Author name cluster

Chen Zhong

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling

Lixiu Wu
Yuanrong Tang
Qisen Pan
Xianyang Zhan
Yuchen Han
Lanxi Xiao
Tianhong Wang
Chen Zhong

Due to privacy concerns, open dialogue datasets for mental health are primarily generated through human or AI synthesis methods. However, the inherent implicit nature of psychological processes, particularly those of clients, poses challenges to the authenticity and diversity of synthetic data. In this paper, we propose ECAs (short for Embodied Conversational Agents), a framework for embodied agent simulation based on Large Language Models (LLMs) that incorporates multiple psychological theoretical principles. Using simulation, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogue data based on high-frequency counseling questions. We validated our framework using the D4 dataset. First, we created a public ECAs dataset through batch simulations based on D4. Licensed counselors evaluated our method, demonstrating that it significantly outperforms baselines in simulation authenticity and necessity. Additionally, two LLM-based automated evaluation methods were employed to confirm the higher quality of the generated dialogues compared to the baselines.

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JBHI Journal 2025 Journal Article

Application and Challenges of Deep Learning in Pulmonary Vessels Segmentation of CTPA Images

Yujie Shi
Xianzi Meng
Xinwei Tang
Chen Zhong
Yuxuan Yang
Dongling Guo
Yong Guo
Jianfeng Wang

Accurate segmentation of pulmonary vessels in medical imaging is critical for the diagnosis of pulmonary vascular diseases (PVDs), particularly in conditions such as chronic thromboembolic pulmonary hypertension (CTEPH), which require detailed vascular mapping. This comprehensive review explores recent advancements in deep learning (DL)-based segmentation techniques for computed tomography pulmonary angiography (CTPA) images, focusing on three primary objectives: (1) to systematically classify network architectures by data dimensionality (2D/3D/2. 5D) and assess their clinical adaptability across varying imaging conditions; (2) to perform quantitative performance comparisons using the standardized Dice Similarity Coefficient (DSC); and (3) to address critical challenges in clinical implementation, including annotation scarcity, computational efficiency versus resolution trade-offs, and model generalization limitations, while proposing innovative mitigation strategies. To this end, we adopted the PRISMA methodology, conducting rigorous searches across Google Scholar and IEEE Xplore databases up to 2024, with manual curation identifying 23 high-quality studies for inclusion. In addition to synthesizing current technological limitations, this review highlights emerging directions such as self-supervised learning and domain adaptation, offering clinicians and AI researchers a structured reference that bridges technical innovation with practical clinical needs

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IROS Conference 2024 Conference Paper

Large Language Models Powered Context-aware Motion Prediction in Autonomous Driving

Xiaoji Zheng
Lixiu Wu
Zhijie Yan
Yuanrong Tang
Hao Zhao 0002
Chen Zhong
Bokui Chen
Jiangtao Gong

Motion prediction is among the most fundamental tasks in autonomous driving. Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks. In this paper, we utilized Large Language Models (LLMs) to enhance the global traffic context understanding for motion prediction tasks. We first conducted systematic prompt engineering, visualizing complex traffic environments and historical trajectory information of traffic participants into image prompts— Transportation Context Map (TC-Map), accompanied by corresponding text prompts. Through this approach, we obtained rich traffic context information from the LLM. By integrating this information into the motion prediction model, we demonstrate that such context can enhance the accuracy of motion predictions. Furthermore, considering the cost associated with LLMs, we propose a cost-effective deployment strategy: enhancing the accuracy of motion prediction tasks at scale with 0. 7% LLM-augmented datasets. Our research offers valuable insights into enhancing the understanding of traffic scenes of LLMs and the motion prediction performance of autonomous driving. The source code is available at https://github.com/AIR-DISCOVER/LLM-Augmented-MTR and https://aistudio.baidu.com/projectdetail/7809548.

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EAAI Journal 2016 Journal Article

A space–time delay neural network model for travel time prediction

Jiaqiu Wang
Ioannis Tsapakis
Chen Zhong

Research on space–time modelling and forecasting has focused on integrating space–time autocorrelation into statistical models to increase the accuracy of forecasting. These models include space–time autoregressive integrated moving average (STARIMA) and its various extensions. However, they are inadequate for the cases when the correlation between data is dynamic and heterogeneous, such as traffic network data. The aim of the paper is to integrate spatial and temporal autocorrelations of road traffic network by developing a novel space–time delay neural network (STDNN) model that capture the autocorrelation locally and dynamically. Validation of the space–time delay neural network is carried out using real data from London road traffic network with 22 links by comparing benchmark models such as Naïve, ARIMA, and STARIMA models. Study results show that STDNN outperforms the Naïve, ARIMA, and STARIMA models in prediction accuracy and has considerable advantages in travel time prediction.

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