@inproceedings{2b09a9c1fb3a45c5859894c042b4679e,
title = "Multi-objective optimal decision making for autonomous driving based on multi-modal predicted trajectories",
abstract = "With the advancement of science and technology, the rapid development of the autonomous driving industry has put forward higher requirements for related algorithms. To improve the reliability and intelligence of autonomous vehicles, it is necessary to have robust and reliable decision-making module, which depends on accurate trajectory prediction. In this paper, research on multi-modal trajectory prediction and decision-making for autonomous driving based on deep learning is carried out. Firstly, a multi-modal trajectory prediction model is constructed based on graph neural network to obtain the predicted trajectories of vehicles around the autonomous vehicle. Based on the prediction results, a decision-making network model of the autonomous vehicle is constructed, and the optimal decision-making results satisfying the multi-objective requirements are obtained by combining the multi-objective optimization method. The experimental results verified the feasibility of the method and its good performance.",
keywords = "autonomous driving, decision-making, trajectory prediction",
author = "Yanzhi Lv and Chao Wei",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2023 International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2023 ; Conference date: 15-12-2023 Through 17-12-2023",
year = "2024",
doi = "10.1117/12.3025497",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Yonghe Wei and Fengli Liu",
booktitle = "International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2023",
address = "United States",
}