no code implementations • 29 Aug 2023 • JiaMing Wang, Jiqian Dong, Sikai Chen, Shreyas Sundaram, Samuel Labi
In the first component of the framework, we develop a realistic reinforcement learning environment termed "ChargingEnv" which incorporates a reliable charging simulation system that accounts for common practical issues in wireless charging deployment, specifically, the charging panel misalignment.
no code implementations • 28 Aug 2023 • Jiqian Dong, Sikai Chen, Samuel Labi
With ongoing development of autonomous driving systems and increasing desire for deployment, researchers continue to seek reliable approaches for ADS systems.
no code implementations • 11 Oct 2021 • Sikai Chen, Jiqian Dong, Runjia Du, Yujie Li, Samuel Labi
Deep learning (DL) based computer vision (CV) models are generally considered as black boxes due to poor interpretability.
no code implementations • 11 Oct 2021 • Runjia Du, Sikai Chen, Jiqian Dong, Tiantian Chen, Xiaowen Fu, Samuel Labi
To address this question, this study proposes a two-stage model that combines GAQ (Graph Attention Network - Deep Q Learning) and EBkSP (Entropy Based k Shortest Path) using a fog-cloud architecture, to reroute vehicles in a dynamic urban environment and therefore to improve travel efficiency in terms of travel speed.
no code implementations • 11 Oct 2021 • Jiqian Dong, Sikai Chen, Shuya Zong, Tiantian Chen, Mohammad Miralinaghi, Samuel Labi
The results demonstrate the efficacy of our proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with lower computational cost.
1 code implementation • 12 Oct 2020 • Jiqian Dong, Sikai Chen, Paul Young Joun Ha, Yujie Li, Samuel Labi
Connected Autonomous Vehicle (CAV) Network can be defined as a collection of CAVs operating at different locations on a multilane corridor, which provides a platform to facilitate the dissemination of operational information as well as control instructions.
no code implementations • 12 Oct 2020 • Paul Young Joun Ha, Sikai Chen, Jiqian Dong, Runjia Du, Yujie Li, Samuel Labi
In addressing this objective, we duly recognize that one of the main challenges of RL-based CAV controllers is the variety and complexity of inputs that exist in the real world, such as the information provided to the CAV by other connected entities and sensed information.
no code implementations • 30 Sep 2020 • Jiqian Dong, Sikai Chen, Yujie Li, Runjia Du, Aaron Steinfeld, Samuel Labi
From a general perspective, its implementation can provide guidance to connectivity equipment manufacturers and CAV operators, regarding the default CR settings for CAVs or the recommended CR setting in a given traffic environment.
no code implementations • 1 Dec 2018 • Jiqian Dong, Gopaljee Atulya, Kartikeya Bhardwaj, Radu Marculescu
To this end, we propose a new network science- and representation learning-based approach that can quantify economic indicators and visualize the growth of various regions.