POP: 部分観測下での高精度軌道予測による自動運転安全性の向上フレームワーク
Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions
完全な観測で事前学習した教師モデルから、観測が少ない生徒モデルへ特徴蒸留で知識を転移し、部分観測下でも安全な軌道予測を可能にするPOPを提案した。
著者: Sheng Wang, Yingbing Chen, Jie Cheng, Xiaodong Mei, Ren Xin, Yongkang Song, Ming Liu
分類: cs.RO
原文アブストラクト
Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two key stages: self-supervised learning (SSL) and feature distillation. POP first employs SLL to help the model learn to reconstruct history representations, and then utilizes feature distillation as the fine-tuning task to transfer knowledge from the teacher model, which has been pre-trained with complete observations, to the student model, which has only few observations. POP achieves comparable results to top-performing methods in open-loop experiments and outperforms the baseline method in closed-loop simulations, including safety metrics. Qualitative results illustrate the superiority of POP in providing reasonable and safe trajectory predictions.
