A comprehensive review of technologies and approaches for active safety systems designed to reduce ground vehicle crashes, as well as the associated severity of injuries and fatalities, is provided. Active safety systems are commonly referred to as systems that can forewarn a driver of a potential safety hazard, or automatically intervene to reduce the likelihood of an accident without requiring driver intervention. The data from naturalistic drivers has shown that such systems are instrumental in improving vehicle safety in various conditions, particularly at higher speeds and under adverse road conditions. The increased integration of sensors, electronics, and real-time processing capabilities has served as one of the critical enabling elements in the widespread integration of active safety systems in modern vehicles. The emphasis is placed on control approaches for active safety systems and their progression over the years from antilock brakes to more advanced technologies that have nearly enabled semiautonomous driving. A review of key active safety control approaches for antilock braking, yaw stability, traction control, roll stability, and various collision avoidance systems is provided.

This study aims to comprehensively assess the lubrication effect of leaves on wheel–rail contact dynamics using the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig, which closely simulates field conditions with precision and repeatability. Railway operators grapple with the seasonally recurring challenge of leaf contamination, which can cause partial loss of braking and lead to undesired events such as station overruns. Better understanding the adhesion-reducing impact of leaf contamination significantly improves railway engineering practices to counter their effects on train braking and traction. This experimental study evaluates the reduction in traction and braking forces (collectively called “adhesion”) as a function of leaf volume, using two leaf species that commonly grow along U.S. railroad tracks. The test methods rely on the chosen leaves’ transpiration characteristics while ensuring the result’s reproducibility. Leaves were symmetrically positioned on the wheel surface, centered around the mid-rib area within the wear band, and taped on the edges far from the wear band. The critical test parameters (i.e., wheel load, wheel velocity, and percentage creepage) are kept constant among the tests. At the same time, leaf volume is reduced from a maximum amount that covers the entire wheel surface (100% coverage) to no leaves (0%). The latter is used as the baseline. The percentage creepage is kept constant at an exaggerated amount of 2% to accelerate the test time. The results indicate a nonlinear relationship between leaf volume and the loss of braking. Even a small amount of leaf contamination causes a significant reduction in adhesion by as much as 50% compared with no contamination (i.e., baseline). Increasing leaf volume results in contact saturation, beyond which adhesion is not reduced. The minimum adhesion observed in this study is 20% of the maximum adhesion that occurs when no leaf contamination is present.

This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance.

A laboratory evaluation of top-of-rail friction modifiers (TORFM) is conducted to evaluate their effect on wear and adhesion, in settings that are closer to field conditions. The study, which uses the Virginia Tech-Federal Railroad Administration (VT-FRA) roller rig, extends past tribological studies with twin disks. Separate tests are performed in two phases. In Phase 1, the effect of TORFM volume, ranging from light to heavy applications (about 1 to 25 μm layer thickness), is evaluated on wheel wear. The normalized results indicate that light applications of TORFM yield the most favourable wear-to-cost ratio. In Phase 2, TORFM effectiveness duration is evaluated by applying the material in multiple cycles and monitoring the time-lapse for adhesion to recover to the unlubricated condition. Repeated applications of TORFM in moderate amounts (5 μm layer thickness) could lead to a stick-slip phenomenon that could not only diminish adhesion but also lead to surface markings on the rail.

A statistical analysis of a large amount of data from experiments conducted on the Virginia Tech-Federal Railroad Administration (VT-FRA) roller rig under various field-emulated conditions is performed to develop multiple regression models for longitudinal and lateral tractions. The experiment-based models are intended to be an alternative to the classical wheel-rail contact models that have been available for decades. The VT-FRA roller rig data is used to develop parametric regression models that efficiently capture the relationship between traction and the combined effects of the influential variables. Single regression models for representing the individual effect of wheel load, creepage, and angle of attack on longitudinal and lateral traction were investigated by the authors in an earlier study. This study extends single regression models to multiple regression models and assesses the interaction among the variables using model selection approaches. The multiple-regression models are then compared with CONTACT, a well-known modelling tool for contact dynamics, in terms of prediction accuracy. The predictions made by both CONTACT and multiple regression models for longitudinal and lateral tractions are in close agreement with the measured data on the VT-FRA roller rig. The multiple regression model, however, offers an algebraic expression that can be solved far more efficiently than a simulation run in CONTACT for a new dynamic condition. The results of the study further indicate that the established multiple regression models are an effective means for studying the effect of multiple parameters such as wheel load, creepage, and angle of attack on longitudinal and lateral tractions. Such data-driven parametric models provide an essential analysis and engineering tool in contact dynamics, just as they have in many other areas of science and engineering.