Abstract
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.
Highlights
According to the annual road traffic accident statistics report released by the Traffic Administration Bureau of the Ministry of Public Security of the People’s Republic of China, the number of people who died in traffic accidents in China in 2019 was 62,763 and the number of people injured was 256,101
Recognizing the pedestrian intention is one of the most critical capabilities for autonomous vehicles to ensure the safe operation of the urban environment
For autonomous vehicles, it is quite challenging to accurately identify pedestrians’ crossing intentions, because they are affected by their emotions, traffic environment, road environment, and weather
Summary
According to the annual road traffic accident statistics report released by the Traffic Administration Bureau of the Ministry of Public Security of the People’s Republic of China, the number of people who died in traffic accidents in China in 2019 was 62,763 and the number of people injured was 256,101. Schneemann and Heinemann [12] proposed a pedestrian crossing intention model based on contextual features, using a support vector machine algorithm. It can be seen that the current pedestrian crossing intention recognition is mainly based on data-driven and pedestrian posture feature-driven, and recognition algorithms are traditional supervised learning algorithms. The current pedestrian street crossing intention model is mainly based on supervised learning. An online semisupervised learning model for pedestrians’ crossing intention recognition based on mobile edge computing technology was established. Edge intelligence was employed to acquire and process pedestrian and vehicle data at the edge of the network, and the pedestrian intention recognition result was fed back to the decision-making system of CAVs in time.
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