Browsing by Author "Lv, Yisheng"

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    Detecting Traffic Information From Social Media Texts With Deep Learning Approaches
    (IEEE, 2018-11) Chen, Yuanyuan; Lv, Yisheng; Wang, Xiao; Li, Lingxi; Wang, Fei-Yue; Electrical and Computer Engineering, School of Engineering and Technology
    Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches.
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    Social relation and physical lane aggregator: Integrating social and physical features for multimodal motion prediction
    (Emerald Publishing, 2022-10-11) Chen, Qiyuan; Wei, Zebing; Wang, Xiao; Li, Lingxi; Lv, Yisheng; Electrical and Computer Engineering, School of Engineering and Technology
    Purpose The purpose of this paper aims to model interaction relationship of traffic agents for motion prediction, which is critical for autonomous driving. It is obvious that traffic agents’ trajectories are influenced by physical lane rules and agents’ social interactions. Design/methodology/approach In this paper, the authors propose the social relation and physical lane aggregator for multimodal motion prediction, where the social relations of agents are mainly captured with graph convolutional networks and self-attention mechanism and then fused with the physical lane via the self-attention mechanism. Findings The proposed methods are evaluated on the Waymo Open Motion Dataset, and the results show the effectiveness of the proposed two feature aggregation modules for trajectory prediction. Originality/value This paper proposes a new design method to extract traffic interactions, and the attention mechanism is used in each part of the model to extract and fuse different relational features, which is different from other methods and improves the accuracy of the LSTM-based trajectory prediction method.