A survey on robustness in trajectory prediction for autonomous vehicles

Autor:	Hagenus, Jeroen, Mathiesen, Frederik Baymler, Schumann, Julian F., Zgonnikov, Arkady
Rok vydání:	2024
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages, 1 figure, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.01397 Zobrazit plný text záznamu View this record from Arxiv