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Open AccessArticle
An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection
by
Xiaolong Xu
Xiaolong Xu 1,
Xiaolin Shi
Xiaolin Shi 1,*,
Yun Chen
Yun Chen 2,* and
Xu Wu
Xu Wu 1
1
College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China
2
College of Mechatronic Engineering, North Minzu University, Yinchuan 750021, China
*
Authors to whom correspondence should be addressed.
Sensors 2024, 24(19), 6459; https://doi.org/10.3390/s24196459 (registering DOI)
Submission received: 15 August 2024
/
Revised: 30 September 2024
/
Accepted: 5 October 2024
/
Published: 6 October 2024
Abstract
Although deep learning techniques have potential in vehicle behavior prediction, it is difficult to integrate traffic rules and environmental information. Moreover, its black-box nature leads to an opaque and difficult-to-interpret prediction process, limiting its acceptance in practical applications. In contrast, ontology reasoning, which can utilize human domain knowledge and mimic human reasoning, can provide reliable explanations for the speculative results. To address the limitations of the above deep learning methods in the field of vehicle behavior prediction, this paper proposes a front vehicle behavior prediction method that combines deep learning techniques with ontology reasoning. Specifically, YOLOv5s is first selected as the base model for recognizing the brake light status of vehicles. In order to further enhance the performance of the model in complex scenes and small target recognition, the Convolutional Block Attention Module (CBAM) is introduced. In addition, so as to balance the feature information of different scales more efficiently, a weighted bi-directional feature pyramid network (BIFPN) is introduced to replace the original PANet structure in YOLOv5s. Next, using a four-lane intersection as an application scenario, multiple factors affecting vehicle behavior are analyzed. Based on these factors, an ontology model for predicting front vehicle behavior is constructed. Finally, for the purpose of validating the effectiveness of the proposed method, we make our own brake light detection dataset. The accuracy and
[email protected] of the improved model on the self-made dataset are 3.9% and 2.5% higher than that of the original model, respectively. Afterwards, representative validation scenarios were selected for inference experiments. The ontology model created in this paper accurately reasoned out the behavior that the target vehicle would slow down until stopping and turning left. The reasonableness and practicality of the front vehicle behavior prediction method constructed in this paper are verified.
Share and Cite
MDPI and ACS Style
Xu, X.; Shi, X.; Chen, Y.; Wu, X.
An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection. Sensors 2024, 24, 6459.
https://doi.org/10.3390/s24196459
AMA Style
Xu X, Shi X, Chen Y, Wu X.
An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection. Sensors. 2024; 24(19):6459.
https://doi.org/10.3390/s24196459
Chicago/Turabian Style
Xu, Xiaolong, Xiaolin Shi, Yun Chen, and Xu Wu.
2024. "An Ontology-Based Vehicle Behavior Prediction Method Incorporating Vehicle Light Signal Detection" Sensors 24, no. 19: 6459.
https://doi.org/10.3390/s24196459
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