Advanced Intelligent Driving Technology

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1095

Special Issue Editors

Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Interests: automated vehicle test and evaluation; driving behavior and traffic safety
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Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
Interests: autonomous driving; intelligent vehicles; intelligent transportation systems

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Guest Editor
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
Interests: intelligent transportation systems; autonomous public transit systems

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Guest Editor
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
Interests: autonomous systems; risk-aware decision-making; human-centered AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are launching this Special Issue in order to highlight the latest achievements in basic theory and new applications in the field of intelligent driving technology and to further promote intelligent connected vehicle technology and sustainable development and innovation in the automotive industry. This Special Issue will publish high-quality papers in the field of advanced intelligent driving technology. It is our pleasure to invite you to contribute an original research article or a comprehensive review on a pertinent topic for peer review and possible publication.

For this Special Issue, we welcome submissions of original research articles and reviews. Research areas may include (but are not limited to) the following:

  • Safety testing and evaluations of automated vehicles;
  • Driving risk recognition and prediction;
  • Human–machine interaction and co-driving technology in diverse environments;
  • Trust in intelligent driving technology;
  • Environmental perception technology for intelligent connected vehicles;
  • Intelligent decision-making technology for intelligent connected vehicles;
  • Collaborative safety assurance technology for intelligent connected vehicles;
  • Techniques for enhancing transportation safety and accident prevention;
  • The development and deployment of autonomous driving technologies;
  • Operational strategies for cooperative driving and multi-agent systems.

Dr. Penghui Li
Dr. Chao Lv
Dr. Rongge Guo
Dr. Heye Huang
Guest Editors

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Keywords

  • safety testing and evaluation
  • risk recognition and prediction
  • human–machine interaction
  • environmental perception
  • decision-making technology
  • accident prevention

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Published Papers (1 paper)

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Research

26 pages, 24376 KB  
Article
Enhancing Traffic Safety and Efficiency with GOLC: A Global Optimal Lane-Changing Model Integrating Real-Time Impact Prediction
by Jia He, Yanlei Hu, Wen Zhang, Zhengfei Zheng, Wenqi Lu and Tao Wang
Technologies 2025, 13(9), 410; https://doi.org/10.3390/technologies13090410 - 10 Sep 2025
Abstract
Lane-changing maneuvers critically influence traffic flow and safety. This study introduces the Global Optimal Lane-Changing (GOLC) model, a framework that optimizes decisions by quantitatively predicting their systemic effects on surrounding traffic. Unlike traditional models that focus on immediate neighbors, the GOLC model integrates [...] Read more.
Lane-changing maneuvers critically influence traffic flow and safety. This study introduces the Global Optimal Lane-Changing (GOLC) model, a framework that optimizes decisions by quantitatively predicting their systemic effects on surrounding traffic. Unlike traditional models that focus on immediate neighbors, the GOLC model integrates a kinematic wave model to precisely quantify the spatiotemporal impacts on the entire affected platoon, striking a balance between local vehicle actions and global traffic efficiency. Implemented in the Simulation of Urban Mobility (SUMO) environment, the GOLC model is evaluated against benchmark models Minimizing Overall Braking Induced by Lane Changes (MOBIL) and SUMO LC2013. Comparative evaluations demonstrate the GOLC model’s superior performance. In a three-lane scenario, the GOLC model significantly enhances traffic efficiency, reducing average delay by 3.4% to 46.8% compared to MOBIL under medium- to high-flow conditions. It also fosters a safer environment by reducing unnecessary lane changes by 1.1 times compared to the LC2013 model. In incident scenarios, the GOLC model shows greater adaptability, achieving higher average speeds and lower travel times while minimizing speed dispersion and deceleration. These findings validate the effectiveness of embedding macroscopic traffic theory into microscopic driving decisions. The model’s unique strength lies in its ability to predict and minimize the collective negative impact on all affected vehicles, representing a significant step towards real-world implementation in Advanced Driver-Assistance Systems (ADAS) and enhancing safety in next-generation intelligent transportation systems. Full article
(This article belongs to the Special Issue Advanced Intelligent Driving Technology)
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