Mathematical Modeling and Algorithmic Techniques for Engineering

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 1731

Special Issue Editor


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Guest Editor
School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
Interests: engineering artificial intelligence; engineering algorithms; intelligent transportation systems; intelligent medical systems; intelligent signal processing

Special Issue Information

Dear Colleagues,

Currently, mathematical modeling and algorithmic techniques are widely used in many engineering fields. For example, railway engineering applies AI models to detect machinery failures, and to alert distracted/misconducted train operators. Car engineering applies AI models and algorithms to predict behaviors of surrounding vehicles and pedestrians, and to plan driving routes with traffic flows prediction. Bioengineering applies AI models and algorithms to analyze genomic data to identify genetic variations and understand their associations with diseases for personalized medicine.

Engineering AI and algorithms is an old but largely neglected research area, and it has two distinguished characteristics. (1) It applies and optimizes existing AI models and algorithms to solve practical engineering problems, essentially to explore beyond theoretical studies. (2) To efficiently and effectively solve an engineering problem, it usually surpasses the boundaries of various AI models and algorithms. For example, it may use a natural language processing model to solve a computer vision-related problem.

This Special Issue “Mathematical Modeling and Algorithmic Techniques for Engineering” aims to further advance the studies on engineering AI and algorithms, and invites manuscripts that explore applications of AI models and algorithms in various engineering and practical fields. The best-fitted manuscripts would include at least one of the following contents: novel AI models or algorithms, innovative applications, and discussions about new engineering problems. Manuscripts about transferring AI models or algorithms from a specific field to a different one are highly recommended.

Dr. Ergude Bao
Guest Editor

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Keywords

  • engineering artificial intelligence
  • engineering algorithms
  • intelligent transportation systems
  • intelligent medical systems
  • intelligent signal processing

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Published Papers (1 paper)

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Research

13 pages, 1190 KiB  
Article
DBN-MACTraj: Dynamic Bayesian Networks for Predicting Combinations of Long-Term Trajectories with Likelihood for Multiple Agents
by Haonan Cui, Haolun Qi and Jianyu Zhou
Mathematics 2024, 12(23), 3674; https://doi.org/10.3390/math12233674 - 23 Nov 2024
Viewed by 1116
Abstract
Accurately predicting the long-term trajectories of agents in complex traffic environments is crucial for the safety and effectiveness of autonomous driving systems. This paper introduces DBN-MACTraj, a probabilistic model that takes historical trajectories and surrounding lane information as inputs to generate a distribution [...] Read more.
Accurately predicting the long-term trajectories of agents in complex traffic environments is crucial for the safety and effectiveness of autonomous driving systems. This paper introduces DBN-MACTraj, a probabilistic model that takes historical trajectories and surrounding lane information as inputs to generate a distribution of predicted trajectory combinations for all agents. DBN-MACTraj consists of two main components: a single-agent probabilistic model and a multi-agent risk-averse sampling algorithm. The single-agent model utilizes a dynamic Bayesian network, which incorporates the driver’s maneuvering decisions along with information about surrounding lanes. The multi-agent sampling algorithm simultaneously generates predictions for all agents, using a risk potential field model to filter out samples that may lead to traffic accidents. Ultimately, this results in a probability distribution of the combinations of long-term trajectories. Experimental evaluations on the nuScenes dataset demonstrate that DBN-MACTraj delivers competitive performance in trajectory prediction compared to other state-of-the-art approaches. Full article
(This article belongs to the Special Issue Mathematical Modeling and Algorithmic Techniques for Engineering)
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