问题尚未在文献中得到充分解决，尽管此类信息已被证明在基于强化学习的交通信号控制中发挥着作用。在此背景下，在这项工作中，我们提出了一种深度学习管道，用于根据交通场景从交叉口提取相关特征。我们的流程包括三个主要步骤：(i) 使用 UAVDT 数据集微调的基于 YOLO 的物体检测器，(ii) 跟踪车辆沿其轨迹的跟踪算法，以及 (iii) 起点-目的地识别算法。使用该管道，可以识别给定交叉路口内的车辆及其起点和目的地。为了评估我们的管道，我们分别评估了每个模块以及整个管道。目标检测器模型平均召回率为 98.2%，准确率为 79.5%。跟踪算法获得了72.6%的MOTA和74.4%的MOTP。最终，完整的管道在始发地和目的地计数方面的平均错误率为 3.065%。