no code implementations • 16 Apr 2024 • Yuning Wang, Zhiyuan Liu, Haotian Lin, Junkai Jiang, Shaobing Xu, Jianqiang Wang
In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
no code implementations • 13 Mar 2024 • Zikun Xu, Jianqiang Wang, Shaobing Xu
To this end, this paper proposes UniLiDAR, an occupancy prediction pipeline that leverages geometric realignment and semantic label mapping to facilitate multiple datasets training and mitigate performance degradation during deployment on heterogeneous platforms.
no code implementations • 29 Jun 2023 • Yuning Wang, Zeyu Han, Yining Xing, Shaobing Xu, Jianqiang Wang
Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving.
no code implementations • 7 Jun 2023 • Zeyu Han, Jiahao Wang, Zikun Xu, Shuocheng Yang, Lei He, Shaobing Xu, Jianqiang Wang, Keqiang Li
In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving.
no code implementations • 2 Dec 2020 • Zhong Cao, Shaobing Xu, Songan Zhang, Huei Peng, Diange Yang
This paper proposes a driving-policy adaptive safeguard (DPAS) design, including a collision avoidance strategy and an activation function.
no code implementations • 14 May 2020 • Pingping Lu, Chen Cui, Shaobing Xu, Huei Peng, Fan Wang
AI-based lane detection algorithms were actively studied over the last few years.
no code implementations • 3 Mar 2020 • Lu Wen, Jingliang Duan, Shengbo Eben Li, Shaobing Xu, Huei Peng
The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.