Zobrazeno 1 - 10
of 27 851
pro vyhledávání: '"Ma, Jun"'
Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controlle
Externí odkaz:
http://arxiv.org/abs/2411.07573
Autor:
Li, Xiaopeng, Wang, Shangwen, Li, Shasha, Ma, Jun, Yu, Jie, Liu, Xiaodong, Wang, Jing, Ji, Bin, Zhang, Weimin
Large Language Models for Code (LLMs4Code) have been found to exhibit outstanding performance in the software engineering domain, especially the remarkable performance in coding tasks. However, even the most advanced LLMs4Code can inevitably contain
Externí odkaz:
http://arxiv.org/abs/2411.06638
In the context of autonomous driving, learning-based methods have been promising for the development of planning modules. During the training process of planning modules, directly minimizing the discrepancy between expert-driving logs and planning ou
Externí odkaz:
http://arxiv.org/abs/2411.00476
In this work, we present FRTree planner, a novel robot navigation framework that leverages a tree structure of free regions, specifically designed for navigation in cluttered and unknown environments with narrow passages. The framework continuously i
Externí odkaz:
http://arxiv.org/abs/2410.20230
In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splattin
Externí odkaz:
http://arxiv.org/abs/2410.17084
Road curbs are considered as one of the crucial and ubiquitous traffic features, which are essential for ensuring the safety of autonomous vehicles. Current methods for detecting curbs primarily rely on camera imagery or LiDAR point clouds. Image-bas
Externí odkaz:
http://arxiv.org/abs/2409.20171
Pre-training techniques play a crucial role in deep learning, enhancing models' performance across a variety of tasks. By initially training on large datasets and subsequently fine-tuning on task-specific data, pre-training provides a solid foundatio
Externí odkaz:
http://arxiv.org/abs/2409.20166
Data augmentation is one of the most common tools in deep learning, underpinning many recent advances including tasks such as classification, detection, and semantic segmentation. The standard approach to data augmentation involves simple transformat
Externí odkaz:
http://arxiv.org/abs/2409.20164
Autor:
Li, Xiaopeng, Wang, Shangwen, Song, Shezheng, Ji, Bin, Liu, Huijun, Li, Shasha, Ma, Jun, Yu, Jie
Knowledge editing has emerged as an efficient technology for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there is a lack of effective measures to prevent the malicious misuse of th
Externí odkaz:
http://arxiv.org/abs/2409.19663
Due to the intricate of real-world road topologies and the inherent complexity of autonomous vehicles, cooperative decision-making for multiple connected autonomous vehicles (CAVs) remains a significant challenge. Currently, most methods are tailored
Externí odkaz:
http://arxiv.org/abs/2409.16190