Authors: Sheng Wang, Yingbing Chen, Jie Cheng, Xiaodong Mei, Ren Xin, Yongkang Song, Ming Liu

Published on: September 27, 2023

Impact Score: 7.2

Arxiv code: Arxiv:2309.15685

Summary

• What is new: A novel trajectory prediction framework called Partial Observations Prediction (POP) for handling partial observations in congested urban road scenarios.
• Why this is important: The difficulty of making accurate trajectory predictions in autonomous driving due to partial observations.
• What the research proposes: POP utilizes self-supervised learning for history representation reconstruction and feature distillation for knowledge transfer from a pre-trained teacher model to a student model with limited observations.
• Results: Comparable results to leading methods in open-loop experiments and better performance than baseline in closed-loop simulations, particularly in safety metrics.

Technical Details

Technological frameworks used: Partial Observations Prediction (POP)

Models used: Self-supervised learning (SSL) and feature distillation

Data used: nan

Potential Impact

Autonomous driving sector, particularly companies developing or utilizing autonomous driving technology in congested urban areas.

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