One of the major challenges that connected autonomous vehicles (CAVs) are facing today is driving in urban environments. To achieve this goal, CAVs need to have the ability to understand the crossing intention of pedestrians. However, for autonomous vehicles, it is quite challenging to understand pedestrians’ crossing intentions. Because the pedestrian is a very complex individual, their intention to cross the street is affected by the weather, the surrounding traffic environment, and even his own emotions. If the established street crossing intention recognition model cannot be updated in real time according to the diversity of samples, the efficiency of human-machine interaction and the interaction safety will be greatly affected. Based on the above problems, this paper established a pedestrian crossing intention model based on the online semisupervised support vector machine algorithm (OS³VM). In order to verify the effectiveness of the model, this paper collects a large amount of pedestrian crossing data and vehicle movement data based on laser scanner, and determines the main feature components of the model input through feature extraction and principal component analysis (PCA). The comparison results of recognition accuracy of SVM, S³VM, and OS³VM indicate that the proposed OS³VM model exhibits a better ability to recognize pedestrian crossing intentions than the SVM and S³VM models, and the accuracy achieves 94.83%. Therefore, the OS³VM model can reduce the number of labeled samples for training the classifier and improve the recognition accuracy.

中文翻译：

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基于移动边缘计算应用的联网自动驾驶人过境意图识别在线半监督学习模型

如今，联网自动驾驶汽车（CAV）面临的主要挑战之一是在城市环境中行驶。为了实现这一目标，CAV需要具备了解行人过路意图的能力。然而，对于自动驾驶汽车而言，要了解行人的过马意图是非常具有挑战性的。由于行人是一个非常复杂的人，他们过马路的意图会受到天气，周围交通环境甚至他自己的情绪的影响。如果不能根据样本的多样性实时更新所建立的过街意图识别模型，将极大地影响人机交互的效率和交互的安全性。基于上述问题，³ VM）。为了验证模型的有效性，本文基于激光扫描仪收集了大量的人行横道数据和车辆行驶数据，并通过特征提取和主成分分析（PCA）确定输入模型的主要特征成分。SVM，S ³ VM和OS ³ VM的识别精度比较结果表明，提出的OS ³ VM模型比SVM和S ³ VM模型具有更好的识别行人过路意图的能力，准确率达到94.83％。因此，OS ³ VM模型可以减少用于训练分类器的标记样本的数量，并提高识别精度。