Zobrazeno 1 - 10
of 28 599
pro vyhledávání: '"MA, Jun"'
Decision-making and motion planning are pivotal in ensuring the safety and efficiency of Autonomous Vehicles (AVs). Existing methodologies typically adopt two paradigms: decision then planning or generation then scoring. However, the former often str
Externí odkaz:
http://arxiv.org/abs/2412.04209
This article considers the joint modeling of longitudinal covariates and partly-interval censored time-to-event data. Longitudinal time-varying covariates play a crucial role in obtaining accurate clinically relevant predictions using a survival regr
Externí odkaz:
http://arxiv.org/abs/2412.03042
We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we formulate decision-making and trajectory planning as a differentiable nonl
Externí odkaz:
http://arxiv.org/abs/2412.01234
This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving. The decision making and trajectory planning tasks are jointly formulated as a nonlinear programming problem with an
Externí odkaz:
http://arxiv.org/abs/2411.18974
Autor:
Song, Shezheng, He, Chengxiang, Li, Shasha, Zhao, Shan, Wang, Chengyu, Yan, Tianwei, Li, Xiaopeng, Wan, Qian, Ma, Jun, Yu, Jie, Mao, Xiaoguang
Multimodal large language models (MLLMs) have shown remarkable progress in high-level semantic tasks such as visual question answering, image captioning, and emotion recognition. However, despite advancements, there remains a lack of standardized ben
Externí odkaz:
http://arxiv.org/abs/2412.00060
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