As the number of technologies implemented in our daily life and rapidly deployed in the transportation system continuously increases, the car-following model is crucial in the transition towards developing an intelligent transportation system. The car-following model adopts different types of sensors to collect driver behaviour for developing road safety and efficient traffic management. This systematic literature review aims to examine previous research to highlight several insights into a number of issues, problems and challenges encountered in research over the last 11 years. Three major databases (i.e. IEEE Xplore®, ScienceDirect and Web of Science) are scanned and surveyed to locate highly cited peer-reviewed studies. Amongst the 714 articles found, only 126 are analysed to map the car-following model research area. Two phases of articles filtration process are implemented on collected articles. The first phase is performed using inclusion criteria like development articles written in English language and development/review articles discussed the integration of car-following model applications in the process of driver behaviour characterisation. Then, the resulted articles from the first phase are included in the second phase for further filtration. The second phase of filtration is achieved using more specific inclusion criteria, i.e. the articles that discuss data acquisition system (DAS) and the integration of car-following model in driver behaviour characterisation. The final set of articles is categorised in three main categories: review articles (5/126), learning-based development articles (16/126) and non-learning-based development articles (105/126). The taxonomy of grouping development studies is created in accordance with the type of dataset used for development. A number of motivational topics have been reported to pursue research development in this area. Recommendations for different stakeholders regarding several valuable points are provided to facilitate and accelerate development in the car-following context. Substantial analysis is performed to identify research gaps in the experimental methodologies applied in the literature. This analysis is conducted on the basis of the methodological aspects of data collection to identify the weaknesses of the current literature. A research map is drawn to provide several insights into potential key points towards the advancement of this research area.

随着我们日常生活中实施的技术的数量不断增加，并在交通运输系统中迅速应用，随着汽车技术的发展，向智能交通系统过渡的过程中至关重要的是汽车跟随模型。跟车模型采用不同类型的传感器来收集驾驶员的行为，以发展道路安全和有效的交通管理。这篇系统的文献综述旨在检查以前的研究，以突出对过去11年研究中遇到的许多问题，问题和挑战的一些见解。扫描并调查了三个主要数据库（即IEEEXplore®，ScienceDirect和Web of Science），以找到被引用次数最多的同行评审研究。在找到的714篇文章中，只有126篇被分析来映射跟车模型研究区域。物品过滤过程分为收集物品的两个阶段。第一阶段使用包含标准来执行，例如用英语编写的开发文章以及讨论驾驶员行为特征化过程中汽车跟随模型应用程序集成的开发/评论文章。然后，将来自第一相的所得制品包括在第二相中以进一步过滤。过滤的第二阶段是使用更具体的纳入标准实现的，即讨论数据采集系统（DAS）的文章以及在驾驶员行为表征中整合跟车模型的文章。最后一组文章分为三个主要类别：评论文章（5/126），基于学习的开发文章（16/126）和基于非学习的开发文章（105/126）。分组开发研究的分类法是根据用于开发的数据集的类型创建的。据报道，许多动机性话题致力于该领域的研究发展。针对不同的利益相关者，针对不同的利益点提出了建议，以促进和加速跟进汽车的发展。进行了实质性分析，以确定文献中采用的实验方法的研究空白。该分析是在数据收集的方法论基础上进行的，以识别当前文献的不足。绘制了一张研究图，以提供一些对这一研究领域发展的潜在关键点的见解。