The results of the active models' simulations regarding the occupant's peak forward and lateral movement, during low-speed sled tests, were comparable to the volunteer data, irrespective of the pulse severity level. A statistically substantial contrast existed between the active and control models' performance.Employing Wilcoxon signed-rank tests on peak forward and lateral excursion data, a value below 0.05 was observed. When evaluating the average CORA scores from both 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, active model performance consistently outperformed that of the control models.Significantly better CORA scores, calculated from both sled and field trials, distinguished the F05-OS+Active and M50-OS+Active models from the control models. The results demonstrate the capability of these models to forecast occupant movement during crash-mitigating maneuvers and low-speed impacts in the frontal, lateral, and frontal-oblique directions.The performance of the F05-OS+Active and M50-OS+Active models, as measured by CORA scores from combined sled and field tests, exceeded that of the control models. Data analysis reveals the ability of these models to project occupant movements in evasive driving maneuvers and low-speed crashes, encompassing frontal, side, and angled frontal-impact situations.Advanced Driving Assistance Systems (ADAS) hold promise for mitigating the frequency and impact of collisions involving Vulnerable Road Users (VRUs). Nevertheless, the practically boundless variety of VRU visual presentations (such as size, clothing, and accessories) poses a technical hurdle for systems, which require precise detection and identification of VRUs to deploy effective mitigation strategies. This study used track testing to discover the parameters impacting systems' abilities to identify pedestrians.The standardized articulated adult male pedestrian (EPTa) and the seven-year-old articulated child pedestrian (EPTc) formed the control group. The track evaluations were subject to the Euro NCAP AEB-VRU test protocols, and their various consequential iterations. Using an iterative testing methodology, the detection capabilities of systems under varying target configurations and environmental factors were measured and compared to a control group (baseline condition). More than a thousand track tests involved 24 different configurations and 13 vehicles (model years 2019 through 2021). The encompassing environmental conditions involved snow-covered roads under the cover of night. A mix of winter clothing, accessories like hats and jackets of varied colors, backpacks, umbrellas, and scooters were seen on the pedestrians. Other situations involved the presence of parked cars as impediments, or utilizing numerous pedestrians (such as a parent with a child, or a crowd at a busy intersection) to provide challenging and authentic urban-like conditions for vehicle evaluations.This research investigates how parameters deviate from the baseline condition to affect vehicle safety performance. The test track's urban-like conditions and weather impacted the systems in a non-uniform manner. Despite the comprehensive nature of the analysis, the validity of these findings is nevertheless constrained by the restricted vehicle sample size and the limited tests per 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.In Canada, vehicles encounter subpar road conditions and a diverse array of pedestrian types. When faced with pedestrian collisions, the vehicles tested demonstrated a variety of performance and capabilities in their collision mitigation strategies. The research demonstrates the safety implications of weather and the different types of VRUs.An enhanced role and improved abilities are driving the growth of driver monitoring systems. Data concerning the vehicle, driver behavior, and physiological indicators are analyzed to generate drowsy driving detection models within this paper. To augment camera-based drowsiness detection, vehicle-based data and heart rate variability from a wearable sensor were integrated. This study then compared the performance of various detection models employing these combined data sources. The models' capacity for timely drowsiness prediction is assessed. A model's speed in detecting drowsiness directly correlates with the predictive window before an adverse incident can be anticipated.A production Driver Monitoring System, a product of Aisin Technical Center of America, provided the behavioral data. Vehicular data were collected from the National Advanced Driving Simulator's large-excursion motion-base driving simulator for analysis. The Empatica E4 wristband served as a tool for collecting physiological data. Forty subjects, after remaining awake for a minimum of 16 hours, piloted the simulator for durations of up to three hours each. il receptor signal Every ten minutes, periodic drowsiness measurements were taken, encompassing both an external rater's observations and the participant's self-reported Karolinska Sleepiness Scale. Nine binary random forest models were formulated, employing different pairings of data sources and corresponding ground truths.Nine models exhibited classification accuracy results ranging from 0.77 to 0.92 on a scale where 1 indicates complete accuracy. A superior model used physiological data from a reduced dataset, with missing data segments excluded after heart rate variability analysis was completed. A model, characterized by its exceptional timeliness, identified drowsiness 67 minutes prior to a driver's lane departure.Model performance experienced a slight uplift due to the inclusion of physiological measures. 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.Integrating physiological measures yielded a modest improvement in the model's accuracy. Models trained using observations of drowsiness exhibited earlier detection of drowsiness compared to those solely reliant on the Karolinska Sleepiness Scale, proving more timely in recognizing the commencement of drowsiness.A prospective study of safety for highly automated driving systems is indispensable before their introduction to the market. The traffic safety impact evaluation of these systems developed by BMW takes a holistic approach and uses stochastic traffic simulations to achieve accuracy. A vital component of this plan is a model of driver behavior, exhibiting the spectrum of driving performance, stretching from typical daily driving to challenging critical conditions. To achieve trustworthy results, it is imperative to validate the driver model. The driver model, as detailed in this paper, aims to depict realistic reactions to various critical real-world traffic situations.Cognitive processes in traffic settings are modeled by BMW's Stochastic Cognitive Model (SCM). From collecting information via eye movements to mental mapping of the environment, identifying circumstances from visual input, and reacting accordingly, these procedures cover a wide array of steps. To generate diverse driver behaviors in simulations, the driver model incorporates stochastic driver parameters alongside cognitive processes. The key to simulating realistic traffic interactions lies in sophisticated visual attention modeling, providing crucial input for the sequential cognitive processes, which include recognizing situations and reacting to them. Using the SCM methodology, this paper describes the modeling of driver gaze behavior.Three crucial real-world traffic situations demonstrate the use of SCM, evaluating and comparing gaze behavior, brake reaction times, and time-to-collision metrics with corresponding real-world data. The stochastic method of evaluation focuses on a collective of virtual SCM test drivers instead of a single SCM agent. The results reveal SCM's potential to simulate the influence of visual inattention and its effect on collision risk.SCM-based methods contribute to replicating realistic driver behavior in simulations. SCM excels in illustrating real-world driving behavior, especially within presented critical scenarios, driven by its gaze behavior and consequent reactions. The distinct characteristics of SCM agents influence driving performance, enabling the simulation of a multitude of driving approaches. The investigation within this paper, however, only encompassed three real-world case studies. Hence, the need arises for future study of situations, ranging from critical to non-critical.Simulated driver behavior that is realistic can be realized through the use of the SCM methodology. Under critical conditions depicted in the presented scenarios, the SCM effectively demonstrates real-world driving behavior, primarily determined by its gaze and subsequent response. The driving performance is not uniform across SCM agents; consequently, different driving behaviors are possible to simulate using SCM. Yet, the examination conducted in this article was restricted to just three real-world situations. Consequently, future investigation must encompass a range of critical and non-critical scenarios.A novel seating configuration for vehicles with automated driving systems, when encountering a frontal impact, involves a reclined rear-facing seat. High-speed collision data regarding biomechanical responses and injuries is limited for this particular seating design. Our investigation sought to understand the thoracic biomechanical responses and injuries in male cadavers during rear-facing crashes, evaluating different environmental factors. Two separate seating locations for PMHS were observed; one using a fixed D-ring seatbelt assembly, and the other having an all-belts-to-seat restraint.