Since prediction plays a significant role in enhancing the performance of decision making and planning procedures, the requirement of advanced methods of prediction becomes urgent. Although many literatures propose methods to make prediction on a single agent, there is still a challenging and open problem on how to make prediction for multi-agent systems. In this work, by leveraging the power of statistics and information theory, we propose a novel deep latent variable model based on Wasserstein auto-encoder, which is able to learn a complex probabilistic distribution. Models such as neural networks cannot guarantee the satisfaction of dynamic system constraints directly. Therefore, we also propose a novel generative model structure to enable our approach to satisfy the kinematic constraints automatically. We test our model on both numerical examples and a real-world application to demonstrate its accuracy and efficiency. The results show that the proposed model achieves a better prediction accuracy than the other state-of-the-art methods under common evaluation metrics. Moreover, we introduce statistics to evaluate if the generative model literally learns the interaction patterns between different agents in the environments.


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    Titel :

    Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Interactive Driving Behavior Prediction


    Beteiligte:
    Ma, Hengbo (Autor:in) / Li, Jiachen (Autor:in) / Zhan, Wei (Autor:in) / Tomizuka, Masayoshi (Autor:in)


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    2390304 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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