自動運転車の軌道予測におけるロバスト性に関するサーベイ
A survey on robustness in trajectory prediction for autonomous vehicles
自動運転の軌道予測モデルのロバスト性評価・改善手法を、データ分割・摂動・アーキテクチャ変更・学習後調整の観点から整理したサーベイ論文。
著者: Jeroen Hagenus, Frederik Baymler Mathiesen, Julian F. Schumann, Arkady Zgonnikov
分類: cs.RO
原文アブストラクト
Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. This involves a detailed exploration of various approaches, including data slicing methods, perturbation techniques, model architecture changes, and post-training adjustments. In the literature, we see many promising methods for increasing robustness, which are necessary for safe and reliable autonomous driving.
