Traffic crashes remain a leading cause of accidental human death where aggressive driving is a significant contributing factor. To review the driver's performance presented in aggressive driving, this systematic review screens 2412 pieces of relevant literature, selects and synthesizes 31 reports with 34 primary studies that investigated the driver's control performance among the general driver population in four-wheeled passenger vehicles and published with full text in English. These 34 selected studies involved 1731 participants in total. By examining the selected 34 studies, the measures relating to vehicle speed (e.g., mean speed, n = 22), lateral control (e.g., lane deviation, n = 17) and driving errors (e.g., violation of traffic rules, n = 12) were reported most frequently with a significant difference observed between aggressive driving and driving in the control group. The result of the meta-analysis indicates that the aggressive driving behaviour would have 1) a significantly faster speed than the behaviour in the control group with an increase of 5.32 km/h (95% confidence interval, [3.27, 7.37] km/h) based on 8 studies with 639 participants in total; 2) 2.51 times more driving errors (95% confidence interval, [1.32, 3.71] times) than the behaviour in the control group, based on 5 studies with 136 participants in total. This finding can be used to support the identification and quantification of aggressive driving behaviour, which could form the basis of an in-vehicle aggressive driving monitoring system.

A human-like driving model can help to improve the acceptance and safety of automated driving systems (ADS). To improve the performance of human-like driving and interaction with conventional vehicles of ADS, the speed behavior of left-turn vehicles at the signalized intersection was studied using natural driving data. In this study, 374 valid data points of left-turn snippets at signalized intersections were extracted and three phases were introduced based on the reaction behavior of braking, stopping, and accelerating in the left-turn process. Firstly, a one-way ANOVA was used to study the influence of traffic density, traffic light state, intersection type, and left-turn waiting area on the reaction position of each phase and the spatial distribution of the speed. The traffic light state and traffic density were the main significant effects. Furthermore, to analyze the spatial distribution of acceleration, a method of frequency contour was conducted. The butterfly-shaped frequency contour suggested that “the closer to the stop line, the higher the variation of acceleration”. Finally, the driving parameters at each phase were further analyzed. The main results indicate the following: (1) The red traffic light will lead to a larger variation of acceleration, a larger maximum deceleration, a larger starting acceleration, and a larger maximum acceleration. (2) On the condition of dense traffic density, more stops and the duration of the stop–go phase may cause the time pressure, and the driver tends to choose a greater maximum acceleration. (3) The red traffic light leads to a further reaction distance of all three phases, whilst increased traffic density only increases the reaction distance of the stop. (4) Both the dense traffic density and red traffic light lead to an earlier reaction time. The findings can provide a basis for the design of human-like driving of left-turn driving assistance systems and improve the interaction with left-turn conventional vehicles.

Purpose: This study aims to investigate the effects of parasitic-parameter-induced sneak circuits on the stability and bifurcation behavior of the Buck converter, and to overcome the limitations of conventional ideal or weak-parasitic models in describing practical dynamic behavior and stability boundaries. Design/methodology/approach: A three-mode piecewise state-space model is established, where the sneak-circuit mode is represented by an equivalent LCR network. A discrete-time mapping and Filippov monodromy matrix are derived for stability analysis. The bifurcation and stability characteristics of the proposed model are compared with those of the conventional discontinuous conduction mode (DCM) model, and experimental validation is conducted. Findings: Sneak circuits are not simple parasitic disturbances but introduce additional dynamics that reconstruct the cycle-to-cycle mapping. This leads to changes in eigenvalue evolution, bifurcation boundaries and stability regions. The proposed model provides more realistic predictions and shows a delaying effect on complex bifurcations compared with the conventional DCM model. Research limitations/implications: The study focuses on a Buck converter under proportional voltage-mode control and does not consider more complex control strategies or active suppression methods. Future work can extend the analysis to broader operating conditions and advanced control schemes. Practical implications: Designs based solely on conventional DCM models may overestimate the stable operating range. The proposed model provides a more accurate basis for parameter tuning and helps ensure sufficient stability margins in practical applications. Social implications: Improved stability analysis of the Buck converter enhances the reliability of power conversion systems used in processors, data centers and vehicular electronics, reducing the risk of instability and improving system safety. Originality/value: This work integrates sneak-circuit mechanisms into nonlinear stability analysis of the Buck converter. The proposed model reveals how parasitic-induced dynamics reconstruct operating modes and stability boundaries, providing a new perspective and tool for practical converter design.