期刊名称：Vehicles

期刊主页: https://www.mdpi.com/journal/vehicles

2025 年，Vehicles 期刊在智能交通、电动化与车辆安全等方向发表了多篇受到关注的研究成果，主题涵盖大模型交通控制、增材制造设计、高效电机驱动以及交通事故预测等关键领域。本期为您精选了10篇来自 Vehicles 期刊热门论文，希望能为相关领域学者提供新的思路和参考，欢迎阅读！

1. Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

大型语言模型（LLM）作为城市交叉路口交通控制系统：一种新范式

文章链接：https://www.mdpi.com/2624-8921/7/1/11

MDPI 引用格式： Masri, S.; Ashqar, H.I.; Elhenawy, M. Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm. Vehicles 2025, 7, 11.

2. Revolutionizing Automotive Design: The Impact of Additive Manufacturing

汽车设计变革：增材制造的影响

文章链接：https://www.mdpi.com/2624-8921/7/1/24

MDPI 引用格式： Hamza, A.; Bousnina, K.; Dridi, I.; Ben Yahia, N. Revolutionizing Automotive Design: The Impact of Additive Manufacturing. Vehicles 2025, 7, 24.

3. Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis

交通安全中新兴决策方法：智能体协同性能分析

文章链接：https://www.mdpi.com/2624-8921/7/1/4

MDPI 引用格式：Maguire-Day, J.; Al-Rubaye, S.; Warrier, A.; Sen, M.A.; Whitworth, H.; Samie, M. Emerging Decision-Making for Transportation Safety: Collaborative Agent Performance Analysis. Vehicles 2025, 7, 4.

4. Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model

碰撞严重程度分析：基于Transformer模型的人员伤害严重程度预测方法

文章链接：https://www.mdpi.com/2624-8921/7/1/5

MDPI 引用格式：Jiang, Y.; Qu, X.; Zhang, W.; Guo, W.; Xu, J.; Yu, W.; Chen, Y. Analyzing Crash Severity: Human Injury Severity Prediction Method Based on Transformer Model. Vehicles 2025, 7, 5.

5. Tire Wear, Tread Depth Reduction, and Service Life

轮胎磨损、胎纹深度衰减和使用寿命

文章链接：https://www.mdpi.com/2624-8921/7/2/29

MDPI 引用格式：Giechaskiel, B.; Ferrarese, C.; Grigoratos, T. Tire Wear, Tread Depth Reduction, and Service Life. Vehicles 2025, 7, 29.

6. Could Disengagement Reports Indicate Evolution of Autonomous Vehicles?

自动驾驶脱离报告是否预示着自动驾驶汽车的发展方向？

文章链接：https://www.mdpi.com/2624-8921/7/2/32

MDPI 引用格式：Skokan, A.; Mare?ek, J. Could Disengagement Reports Indicate Evolution of Autonomous Vehicles? Vehicles 2025, 7, 32.

7. Navigating Uncertainty: Advanced Techniques in Pedestrian Intention Prediction for Autonomous Vehicles—A Comprehensive Review

应对不确定性：自动驾驶车辆行人意图预测的先进技术—综述

文章链接：https://www.mdpi.com/2624-8921/7/2/57

MDPI 引用格式：Mirzabagheri, A.; Ahmadi, M.; Zhang, N.; Alirezaee, R.; Mozaffari, S.; Alirezaee, S. Navigating Uncertainty: Advanced Techniques in Pedestrian Intention Prediction for Autonomous Vehicles—A Comprehensive Review. Vehicles 2025, 7, 57.

8. Comparison of Machine Learning Models to Predict Nighttime Crash Severity: A Case Study in Tyler, Texas, USA

夜间交通事故严重程度预测的机器学习模型比较：以美国德克萨斯州泰勒市为例

文章链接：https://www.mdpi.com/2624-8921/7/1/20

MDPI 引用格式： Daoud, R.; Vechione, M.; Gurbuz, O.; Sundaravadivel, P.; Tian, C. Comparison of Machine Learning Models to Predict Nighttime Crash Severity: A Case Study in Tyler, Texas, USA. Vehicles 2025, 7, 20.

9. No-Reference Image Quality Assessment with Moving Spectrum and Laplacian Filter for Autonomous Driving Environment

自动驾驶环境中基于动态频谱与拉普拉斯滤波的无参考图像质量评价

文章链接：https://www.mdpi.com/2624-8921/7/1/8

MDPI 引用格式： Nam, W.; Youn, T.; Ha, C. No-Reference Image Quality Assessment with Moving Spectrum and Laplacian Filter for Autonomous Driving Environment. Vehicles 2025, 7, 8.

10. Dynamic Load Optimization of PEMFC Stacks for FCEVs: A Data-Driven Modelling and Digital Twin Approach Using NSGA-II

面向燃料电池汽车的质子交换膜燃料电池堆动态负载优化：基于NSGA-II的数据驱动建模和数字孪生方法

文章链接：https://www.mdpi.com/2624-8921/7/3/96

MDPI 引用格式： Sriram, B.; Shirazi, S.; Kalyvas, C.; Ghassemi, M.; Chizari, M. Dynamic Load Optimization of PEMFC Stacks for FCEVs: A Data-Driven Modelling and Digital Twin Approach Using NSGA-II. Vehicles 2025, 7, 96.

期刊简介

主编：Prof. Dr. Mohammed Chadli, Université Paris-Saclay, France

Vehicles ( ISSN: 2624-8921) 创刊于2019年，最新Imapct Factor 2.2，CiteScore 5.3，是MDPI出版的国际性开放获取期刊，聚焦车辆与交通工程领域，涵盖车辆及其工程系统的设计、建模与仿真、电动汽车、自动驾驶、智能交通、车辆设计及能源优化等研究方向。目前已被Scopus、ESCI (Web of Science)、Ei Compendex、CNKI、DOAJ等数据库收录。