Zobrazeno 1 - 10
of 111
pro vyhledávání: '"KE Ruimin"'
Autor:
Azfar, Talha, Ke, Ruimin
Traffic simulations are commonly used to optimize traffic flow, with reinforcement learning (RL) showing promising potential for automated traffic signal control. Multi-agent reinforcement learning (MARL) is particularly effective for learning contro
Externí odkaz:
http://arxiv.org/abs/2412.03925
Autonomous vehicles (AVs) face significant threats to their safe operation in complex traffic environments. Adversarial training has emerged as an effective method of enabling AVs to preemptively fortify their robustness against malicious attacks. Tr
Externí odkaz:
http://arxiv.org/abs/2409.12997
Large Language Models (LLMs), capable of handling multi-modal input and outputs such as text, voice, images, and video, are transforming the way we process information. Beyond just generating textual responses to prompts, they can integrate with diff
Externí odkaz:
http://arxiv.org/abs/2409.09040
Autor:
Zhuang, Yifan, Azfar, Talha, Wang, Yinhai, Sun, Wei, Wang, Xiaokun Cara, Guo, Qianwen Vivian, Ke, Ruimin
Quantum computing, a field utilizing the principles of quantum mechanics, promises great advancements across various industries. This survey paper is focused on the burgeoning intersection of quantum computing and intelligent transportation systems,
Externí odkaz:
http://arxiv.org/abs/2406.00862
Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets, many real-w
Externí odkaz:
http://arxiv.org/abs/2211.05120
Publikováno v:
In Science of the Total Environment 1 May 2024 923
Traffic near-crash events serve as critical data sources for various smart transportation applications, such as being surrogate safety measures for traffic safety research and corner case data for automated vehicle testing. However, there are several
Externí odkaz:
http://arxiv.org/abs/2008.00549
Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting has not yet been fully exploited
Externí odkaz:
http://arxiv.org/abs/2005.11627
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, 2020
Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would r
Externí odkaz:
http://arxiv.org/abs/2001.00269
Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models hav
Externí odkaz:
http://arxiv.org/abs/1903.01678