Deep Kalman Filter: A Refinement Module for the Rollout Trajectory Prediction Methods

• URL: http://arxiv.org/abs/2102.10859v1
• Date: Mon, 22 Feb 2021 09:47:31 GMT
• Title: Deep Kalman Filter: A Refinement Module for the Rollout Trajectory Prediction Methods
• Authors: Qifan Xue, Xuanpeng Li, Jingwen Zhao, Weigong Zhang
• Abstract summary: Trajectory prediction plays a pivotal role in the field of intelligent vehicles. This paper proposes a parametric-learning Kalman filter based on deep neural network for trajectory prediction.
• Score: 2.7955111755177695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trajectory prediction plays a pivotal role in the field of intelligent vehicles. It currently suffers from several challenges, e.g., accumulative error in rollout process and weak adaptability in various scenarios. This paper proposes a parametric-learning Kalman filter based on deep neural network for trajectory prediction. We design a flexible plug-in module which can be readily implanted into most rollout approaches. Kalman points are proposed to capture the long-term prediction stability from the global perspective. We carried experiments out on the NGSIM dataset. The promising results indicate that our method could improve rollout trajectory prediction methods effectively.

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