Search Results for author: Haotian Shi

Found 6 papers, 0 papers with code

Beacon-enabled TDMA Ultraviolet Communication Network System Design and Realization

no code implementations6 Dec 2023 Yuchen Pan, Fei Long, Ping Li, Haotian Shi, Jiazhao Shi, Hanlin Xiao, Chen Gong, Zhengyuan Xu

Nonline of sight (NLOS) ultraviolet (UV) scattering communication can serve as a good candidate for outdoor optical wireless communication (OWC) in the cases of non-perfect transmitter-receiver alignment and radio silence.

Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms

no code implementations4 Dec 2023 Hongjie Liu, Haotian Shi, Sicheng Fu, Tengfei Yuan, Xinhuan Zhang, Hongzhe Xu, Bin Ran

This study presents a bus travel feature extraction method rooted in Point of Interest (POI) data, employing enhanced P-KMENAS and P-LDA algorithms to overcome these limitations.

Graph-Based Interaction-Aware Multimodal 2D Vehicle Trajectory Prediction using Diffusion Graph Convolutional Networks

no code implementations5 Sep 2023 Keshu Wu, Yang Zhou, Haotian Shi, Xiaopeng Li, Bin Ran

Within this framework, vehicles' motions are conceptualized as nodes in a time-varying graph, and the traffic interactions are represented by a dynamic adjacency matrix.

Graph Embedding Intent Detection +1

A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

no code implementations16 Aug 2023 Qinghui Nie, Jishun Ou, Haiyang Zhang, Jiawei Lu, Shen Li, Haotian Shi

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers.

Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach

no code implementations3 Dec 2020 Haotian Shi, Yang Zhou, Keshu Wu, Xin Wang, Yangxin Lin, Bin Ran

This paper proposes a cooperative strategy of connected and automated vehicles (CAVs) longitudinal control for partially connected and automated traffic environment based on deep reinforcement learning (DRL) algorithm, which enhances the string stability of mixed traffic, car following efficiency, and energy efficiency.

reinforcement-learning Reinforcement Learning (RL)

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