Browsing by Author "Wang, Zhaoqi"

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    A Fusion Crowd Simulation Method: Integrating Data with Dynamics, Personality with Common
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Mao, Tianlu; Wang, Ji; Meng, Ruoyu; Yan, Qinyuan; Liu, Shaohua; Wang, Zhaoqi; Dominik L. Michels; Soeren Pirk
    This paper proposes a novel crowd simulation method which integrates not only modelling ideas but also advantages from both data-driven methods and crowd dynamics methods. To seamlessly integrate these two different modelling ideas, first, a fusion crowd motion model is developed. In this model the motion of crowd are driven dynamically by different forces. Part of the forces are modeled under a universal interaction mechanism, which describe the common parts of crowd dynamics. Others are modeled by examples from real data, which describe the personality parts of the agent motion. Second, a construction method for example dataset is proposed to support the fusion model. In the dataset, crowd trajectories captured in the real world are decomposed and re-described under the structure of the fusion model. Thus, personality parts hidden in the real data could be locked and extracted, making the data understandable and migratable for our fusion model. A comprehensive crowd motion generation workflow using the fusion model and example dataset is also proposed. Quantitative and qualitative experiments and user studies are conducted. Results show that the proposed fusion crowd simulation method can generate crowd motion with the great motion fidelity, which not only match the macro characteristics of real data, but also has lots of micro personality showing the diversity of crowd motion.
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    A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Chao, Qianwen; Bi, Huikun; Li, Weizi; Mao, Tianlu; Wang, Zhaoqi; Lin, Ming C.; Deng, Zhigang; Benes, Bedrich and Hauser, Helwig
    Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.