Assessing the reliability of biomechanical variables during a horizontal deceleration task in healthy adults.

Horizontal deceleration technique is an underpinning factor to musculoskeletal injury risk and performance in multidirectional sport. This study primarily assessed within- and between-session reliability of biomechanical and performance-based aspects of a horizontal deceleration technique and secondarily investigated the effects of limb dominance on reliability. Fifteen participants completed four horizontal decelerations on each leg during test and retest sessions. A three-dimensional motion analysis system was used to collect kinetic and kinematic data. Completion time, ground contact time, rate of horizontal deceleration, minimum centre of mass height, peak eccentric force, impulse ratio, touchdown distance, sagittal plane foot and knee angles at initial contact, maximum sagittal plane thorax angle, and maximum knee flexion moment were assessed. Coefficients of variation (COV) and intraclass correlation coefficients (ICC) were used to assess within- and between-session reliability, respectively. Seven variables showed "great" within-session reliability bilaterally (COV ≤9.13%). ICC scores were 'excellent' (≥0.91; n = 4), or 'good' (0.76-0.89; n = 7), bilaterally. Limb dominance affected five variables; three were more reliable for the dominant leg. This horizontal deceleration task was reliable for most variables, with little effect of limb dominance on reliability. This deceleration task may be reliably used to assess and track changes in deceleration technique in healthy adults.

SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System.

Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles' bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird's eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively.

Variable Universe Fuzzy-Proportional-Integral-Differential-Based Braking Force Control of Electro-Mechanical Brakes for Mine Underground Electric Trackless Rubber-Tired Vehicles.

Currently, the main solution for braking systems for underground electric trackless rubber-tired vehicles (UETRVs) is traditional hydraulic braking systems, which have the disadvantages of hydraulic pressure crawling, the risk of oil leakage and a high maintenance cost. An electro-mechanical-braking (EMB) system, as a type of novel brake-by-wire (BBW) system, can eliminate the above shortcomings and play a significant role in enhancing the intelligence level of the braking system in order to meet the motion control requirements of unmanned UETRVs. Among these requirements, the accurate control of clamping force is a key technology in controlling performance and the practical implementation of EMB systems. In order to achieve an adaptive clamping force control performance of an EMB system, an optimized fuzzy proportional-integral-differential (PID) controller is proposed, where the improved fuzzy algorithm is utilized to adaptively adjust the gain parameters of classic PID. In order to compensate for the deficiency of single-close-loop control and adjusting the brake gap automatically, a cascaded three-closed-loop control architecture with force/position switch technology is established, where a contact point detection method utilizing motor rotor angle displacement is proposed via experiments. The results of the simulation and experiments indicate that the clamping force response of the proposed multi-close-loop Variable Universe Fuzzy-PID (VUF-PID) controller is faster than the multi-closed-loop Fuzzy-PID and cascaded three-close-loop PID controllers. In addition, the chattering of braking force can be suppressed by 17%. This EMB system may rapidly and automatically finish the operation of the overall braking process, including gap elimination, clamping force tracking and gap recovery, which can obviously enhance the precision of the longitudinal motion control of UETRVs. It can thus serve as a BBW actuator of mine autonomous driving electric vehicles, especially in the stage of braking control.

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

Vertical Center-of-Mass Braking and Motor Performance during Gait Initiation in Young Healthy Adults, Elderly Healthy Adults, and Patients with Parkinson's Disease: A Comparison of Force-Plate and Markerless Motion Capture Systems.

BACKGROUND: This study tested the agreement between a markerless motion capture system and force-plate system ("gold standard") to quantify stability control and motor performance during gait initiation. METHODS: Healthy adults (young and elderly) and patients with Parkinson's disease performed gait initiation series at spontaneous and maximal velocity on a system of two force-plates placed in series while being filmed by a markerless motion capture system. Signals from both systems were used to compute the peak of forward center-of-mass velocity (indicator of motor performance) and the braking index (indicator of stability control). RESULTS: Descriptive statistics indicated that both systems detected between-group differences and velocity effects similarly, while a Bland-Altman plot analysis showed that mean biases of both biomechanical indicators were virtually zero in all groups and conditions. Bayes factor 01 indicated strong (braking index) and moderate (motor performance) evidence that both systems provided equivalent values. However, a trial-by-trial analysis of Bland-Altman plots revealed the possibility of differences >10% between the two systems. CONCLUSION: Although non-negligible differences do occur, a markerless motion capture system appears to be as efficient as a force-plate system in detecting Parkinson's disease and velocity condition effects on the braking index and motor performance.

Advanced emergency braking system reduces the risk of motor vehicle collisions caused by falling asleep while driving in patients with untreated obstructive sleep apnea.

Obstructive sleep apnea leads to excessive daytime sleepiness and cognitive dysfunction, which are risk factors for motor vehicle collisions. We aimed to clarify if vehicles with an advanced emergency braking system could reduce motor vehicle collisions caused by falling asleep while driving among patients with untreated obstructive sleep apnea. We enrolled patients with untreated obstructive sleep apnea who underwent polysomnography. The questionnaires included the Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index, history of drowsy driving accidents, and use of an advanced emergency braking system. Multivariate analysis was performed, and odds ratios and 95% confidence intervals were calculated. This study included 1097 patients (mean age, 51.2 ± 12.9 years). Collisions caused by falling asleep while driving were recorded in 59 (5.4%) patients, and were more frequently observed in vehicles without an advanced emergency braking system (p = 0.045). Multivariate analysis showed that these collisions were associated with use of an advanced emergency braking system (odds ratio [95% confidence interval]: 0.39 [0.16-0.97], p = 0.04), length of driving (2.79 [1.19-6.50], p = 0.02), total sleep time (2.40 [1.62-3.55], p < 0.0001), sleep efficiency (0.94 [0.90-0.98], p = 0.003) and periodic limb movement index (1.02 [1.01-1.03], p = 0.004). The collision risk caused by falling asleep while driving in vehicles with an advanced emergency braking system was significantly lower. This study indicates that advanced emergency braking systems may be a preventive measure to reduce motor vehicle collisions among patients with untreated obstructive sleep apnea.

EEG-based emergency braking intention detection during simulated driving.

BACKGROUND: Current research related to electroencephalogram (EEG)-based driver's emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features. METHODS: To this end, a novel EEG-based driver's emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input. RESULTS: We recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking. CONCLUSIONS: The study provides a user-centered framework for human-vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions.

Research on the Prediction Method of Braking Rotation Angle for Remote-Controlled Excavator.

To calculate, analyze, and predict the rotation angle during the deceleration and braking process of large remote-controlled excavators, this article established a spatial coordinate system based on a simplified model of a hydraulic excavator's upper structure. Using the D-H parameter method, a mathematical model of the working device's center of gravity and its rotational inertia was established. Based on the characteristics of the excavator's hydraulic system and the relationship between brake torque variations, a prediction model was developed to forecast the stopping position (brake rotary angle) of the excavator's bucket after braking. Subsequently, the predicted results were validated using simulation and compared with existing experimental data to assess the accuracy of the model. The findings demonstrate that the predictive model exhibited high precision with minimal error. The utilization of this model enabled effective forecasting of the excavator's braking position changes, providing a theoretical foundation for the intelligent remote control of excavators.

Accuracy Improvement of Braking Force via Deceleration Feedback Functions Applied to Braking Systems.

Currently, braking control systems used in regional railways are open-loop systems, such as metro and tramways. Given that the performance of braking can be influenced by issues such as wheel sliding or the properties of the friction components present in brake systems, our study puts forward a novel closed-loop mechanism to autonomously stabilize braking performance. It is able to keep train deceleration close to the target values required by the braking control unit (BCU), especially in terms of the electrical-pneumatic braking transform process. This method fully considers the friction efficiency characteristics of brake pads and encompasses running tests using rolling stock. The test results show that the technique is able to stabilize the actual deceleration at a closer rate to the target deceleration than before and avoid wheel sliding protection (WSP) action, especially during low-speed periods.

Performance Improvement during Attitude Motion of a Vehicle Using Aerodynamic-Surface-Based Anti-Jerk Predictive Controller.

This study presents the effectiveness of an anti-jerk predictive controller (AJPC) based on active aerodynamic surfaces to handle upcoming road maneuvers and enhance vehicle ride quality by mitigating external jerks operating on the body of the vehicle. In order to eliminate body jerk and improve ride comfort and road holding during turning, accelerating, or braking, the proposed control approach assists the vehicle in tracking the desired attitude position and achieving a realistic operation of the active aerodynamic surface. Vehicle speed and upcoming road data are used to calculate the desired attitude (roll or pitch) angles. The simulation results are performed for AJPC and predictive control strategies without jerk using MATLAB. The simulation results and comparison based on root-mean-square (rms) values show that compared to the predictive control strategy without jerk, the proposed control strategy significantly reduces the effects of vehicle body jerks transmitted to the passengers, improving ride comfort without degrading vehicle handling at the cost of slow desired angle tracking.

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles.

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

The effect of an occlusion-induced delay on braking behavior in critical situations: A driving simulator study.

OBJECTIVE: To share results of an experiment that used visual occlusion for a new purpose: inducing a waiting time. BACKGROUND: Senders was a leading figure in human factors. In his research on the visual demands of driving, he used occlusion techniques. METHODS: In a simulator experiment, we examined how drivers brake for different levels of urgency and different visual conditions. In three blocks (1 = brake lights, 2 = no brake lights, 3 = occlusion), drivers followed a vehicle at 13.4 or 33.4 m distance. At certain moments, the lead vehicle decelerated moderately (1.7 m/s2) or strongly (6.5 m/s2). In the occlusion condition, the screens blanked for 0.4 s (if 6.5 m/s2) or 2.0 s (if 1.7 m/s2) when the lead vehicle started to decelerate. Participants were instructed to brake only after the occlusion ended. RESULTS: The lack of brake lights caused a delayed response. In the occlusion condition, drivers adapted to the instructed late braking by braking harder. However, adaptation was not always possible: In the most urgent condition, most participants collided with the lead vehicle because the ego-vehicle's deceleration limits were reached. In non-urgent conditions, some drivers braked unnecessarily hard. Furthermore, while waiting until the occlusion cleared, some drivers lightly touched the brake pedal. CONCLUSION: This experimental design demonstrates how drivers (sometimes fail to) adjust their braking behavior to the criticality of the situation. APPLICATION: The phenomena of biomechanical readiness and (inappropriate) dosing of the brake pedal may be relevant to safety, traffic flow, and ADAS design.

Sex differences in cognitive-motor components of braking in older adults.

Fast and accurate braking is essential for safe driving and relies on efficient cognitive and motor processes. Despite the known sex differences in overall driving behavior, it is unclear whether sex differences exist in the objective assessment of driving-related tasks in older adults. Furthermore, it is unknown whether cognitive-motor processes are differentially affected in men and women with advancing age. We aimed to determine sex differences in the cognitive-motor components of the braking performance in older adults. Fourteen men (63.06 ± 8.53 years) and 14 women (67.89 ± 11.81 years) performed a braking task in a simulated driving environment. Participants followed a lead car and applied a quick and controlled braking force in response to the rear lights of the lead car. We quantified braking accuracy and response time. Importantly, we also decomposed response time in its cognitive (pre-motor response time) and motor (motor response time) components. Lastly, we examined whether sex differences in the activation and coordination of the involved muscles could explain differences in performance. We found sex differences in the cognitive-motor components of braking performance with advancing age. Specifically, the cognitive processing speed is 27.41% slower in women, while the motor execution speed is 24.31% slower in men during the braking task. The opposite directions of impairment in the cognitive and motor speeds contributed to comparable overall braking speed across sexes. The sex differences in the activation of the involved muscles did not relate to response time differences between men and women. The exponential increase in the number of older drivers raises concerns about potential effects on traffic and driver safety. We demonstrate the presence of sex differences in the cognitive-motor components of braking performance with advancing age. Driving rehabilitation should consider differential strategies for ameliorating sex-specific deficits in cognitive and motor speeds to enhance braking performance in older adults.

Analysis of a Measurement Method and Test System for Pressure Change Rates in Commercial Vehicle Brake Chambers.

The pressure change rate (PCR) of the brake chamber is the key control parameter and evaluation index in the pneumatic braking system for intelligent braking. The PCR threshold value of commercial vehicle brake chambers for braking comfort is analyzed. The PCR measurement method based on a laminar flow resistance tube is proposed, and the PCR test system is designed. The simulation model of a PCR test system for commercial vehicle brake chambers is presented. By analyzing the simulation and experimental results, it is validated that the PCR test system of commercial vehicle brake chambers has the function of measuring PCR in real time. Finally, according to the MSA (Measurement System Analysis) evaluation method, the performance of the PCR test system for commercial vehicle brake chambers is analyzed, and the correctness and applicability of the test system are verified.

Effect of Behavioral Precaution on Braking Operation of Elderly Drivers under Cognitive Workloads.

The number of accidents by elderly drivers caused by the erroneous tread of a brake pedal or accelerator pedal has increased. A recent study reported that the number of accidents could be reduced by preparing for braking mistakes due to driving behavior by using a simulator. However, related studies have pointed out that driving behavior in simulators does not always reflect driving behavior in the real world. This paper focuses on the posture of the left foot as a behavioral precaution and provides insights into braking mistakes by comparing behavioral precautions taken on simulators and on public roads. In the experimental results, cognitive and action errors increased with age, but elderly drivers are less likely to have an accident when they are exposed to the risk of collision in situations with a mental workload by making space for the right foot to step on the brake pedal. Elderly drivers with coping skills had their left foot perpendicular to the ground and their body was unstable. This result was different from the driving behavior in the simulator, but it was not possible to identify that this difference was the cause of the collision accidents. Coping skills were predicted with 70% accuracy from the left foot posture of an elderly driver near the intersection. We expanded the system's range of use and enhanced its usefulness by predicting coping skills derived from natural driving behavior in the real world. The contributions of this study are as follows. We clarify the effect of behavioral precautions on the braking operation of elderly drivers when under a cognitive workload. We provide new insights into the use of behavioral precautions in older drivers' braking operations in the real world. We predicted coping skills from natural driving behavior near intersections in the real world.

Design of an Estimator Using the Artificial Neural Network Technique to Characterise the Braking of a Motor Vehicle.

Automatic systems are increasingly being applied in the automotive industry to improve driving safety and passenger comfort, reduce traffic and increase energy efficiency. The objective of this work is focused on improving the automatic brake assistance systems of motor vehicles trying to imitate human behaviour but correcting possible human errors such as distractions, lack of visibility or time reaction. The proposed system can optimise the intensity of the braking according to the available distance to carry out the manoeuvre and the vehicle speed to be as less aggressive as possible, thus giving priority to the comfort of the driver. A series of tests are carried out in this work with a vehicle instrumented with sensors that provide real-time information about the braking system. The data obtained experimentally during the dynamic tests are used to design an estimator using the Artificial Neural Network (ANN) technique. This information makes it possible to characterise all braking situations based on the pressure of the brake circuit, the type of manoeuvre and the test speed. Thanks to this ANN, it is possible to estimate the requirements of the braking system in real driving situations and carry out the manoeuvres automatically. Experiments and simulations verified the proposed method for the estimation of braking pressure in real deceleration scenarios.

Nonlinear Predictive Motion Control for Autonomous Mobile Robots Considering Active Fault-Tolerant Control and Regenerative Braking.

To further advance the performance and safety of autonomous mobile robots (AMRs), an integrated chassis control framework is proposed. In the longitudinal motion control module, a velocity-tracking controller was designed with the integrated feedforward and feedback control algorithm. Besides, the nonlinear model predictive control (NMPC) method was applied to the four-wheel steering (4WS) path-tracking controller design. To deal with the failure of key actuators, an active fault-tolerant control (AFTC) algorithm was designed by reallocating the driving or braking torques of the remaining normal actuators, and the weighted least squares (WLS) method was used for torque reallocation. The simulation results show that AMRs can advance driving stability and braking safety in the braking failure condition with the utilization of AFTC and recapture the braking energy during decelerations.

A Dual Distribution Control Method for Multi-Power Components Energy Output of 4WD Electric Vehicles.

Energy management strategies are vitally important to give full play to the energy-saving of the four-wheel drive electric vehicle (4WD EV). The cooperative output of multi-power components is involved in the process of driving and braking energy recovery of 4WD EV. This paper proposes a novel energy management strategy of dual equivalent consumption minimization strategy (D-ECMS) to improve the economy of the vehicle. According to the different driving and braking states of the vehicle, D-ECMS can realize the proportional control of the energy cooperative output among the multi-power components. Under the premise of satisfying the dynamic performance of the vehicle, the operating points of the power components are distributed more in the high-efficiency range, and the economy and driving range of the vehicle are optimized. In order to achieve the effectiveness of D-ECMS, MATLAB/Simulink is used to realize the simulation of the vehicle. Compared with the rule-based strategy, the economy of D-ECMS increased by 4.35%.

A Data-Driven Method for the Estimation of Truck-State Parameters and Braking Force Distribution.

In the study of braking force distribution of trucks, the accurate estimation of the state parameters of the vehicle is very critical. However, during the braking process, the state parameters of the vehicle present a highly nonlinear relationship that is difficult to estimate accurately and that seriously affects the accuracy of the braking force distribution strategy. To solve this problem, this paper proposes a machine-learning-based state-parameter estimation method to provide a solid data base for the braking force distribution strategy of the vehicle. Firstly, the actual collected complete vehicle information is processed for data; secondly, random forest is applied for the feature screening of data to reduce the data dimensionality; subsequently, the generalized regression neural network (GRNN) model is trained offline, and the vehicle state parameters are estimated online; the estimated parameters are used to implement the four-wheel braking force distribution strategy; finally, the effectiveness of the method is verified by joint simulation using MATLAB/Simulink and TruckSim.

Emergency Braking Evoked Brain Activities during Distracted Driving.

Electroencephalogram (EEG) was used to analyze the mechanisms and differences in brain neural activity of drivers in visual, auditory, and cognitive distracted vs. normal driving emergency braking conditions. A pedestrian intrusion emergency braking stimulus module and three distraction subtasks were designed in a simulated experiment, and 30 subjects participated in the study. The common activated brain regions during emergency braking in different distracted driving states included the inferior temporal gyrus, associated with visual information processing and attention; the left dorsolateral superior frontal gyrus, related to cognitive decision-making; and the postcentral gyrus, supplementary motor area, and paracentral lobule associated with motor control and coordination. When performing emergency braking under different driving distraction states, the brain regions were activated in accordance with the need to process the specific distraction task. Furthermore, the extent and degree of activation of cognitive function-related prefrontal regions increased accordingly with the increasing task complexity. All distractions caused a lag in emergency braking reaction time, with 107.22, 67.15, and 126.38 ms for visual, auditory, and cognitive distractions, respectively. Auditory distraction had the least effect and cognitive distraction the greatest effect on the lag.