Version 1
: Received: 26 September 2024 / Approved: 26 September 2024 / Online: 26 September 2024 (14:44:48 CEST)
How to cite:
Zhang, Z. Exploring Round Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction. Preprints2024, 2024092107. https://doi.org/10.20944/preprints202409.2107.v1
Zhang, Z. Exploring Round Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction. Preprints 2024, 2024092107. https://doi.org/10.20944/preprints202409.2107.v1
Zhang, Z. Exploring Round Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction. Preprints2024, 2024092107. https://doi.org/10.20944/preprints202409.2107.v1
APA Style
Zhang, Z. (2024). Exploring Round Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction. Preprints. https://doi.org/10.20944/preprints202409.2107.v1
Chicago/Turabian Style
Zhang, Z. 2024 "Exploring Round Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction" Preprints. https://doi.org/10.20944/preprints202409.2107.v1
Abstract
In this paper, we conduct a comprehensive analysis of the RounD dataset to enhance the understanding of motion forecasting for autonomous vehicles in complex roundabout environments. We implement a trajectory prediction framework that combines Long Short-Term Memory (LSTM) networks with graph-based modules to model vehicle interactions. Our primary objective is to assess the generalizability of a standard trajectory prediction model across diverse training and testing datasets. Through extensive experiments, we analyze how varying data distributions, including different road configurations and recording times, influence the model’s prediction accuracy and robustness. This study provides key insights into the challenges of domain generalization in autonomous vehicle trajectory prediction.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
