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Recursive Least Squares Based Refinement Network for the Rollout Trajectory Prediction Methods

arXiv 2102.10859 ↗First posted 22 Feb 2021cs.LGcited by 0

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Abstract (arXiv)

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 recursive least squares (RLS) estimation based on deep neural network for trajectory prediction. We design a flexible plug-in module which can be readily implanted into rollout approaches. Goal 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 19 references that are in the map. First posted on arXiv 22 Feb 2021.

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References · 13

  1. 2017Attention Is All You Needcited by 60,361
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  6. 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
  7. 2020It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Predictioncited by 236
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  9. 2019Multiple Futures Predictioncited by 178
  10. 2020Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimationcited by 57
  11. 2019Particle Filter Recurrent Neural Networkscited by 36
  12. 2019Differentiable Algorithm Networks for Composable Robot Learningcited by 24
1 more
  1. 2020TPPO: A Novel Trajectory Predictor with Pseudo Oraclecited by 4

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