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.
Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Interactive Driving Behavior Prediction
2019 IEEE Intelligent Vehicles Symposium (IV) ; 2477-2483
01.06.2019
2390304 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch