1 code implementation • 9 Apr 2023 • Tengfei Xu, Dachuan Liu, Peng Hao, Bo wang
We propose a novel paradigm that provides a unified framework of training neural operators and solving PDEs with the variational form, which we refer to as the variational operator learning (VOL).
no code implementations • 28 Feb 2022 • Zhensong Wei, Xuewei Qi, Zhengwei Bai, Guoyuan Wu, Saswat Nayak, Peng Hao, Matthew Barth, Yongkang Liu, Kentaro Oguchi
The current challenges of this solution are how to effectively combine different perception tasks into a single backbone and how to efficiently learn the spatiotemporal features directly from point cloud sequences.
no code implementations • 19 Jan 2022 • Zhengwei Bai, Peng Hao, Wei Shangguan, Baigen Cai, Matthew J. Barth
However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system.
1 code implementation • 6 Jun 2021 • Qianren Mao, Xi Li, Bang Liu, Shu Guo, Peng Hao, JianXin Li, Lihong Wang
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no code implementations • 24 Jan 2020 • Zhensong Wei, Yu Jiang, Xishun Liao, Xuewei Qi, Ziran Wang, Guoyuan Wu, Peng Hao, Matthew Barth
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system.
no code implementations • 8 Nov 2019 • Zhensong Wei, Chao Wang, Peng Hao, Matthew Barth
Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning.