no code implementations • 8 Apr 2024 • Liguo Zhou, Yirui Zhou, Huaming Liu, Alois Knoll
Our findings highlight the potential of Residual Chain Loss to revolutionize planning component of autonomous driving systems, marking a significant step forward in the quest for level 5 autonomous driving system.
no code implementations • 22 Feb 2024 • Yanliang Huang, Liguo Zhou, Chang Liu, Alois Knoll
The implementation of Autonomous Driving (AD) technologies within urban environments presents significant challenges.
1 code implementation • 28 Jan 2024 • Liguo Zhou, Yinglei Song, Yichao Gao, Zhou Yu, Michael Sodamin, Hongshen Liu, Liang Ma, Lian Liu, Hao liu, Yang Liu, Haichuan Li, Guang Chen, Alois Knoll
However, the availability of free and open-source simulators is limited, and the installation and configuration process can be daunting for beginners and interdisciplinary researchers.
no code implementations • 26 Oct 2023 • Chang Liu, Liguo Zhou, Yanliang Huang, Alois Knoll
Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation.
no code implementations • 17 May 2023 • Lian Liu, Liguo Zhou
Our method performs well on low-quality and tiny face samples.
no code implementations • 15 Mar 2023 • Liguo Zhou, Tianhao Lin, Alois Knoll
To address the above challenges, based on extensive literature research, this paper analyzes methods for improving and optimizing mainstream object detection algorithms from the perspective of evolution of one-stage and two-stage object detection algorithms.
no code implementations • 12 Mar 2023 • Haichuan Li, Liguo Zhou, Alois Knoll
In this paper, we propose a CNN-based method that overcomes the limitation by establishing feature correlations between regions in sequential images using variants of attention.
no code implementations • 12 Mar 2023 • Haichuan Li, Liguo Zhou, Zhenshan Bing, Marzana Khatun, Rolf Jung, Alois Knoll
Several autonomous driving strategies have been applied to autonomous vehicles, especially in the collision avoidance area.
no code implementations • 23 Feb 2023 • Hanzhen Zhang, Liguo Zhou, Ruining Wang, Alois Knoll
Using real road testing to optimize autonomous driving algorithms is time-consuming and capital-intensive.
no code implementations • 31 Dec 2022 • Wei Cao, Liguo Zhou, Yuhong Huang, Alois Knoll
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive.
no code implementations • 11 Dec 2022 • Rui Song, Liguo Zhou, Lingjuan Lyu, Andreas Festag, Alois Knoll
To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training.
1 code implementation • 1 Apr 2022 • Rui Song, Liguo Zhou, Venkatnarayanan Lakshminarasimhan, Andreas Festag, Alois Knoll
Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i. e., aggregation in layers of roadside units and cloud.