The simulations using the active models for the occupant's peak forward and lateral movement in the low-speed sled tests demonstrated a satisfactory match with the corresponding volunteer data, for both pulse intensities. Statistically significant differences were found in the performance of the active and control models.The results of Wilcoxon signed-rank tests, utilizing peak forward and lateral excursion data, demonstrated a value of less than 0.05. The CORA scores derived from the sled test (sled test M50-OS+Active= 0543, male control= 0471, F05-OS+Active= 0621, female control= 0505) and field test (M50-OS+Active= 0836, male control= 0466, F05-OS+Active= 0832, female control= 0787) simulations, on average, were greater for active models than for control groups.The F05-OS+Active and M50-OS+Active models outperformed control models in terms of CORA scores, encompassing results from both sled and field tests. These results emphasize the models' success in anticipating the movements of occupants during crash-mitigating maneuvers and low-speed impacts, including those occurring in frontal, lateral, and frontal-oblique collisions.The F05-OS+Active and M50-OS+Active models' CORA scores, calculated using data from both sled and field tests, surpassed those of control models, indicating better performance. The research findings underscore the models' potential to predict the body motions of occupants involved in collision-avoidance maneuvers and low-speed crashes in frontal, lateral, and oblique frontal directions.Advanced Driving Assistance Systems (ADAS) have the potential to contribute to a decrease in the number and severity of collisions involving Vulnerable Road Users (VRUs). Still, the practically boundless assortment of VRU visual appearances (such as size, clothing, and accessories) poses a significant technical problem, forcing systems to correctly identify and detect VRUs to initiate effective mitigation protocols. The goal of this study was to ascertain, through track testing, which parameters dictate the capabilities of systems in pedestrian recognition.The standardized articulated adult male pedestrian (EPTa), and the seven-year-old articulated child pedestrian (EPTc), were the control group used in the study. Track evaluations leveraged the Euro NCAP AEB-VRU test protocols, along with their derived forms. To assess the detection capabilities of systems under varying target configurations and environmental conditions, an iterative testing approach was employed, comparing results against a control group (baseline). More than a thousand track tests involved 24 different configurations and 13 vehicles (model years 2019 through 2021). The environmental conditions included roads that were both snow-covered and at night. Pedestrians sported winter gear, such as hats, colorful jackets, backpacks, umbrellas, and even scooters. In various other situations, stationary vehicles served as impediments, or numerous pedestrian targets (mimicking a parent leading a child across the street or a gathering at a junction) were employed to test vehicles' responses in realistic urban settings.A vehicle's safety is explored in this study, focusing on how parameter variations outside the baseline condition influence its performance. Uneven effects on different systems resulted from the test track's weather and urban-like conditions. While the study has merit, the conclusions' validity is nevertheless limited by the small sample size of vehicles and the limited number of tests in each scenario.Road conditions in Canada are frequently less than ideal, presenting challenges for vehicles alongside a diverse pedestrian population. Regarding pedestrian collision mitigation, the tested vehicles displayed varying degrees of performance and capabilities. The research examines how weather and VRU types combine to create safety issues.Canadian roads present less-than-ideal conditions for vehicles, alongside a wide range of pedestrian profiles. The vehicles under evaluation showcased different levels of performance and capability in their responses to pedestrian collision scenarios. Weather events and VRU characteristics are examined for their potential safety hazards in the research.The capabilities and importance of driver monitoring systems are on the rise. This paper investigates drowsy driving detection models that integrate data acquired from the vehicle, the driver's actions, and physiological responses. Utilizing vehicle-based data and heart rate variability from a wearable device, camera-based systems were expanded, and the efficacy of drowsiness detection models that leverage these data sources was evaluated. The investigation explores the relationship between model timeliness and drowsiness prediction accuracy. Timely drowsiness identification by a model translates into an advanced warning system for mitigating harmful incidents.Driver Monitoring System data, produced by Aisin Technical Center of America, was provided. Vehicular data were captured by the National Advanced Driving Simulator's large-excursion motion-base driving simulator. An Empatica E4 wristband provided the source for the collected physiological data. The simulator was operated by 40 participants who had been awake for at least sixteen hours, each participant spending no more than three hours in operation. histonemethyltransf signal Employing a Karolinska Sleepiness Scale self-report alongside an external rater's observational assessment, periodic drowsiness measurements were recorded every ten minutes. Different data source and ground truth combinations were used to generate nine binary random forest models.Regarding classification accuracy across nine models, scores varied from 0.77 to 0.92, measured on a scale of 0 to 1, where a score of 1 indicates a perfectly accurate model. The most effective model incorporated physiological data and a subset of the data, excluding segments with missing values, subsequently to heart rate variability calculations. Drowsiness was anticipated 67 minutes ahead of a lane departure incident, thanks to the most pertinent model.Physiological metrics, when applied, produced a minimal gain in the model's accuracy. The models trained using observational drowsiness assessments achieved earlier drowsiness detection compared to models reliant on the Karolinska Sleepiness Scale, thus showcasing a more immediate method for identifying the onset of drowsiness.A small improvement in model accuracy resulted from incorporating physiological measures. The training of models on observational drowsiness ratings led to earlier identification of drowsiness, compared to models solely using the Karolinska Sleepiness Scale, enabling more prompt detection of the commencement of drowsiness.A prospective study of safety for highly automated driving systems is indispensable before their introduction to the market. BMW has established a thorough method for evaluating the impact of these systems on traffic safety, with stochastic traffic simulations playing a key role in this process. A driver behavior model displaying various levels of performance, from typical daily driving to demanding critical circumstances, is fundamental for this method. To guarantee dependable outcomes, the driver model's validation is essential. This research paper aims to demonstrate the realistic portrayal of the driver model in the face of various critical real-world traffic scenarios.The Stochastic Cognitive Model (SCM), developed by BMW, is designed for modeling cognitive procedures occurring in traffic situations. From gathering information with eye movements to creating mental maps of the environment, recognizing situations from visual input, and reacting to those situations, these processes span a wide array of activities. Cognitive processes, in conjunction with stochastic driver parameters, are integral to the driver model's ability to produce a variation in driver behavior during simulations. Visual attention modeling is the cornerstone of realistic traffic simulation interactions, serving as the necessary input for the subsequent sequential cognitive processes, namely, situation recognition and reaction. Using the SCM methodology, this paper describes the modeling of driver gaze behavior.In three real-world traffic scenarios, the application of SCM evaluates gaze behavior, brake reaction times, and time-to-collision, comparing these results to real-world data. A collection of virtual SCM test drivers, alongside a single SCM agent, is scrutinized using the stochastic approach. Simulation of visual inattention's effect on collision risk is possible with SCM, as indicated by the results.By incorporating SCM, simulations can generate realistic driver behaviors. The critical scenarios presented highlight SCM's capability to model real-world driving behavior, primarily due to its gaze-based behavior and ensuing reactions. The performance of driving varies significantly depending on the specific SCM agent, enabling the simulation of diverse driving behaviors. Nevertheless, the inquiry presented in this document encompassed just three actual instances. As a result, further investigation into both critical and non-critical scenarios is necessary for future endeavors.Simulated driver behavior that is realistic can be realized through the use of the SCM methodology. The presented critical scenarios allow the SCM to convincingly model real-world driver behavior, influenced chiefly by its gaze patterns and subsequent reactions. The driving performance is not uniform across SCM agents; consequently, different driving behaviors are possible to simulate using SCM. The research, though, confined itself in this paper to examining only three real-world cases. Henceforth, a deeper examination of future scenarios, encompassing both critical and non-critical aspects, is imperative.In the event of a frontal impact, a possible non-standard seating arrangement for automobiles with automated driving is a reclined seat positioned to face rearward. This seating setup's biomechanical response and injury data during high-speed impacts are incomplete and limited. Thoracic biomechanical responses and injuries in male post-mortem human subjects (PMHS) under rear-facing impacts were the focus of this study, which assessed different boundary conditions. PMHS were placed in two separate seating areas; one seat featured a fixed D-ring seatbelt assembly, the other, an all-belts-to-seat restraint.