Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety by analyzing drivers’ behavior and performance under the cognitive workload in complex environment areas. First, the driving workload of drivers was tested based on traffic signs with different quantities of information. Forty-four drivers were recruited to conduct a traffic sign cognition experiment under static controlled environment conditions. Different complex traffic signs were used for applying the cognitive workload. The static experiment results reveal that workload is highly related to the amount of information on traffic signs and reaction time increases with the information grade, while driving experience and gender effect are not significant. This shows that the cognitive workload of subsequent driving experiments can be controlled by the amount of information on traffic signs. Second, driving characteristics and driving performance were analyzed under different secondary task driving workload levels using a driving simulator. Drivers were required to drive at the required speed on a designed highway off-ramp scene. The cognitive workload was controlled by reading traffic signs with different information, which were divided into four levels. Drivers had to make choices by pushing buttons after reading traffic signs. Meanwhile, the driving performance information was recorded. Questionnaires on objective workload were collected right after each driving task. The results show that speed maintenance and lane deviations are significantly different under different levels of cognitive workload, and the effects of driving experience and gender groups are significant. The research results can be used to analyze traffic safety in highway environments, while considering more drivers’ cognitive and driving performance.

Inappropriate speed in negotiating curves is the primary cause of rollovers and sideslips. In this study, the authors proposed an improved curve speed model considering driving styles, as well as vehicle and road factors. On the basis of a vehicle–road interaction model, the driver behaviour factor was introduced to quantify driving styles of curve speed choices. Firstly, the fuzzy synthetic evaluation method was utilised to classify the driving styles of 30 professional drivers into three different types (i.e. cautious, moderate and aggressive). Secondly, the classification results using fuzzy synthetic evaluation were compared to and verified with the K ‐means clustering method resulting over 60% the similarities. Finally, the proposed curve speed model was built and compared with four existing models. The authors’ model has the following promising advantages: (i) it reflects the speed preferences of three different types of drivers on the premise of driving safety on curves; and (ii) it shows a stationary speed transition when the road adhesion coefficient exceeds 0.8, which indicates that rollover, instead of sideslip, becomes the primary cause for lateral instability crashes on curves. Therefore, this proposed curve speed model could be applied in a curve speed warning system to improve both driving safety and comfort.

Vehicle Group Intelligence (VGI) represents a significant research domain that contributes to the advancement of intelligent transportation systems. This study employs bibliometric approaches to uncover knowledge bases and prevailing research perspectives within the VGI field during the past two decades. We collected 2821 publications from the SCIE and SSCI databases of the Web of Science platform for the literature survey. A visual analysis is conducted from five distinct perspectives (journal, author, year, country, and institution) to reveal the overall distribution of these publications. Two analytical techniques (document co-citation and keyword co-occurrence) are utilized to highlight highly co-cited publications and popular research topics. According to the analysis of the document co-citation, six representative knowledge bases are identified: string stability of vehicle platoons, wireless communication of vehicle platoons, cooperative driving in merging areas, the influence of Cooperative Adaptive Cruise Control (CACC) platoon on traffic flow, platoon management of heavy-duty vehicles, and cooperative control of multi-agent systems. These knowledge bases have provided significant contributions to the exploration and development of the VGI field. Based on the knowledge mapping related to the keyword co-occurrence, the core research trends include five categories: vehicle platooning in different traffic scenarios, cooperative merging of Connected and Automated Vehicles (CAVs), active safety control of vehicle groups, stability analysis of vehicle platoons, and wireless communication of cooperative driving. Furthermore, we illustrate potential research opportunities at four levels, including control architectures, issues in the application of vehicle platoons, cooperative strategies of vehicle groups, and communication protocols and security. The research findings provide valuable guidance for researchers by clarifying the direction of academic exploration in the VGI field.