Hierarchical Probabilistic Decision-making for Intelligent Heavy Vehicles in Freight Transportation Networks

被引:0
|
作者
Feng, Qi [1 ]
Hu, Chuan [1 ]
Zhang, Xi [1 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China
来源
IFAC PAPERSONLINE | 2024年 / 58卷 / 10期
基金
中国国家自然科学基金;
关键词
Intelligent Heavy Vehicles (IHVs); autonomous driving; decision-making; reinforcement learning; risk assessment;
D O I
10.1016/j.ifacol.2024.07.352
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Heavy vehicles play an important role in the freight transportation networks, but they are often plagued by traffic accidents due to their dynamic characteristics. Autonomous driving technology is considered promising to improve vehicle driving safety. This work proposes a hierarchical probabilistic lane-changing decision-making framework integrating decision-making and control modules, addressing the stability concerns of heavy vehicles. An improved risk assessment method is proposed, utilizing risk field intensity indicators that consider the characteristics of heavy vehicles. Experiments show that the proposed method can effectively improve driving safety and stability during lane changing. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
引用
收藏
页码:273 / 278
页数:6
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