基于视觉注意映射和深度卷积神经网络的图像碰撞风险预测模型

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2021-12-28 DOI:10.1080/19439962.2021.2015731
Chengyu Hu, Wenchen Yang, Chenglong Liu, Rui Fang, Zhongyin Guo, Bijiang Tian
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引用次数: 1

摘要

碰撞风险预测在交通管理和基础设施优化中起着至关重要的作用。以往的研究使用统计方法研究了车祸与多因素之间的关系。由于驾驶员的注意力和环境复杂性对交通安全都有重要影响,本文提出了一种将驾驶员注意力和环境复杂性相结合的前瞻性碰撞风险预测方法。通过社交媒体数据筛选出200多个高风险区域和300多个非事故区域。利用街景地图收集相应的环境信息。采用光谱显著性映射来描述驾驶员对图像的注意分布。采用DeepLabV3预训练网络对环境中的语义特征进行标注。然后将视觉注意模型与图像语义相融合,构造特征向量。采用梯度增强决策树算法分析多类型碰撞数据与特征向量之间的关系。结果表明,该方法检测不同类型碰撞的准确率均在0.81以上。动态对象是影响碰撞可能性和类别的最重要因素。交通信号很容易引起司机的注意,这一点很容易被忽视。该方法为理解交通事故风险提供了新的视角,可以帮助我们更有效地预测不同类型的交通事故。
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An image-based crash risk prediction model using visual attention mapping and a deep convolutional neural network
Abstract Crash risk prediction plays a pivotal role in traffic management and infrastructure optimization. Previous research has studied the relationship between crashes and multiple factors using statistical methods. As both drivers’ attention and environmental complexity substantially affect traffic safety, this article presents a novel method to predict crash risk proactively by combining these two interactive factors. More than 200 high-risk zones and 300 noncrash zones were screened out through social media data. Corresponding environmental information was collected using the street view map. Spectral saliency mapping was applied to depict the driver’s attention distribution toward images. A DeepLabV3 pretrained network was implemented to label the semantic features in the environment. A featured vector was then constructed by fuzing the visual attention model and image semantics. The gradient boosting decision tree algorithm was applied to analyze the relationship between the multitype crash data and featured vectors. The results showed that the accuracy of the proposed method for detecting different types of crashes was over 0.81. Dynamic objects are the most substantial factors that affect crash possibility and categories. Traffic signals are vulnerable to drivers’ attention, which may be easily overlooked. The proposed method provides new insights into understanding traffic crash risk, which can help us predict different types of crashes more effectively.
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CiteScore
6.00
自引率
15.40%
发文量
38
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