Braking Behavior Research Articles

FBrake, a Method to Simulate the Brake Efficiency of Laden Light Passenger Vehicles in PTIs While Measuring the Braking Forces of Their Unladen Configurations

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections.

Comparison of the Braking Behavior Among Cast Steel, Carbon/Carbon (C/C) and Carbon/Carbon-Silicon Carbide (C/C-SiC) Materials Mated with Copper-Based Composites

Carbon-based materials (e.g., carbon/carbon [C/C] and carbon/carbon-silicon carbide [C/C-SiC] materials) are considered promising brake disk materials for high-speed trains to replace cast or forged steels. Investigating the braking behavior of copper-based composites paired with various counterpart materials is helpful in advancing the development of high-speed and lightweight brake systems. In this study, the braking behavior and wear mechanisms of a novel copper-based composite mated with cast steel, C/C and C/C-SiC materials were investigated. The average coefficients of friction for the copper-based composite against C/C and C/C-SiC materials are increased by approximately 16.5% and 6.7%, and the wear rates of the copper-based composite against C/C and C/C-SiC materials are reduced by about 25.9% and 33.9% in comparison to those of the copper-based composite against cast steel. Additionally, compared with that of cast steel, the wear rate of the C/C material is increased by more than 3 times, and the negative value of the wear rate of the C/C-SiC material is noted. The dominant wear mechanisms of the copper-based composite mated with cast steel, C/C and C/C-SiC materials are adhesive wear combined with oxidation, graphite loss combined with oxidation as well as exfoliation of the material near graphite, and the material transfer combined with oxidation, respectively.

Using distributed simulations to investigate driver-pedestrian interactions and kinematic cues: Implications for automated vehicle behaviour and communication

As we move towards a future with Automated Vehicles (AVs) incorporated in the current traffic system, it is crucial to understand driver-pedestrian interaction, in order to enhance AV design and optimization. Previous research in this area, which has primarily used naturalistic observations or single-actor virtual reality simulations, has been limited by its inability to draw causal conclusions, also due to a lack of real human–human interactions. Our study addresses these limitations by employing a high-fidelity distributed simulation setup that links drivers in a motion-based simulator with pedestrians in a CAVE-based environment. This method allows for the examination of real-time and reciprocal interactions across a range of road-crossing scenarios. Using thirty-two pairs of drivers and pedestrians, we investigated how different factors, such as the presence of zebra crossings and varying time gaps of the approaching vehicle, influence driver behaviour and pedestrian crossing decisions. The effect of drivers’ control of the vehicle during such crossings (e.g., braking behaviour and lateral deviation) on pedestrians’ crossing decisions were also analysed. We found that the distribution of drivers’ average deceleration values were bimodal, where drivers either markedly yielded to pedestrians, or continued in their path, with very few instances of intermediate behaviour. We also found that pedestrian decisions were seemingly influenced by the different braking strategies adopted by the driver, with pedestrians crossing before the vehicles in response to soft and early, or late and hard braking, while late and soft braking often resulted in the vehicle passing first. We also observed a slight lateral movement of the vehicle away from pedestrians when drivers were not yielding, but more of a lateral deviation towards them when yielding. This may be because drivers subconsciously transfer their walking interaction habits to their driving behaviour, to avoid a collision with pedestrians. Finally, our results showed a stronger influence of these kinematic cues on pedestrian crossing decisions, when compared to zebra crossings. As well as highlighting the value of a novel approach for investigating vehicle–pedestrian interactions, this study illustrates how vehicle cues can assist pedestrian decisions, adding new knowledge in the development of human-like behaviour for future AVs.

Gain-scheduled model predictive controller for vehicle-following trajectory generation for autonomous vehicles

As the development of autonomous vehicles accelerates, the need to enhance the comfort characteristics for those vehicles has become important. In the present article, an enhanced vehicle-following motion planner algorithm is presented. The aim of the algorithm is to smoothen the repetitive braking and acceleration behaviour during vehicle following in traffic jam situations. The algorithm uses the information gathered from Lidar sensor, cameras and vehicle-embedded sensors in real time to construct the range vs. range-rate diagram, and it computes the desired velocity trajectory for the speed controller. The algorithm is based on the Gain-Scheduled Model Predictive Controller (MPC), where at least one MPC controller is designed to handle one of the three vehicle-following operating conditions: speed control, headway control and emergency brake control. The algorithm allows the designer to manipulate two vehicle following variables: standstill distance between lead vehicle and ego vehicle, and the headway time gap. The algorithm is experimentally validated on a full-size passenger vehicle.

Application of dynamic desired headway based adaptive backstepping sliding mode control design to mixed traffic system

To optimize the mixed car-following behavior of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), an adaptive backstepping sliding mode control (ABSMC) strategy for longitudinal velocity and distance control model (namely, MCF-ABSMCM) is put forth. First, the variable desired headway model (VDHM) is designed based on the vehicle-to-vehicle and vehicle-to-infrastructure information. Also, an asymmetric stochastic car-following model (ASCM) is designed to realistically describe the stochastic factor as well as vehicle acceleration and braking behaviors in mixed car-following. Second, an adaptive cruise sliding mode control law with a backstepping approach is devised to precisely track the desired following distance based on the VDHM. After creating the basic diagram of the two-model traffic flow, the intelligent driver model (IDM) is used as a baseline to compare and analyze the traffic capacity. Lastly, the effectiveness of the MCF-ABSMCM approach is confirmed in terms of both the mixed traffic flow density wave evolution and the vehicle control strategy, and the MCF-ABSMCM is validated under various adversary inputs. The simulation results demonstrate that the mixed-flow queue can finish headway tracking and keep a safe distance when using the MCF-ABSMCM described. This control strategy has been validated in VISSIM to significantly improve the efficiency of traffic operations compared to IDM and ASCM.

Effects of driver’s braking behavior by the real-time pedestrian scale warning system

A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian’s gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments.

Investigation of the friction and deformation behaviour of high-speed brakes

Spring-applied brakes are widely used components in industrial drive systems. They provide a braking torque by friction between a mostly organic friction lining and a metallic counter surface. Increasing with decreasing size, they currently achieve speeds of up to 6,000 rpm, which corresponds to a sliding speed in frictional contact of up to 35 m/s. At the same time, there is a trend towards high-speed drives, with speeds of 10,000 rpm and above. So far, little is known about the behaviour of the friction value and torque of conventional spring-applied brakes with low-cost organic friction linings under these operating conditions. For this reason, a test rig was develop - ed that allows testing at sliding speeds of up to 120 m/s with different load inertias. The tests carried out at KAt so far showed that with limited friction work, the conventional spring-applied brake reaches the nominal braking torque at higher sliding speeds. In addition to thermal overload of the friction lining, plastic deformation of the friction bodies can also permanently disrupt the operating behaviour of brakes operated at high sliding speeds. The plastic deformation of the friction discs manifests itself, for example, in a saucer-like shape of the discafs, leading to a reduction in the air gap and causing unwanted changes in the friction conditions. This paper describes the relationship between friction work and friction coefficient in organic linings and the physical mechanism of the deformation process of the friction discs. Based on these possible measures to reduce deformation are explained.

Effects of Transition Curves and Superelevation on the Critical States of Truck Rollovers on Sharp Curves

Sharp curves are vulnerable sections for rollover accidents. This study was conducted to investigate the effects of transition curve and superelevation on the critical speed and critical braking distance of truck rollovers. Using different transition types (spiral, Bloss and Grabowski curve) and different superelevation values (6%, 120 m transition length and 8%, 160 m) as variables, experiments of constant speed driving and hard braking were simulated and conducted by Trucksim. Conclusions regarding the influence of these road design factors on the stability of vehicles were then proposed. Grabowski curve allowed for the maximum critical rollover speed. The difference in critical rollover speed between different transition types was only determined by the length of the transition section. Simply extending the length of the transition section increased the critical rollover speed of spiral curve, at the same time decreasing the critical speed of Bloss and Grabowski curve. Hard braking experiments showed completely different characteristics. As the initial speed increased, only spiral curve became safer. Increasing superelevation made the braking behaviour of the vehicle more dangerous and fraught with uncertainties due to different target speed, starting curvature and changing superelevation. Based on these findings, a number of useful recommendations for drivers and road designers were put forward.

Index of braking behaviour in two dimensions within risk perception

Time To Collision (TTC) and other physical indices are widely used to predict drivers' braking behaviour. However, these indices often overlook the psychological aspects of how drivers perceive risk. Moreover, such indices are predominantly one-dimensional and do not account for the shared space, where vehicles can move in two dimensions. In this study, we introduce an index designed to predict drivers' braking behaviour in the shared space, taking into account the drivers' risk perception. In Experiment 1, a functional equation was derived to estimate the probability that drivers would brake in a given situation. The results confirmed that the index provides an adequate reflection of drivers' risk perception. In Experiment 2, the braking rate estimated with the index provided a better fit to a third-person risk evaluation when compared to other physical indices.

Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction.

Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the surrounding traffic conditions. Monocular camera-based methods are becoming increasingly popular for driver assistance, with precise predictions of vehicle speed and emergency braking being important for road safety enhancement, especially in the prevention of speed-related accidents. In this research paper, we introduce the implementation of a convolutional neural network (CNN) model tailored for the prediction of vehicle velocity, braking events, and emergency braking, employing sequential image sequences and velocity data as inputs. The CNN model is trained on a dataset featuring sequences of 20 consecutive images and corresponding velocity values, all obtained from a moving vehicle navigating through road-traffic scenarios. The model's primary objective is to predict the current vehicle speed, braking actions, and the occurrence of an emergency-brake situation using the information encoded in the preceding 20 frames. We subject our proposed model to an evaluation on a dataset using regression and classification metrics, and comparative analysis with existing published work based on recurrent neural networks (RNNs). Through our efforts to improve the prediction accuracy for velocity, braking behavior, and emergency-brake events, we make a substantial contribution to improving road safety and offer valuable insights for the development of perception-based techniques in the field of autonomous vehicles.

Modeling and braking analyses of longitudinal-vertical coupling towbarless aircraft taxiing system

The variation of tire vertical load has effects on the vehicle braking performance, which may lead to the misjudgment of braking distance and increasement of the accident risks. In order to evaluate the braking distance of the towbarless aircraft taxiing system (TLATS) accurately, a longitudinal-vertical coupling model of the TLATS is established, in which the effects of the vertical vibration on braking dynamics is considered. An 11-degrees of freedom (DOF) dynamic model of the TLATS, which consists of the system longitudinal motion, vertical vibration, and an advanced Lugre tire model, is proposed. The tire vertical dynamic loads are obtained by calculating system vibration differential equations as the input for the Lugre tire model, in which an arbitrary pressure distribution function over the contact patch and load related parameters are used. The Lugre tire model parameters are identified based on the virtual experiment results in ADAMS/Car software through a multi-objective optimization (MOO) method. The proposed tire model is more accurate by comparing with the models using other pressure distribution functions. The advanced Lugre model is integrated into the longitudinal-vertical coupling dynamic model of the TLATS, and the fuzzy PID control method is used to achieve the optimal slip rate during the braking procedure. The tire friction and TLATS’s braking performances in terms of the slip rate, braking speed and braking distance are compared between the coupling and uncoupling models under different traction velocities and the road excitations when a same fuzzy control method is used for the system braking. The results show that the proposed longitudinal-vertical coupling TLATS dynamic model could assess the braking behaviors of the TLATS more accurately than the simplified uncoupling model, which could decrease the potential safety risk of the aircraft towing operation on the airport apron.

Motorized two-wheeler riders’ rear brake application in sudden hazardous event of animal crossing

ABSTRACT This study aims to quantify the riders’ performance in the sudden event of animal crossing using explanatory variables such as psychological riding conditions and socio demographic parameters. The participants were asked to ride four sessions on the motorized two-wheeler simulator in randomized order: 1) Base, 2) Distraction, 3) Time pressure, and 4) Distraction along with Time pressure. The rear brake application was classified into four groups using K-means clustering: No braking, Mild braking, Harsh braking, and Very Harsh braking. Further, a multinomial logistic regression model was developed to quantify the rear braking behavior. The results from the study revealed that the odds of applying the harsher brakes are only 0.32 times in comparison to mild braking when riders are distracted. Overall findings indicate that riders’ psychological conditions can alter rider’s behavior, and driver training focusing on several hazards can further help in improving road safety.

Dynamic Characteristics of Three-Body Braking System Considering Tire-Road Friction

Abstract With the significant increase in vehicle ownership in our country, urban traffic conditions have become increasingly congested. Low-speed driving has become more prevalent, leading to more frequent instances of starting and braking. Consequently, the issue of low-speed braking flutter has become increasingly prominent. While the brake is in an open environment, the dust and particles in the air and the debris generated by the brake itself will have an impact on the braking behavior. In addition, according to the theory of modal coupling, the braking stability of the vehicle is also affected by other components. In this paper, different dynamic torsional models of braking system are established according to different braking conditions. Through numerical calculation, the influence of tire parameters, road conditions, and particles on the friction dynamics characteristics of braking system pairs is explored. The results show that the instability of the brake pair system often occurs at low speed. Different tire slip ratios, tire offset factors, and road conditions will lead to different relative motions of the braking system, but the existence of particles in the brake lining-disk interface can enhance the motion stability of the system.

Thermal Field Simulation and Thermal Capacity Influence Factor Analysis of Disc Brake Based on Thermal-structure Coupling

The matching and heat dissipation capacity of the brake have an important impact on the braking performance of the vehicle. During braking, most of the driving kinetic energy of the vehicle is converted into the internal energy of the brake disc through friction heat generation, resulting in a quick rise in the temperature of the brake disc, thus causing a complex thermal-solid coupling problem. This paper takes the disc brake of a certain vehicle as the analysis object, establishes the finite element model of the disc brake through hypermesh, and analyzes the distribution characteristics of the contact pressure between the friction pad and the brake disc under clamping and torsion braking behaviors. A thermal-solid coupling simulation analysis method considering the actual heat flow and heat transfer boundary of the brake disc is proposed. Compared with the temperature field of the bench test, the simulation accuracy reaches 97%. Finally, three types of brake discs with point rib, rotation rib and straight rib are designed, and the influence of brake disc thickness, shape and number of ventilation rib on the thermal capacity of brake disc is analysed. It provides guidance for the structural design of brake discs and can replace partial tests and shorten the brake disc development cycle.

Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method.

Due to the environmental protection of electric buses, they are gradually replacing traditional fuel buses. Several previous studies have found that accidents related to electric vehicles are linked to Unintended Acceleration (UA), which is mostly caused by the driver pressing the wrong pedal. Therefore, this study proposed a Model for Detecting Pedal Misapplication in Electric Buses (MDPMEB). In this work, natural driving experiments for urban electric buses and pedal misapplication simulation experiments were carried out in a closed field; furthermore, a phase space reconstruction method was introduced, based on chaos theory, to map sequence data to a high-dimensional space in order to produce normal braking and pedal misapplication image datasets. Based on these findings, a modified Swin Transformer network was built. To prevent the model from overfitting when considering small sample data and to improve the generalization ability of the model, it was pre-trained using a publicly available dataset; moreover, the weights of the prior knowledge model were loaded into the model for training. The proposed model was also compared to machine learning and Convolutional Neural Networks (CNN) algorithms. This study showed that this model was able to detect normal braking and pedal misapplication behavior accurately and quickly, and the accuracy rate on the test dataset is 97.58%, which is 9.17% and 4.5% higher than the machine learning algorithm and CNN algorithm, respectively.

When and How to Apply Automatic Emergency Brakes Based on Risk Perception and Professional Driver Emergency Braking Behavior

<div>The key issues of automatic emergency braking (AEB) control algorithm are when and how to brake. This article proposes an AEB control algorithm that integrates risk perception (RP) and emergency braking characteristics of professional drivers for rear-end collision avoidance. Using the formulated RP by time to collision (TTC) and time headway (THW), the brake trigger time can be determined. Based on the professional driver fitting (PDF) characteristic, the brake pattern can be developed. Through MATLAB/Simulink simulation platform, the European New Car Assessment Programme (Euro-NCAP) test scenarios are used to verify the proposed control algorithm. The simulation results show that compared with the TTC control algorithm, PDF control algorithm, and the integrated PDF and TTC control algorithm, the proposed integrated PDF and RP control algorithm has the best performance, which can not only ensure safety and brake comfort, but also improve the road resource utilization rate.</div>

Modelling speed reduction behaviour on variable speed limit-controlled highways considering surrounding traffic pressure: A random parameters duration modelling approach

Variable speed limits are frequently used to improve traffic safety and harmonise traffic flow. This study investigates how, and to what extent, drivers reduce their speed upon passing a variable speed limit sign. We specifically consider the impact on braking behaviour due to the systematic inclusion of different social pressures exerted by surrounding traffic. This social pressure is the natural result of having two vehicle cohorts created by a change in the variable speed limit (the new speed limit being higher than the original). The cohort with the higher speed limit overtakes vehicles with the lower speed limit, instigating a specific passing rate on drivers in the lower speed cohort. A driving simulator study is employed to obtain individual driver data whilst being able to systematically change the social pressure applied. A sample comprising sixty-seven participants conducted multiple randomised drives, with varying passing rates from as low as 90 veh/h to as high as 360 veh/h. The speed reduction behaviour of the participants is modelled using a random parameter duration modelling approach. Both the panel nature of the data and unobserved heterogeneity are captured through a correlated grouped random parameters with heterogeneity-in-the-mean model. The random parameters are predicated on the different passing rate scenarios, allowing drivers to take shorter or longer to reduce their speeds compared to the reference passing rate. It is shown that the extent of social pressure impacts braking behaviour and therefore affects safety measures, which is a function of the magnitude of the speed limit change. In addition, an extensive decision tree analysis is conducted to understand differential braking behaviour. Results reveal that, on average, female drivers take a shorter time to reduce their speed under a high passing rate but longer in a low passing rate scenario compared to males. Similarly, young drivers are found to take longer to reduce their speeds in a high passing rate scenario compared to other age groups. Our main findings indicate that the within-cohort safety is lowest under low passing rates due to comparatively larger speed differences between drivers. Yet, under a high passing rate, we observe an increase in violation of the speed limit by the lower speed limit vehicles (but less within cohort speed differences). Whilst normally this would be an undesired effect across cohorts, this violation is argued to lead to increased safety due to the smaller discrepancy in speed.

PECULIARITIES OF THE BEHAVIOR OF BULLS-CALFS OF UKRAINIAN GRAY BREED AND ITS RELATIONSHIP WITH LIVE WEIGHT GAINS

PECULIARITIES OF THE BEHAVIOR OF BULLS-CALFS OF UKRAINIAN GRAY BREED AND ITS RELATIONSHIP WITH LIVE WEIGHT GAINS

Fidelity Assessment of Motion Platform Cueing: Comparison of Driving Behavior under Various Motion Levels.

The present paper focuses on vehicle simulator fidelity, particularly the effect of motion cues intensity on driver performance. The 6-DOF motion platform was used in the experiment; however, we mainly focused on one characteristic of driving behavior. The braking performance of 24 participants in a car simulator was recorded and analyzed. The experiment scenario was composed of acceleration to 120 km/h followed by smooth deceleration to a stop line with prior warning signs at distances of 240, 160, and 80 m to the finish line. To assess the effect of the motion cues, each driver performed the run three times with different motion platform settings-no motion, moderate motion, and maximal possible response and range. The results from the driving simulator were compared with data acquired in an equivalent driving scenario performed in real conditions on a polygon track and taken as reference data. The driving simulator and real car accelerations were recorded using the Xsens MTi-G sensor. The outcomes confirmed the hypothesis that driving with a higher level of motion cues in the driving simulator brought more natural braking behavior of the experimental drivers, better correlated with the real car driving test data, although exceptions were found.

Impact on driver behavior from ERTMS speed-filtering

In the signal planning process, the ERTMS speed profiles based on track nature needs to be complemented with additional constraints. The base profile resolving the allowed track speed, often includes several speed changes which can be difficult for the train driver to follow. One approach to deal with this problem is to filter the base profile, reducing the number of changes and giving the driver more time for attention.This paper presents the effects of a speed profile filtering principle, based on possible time usage during a speed increase, on train energy consumption, train driver braking behavior, running time, and driver workload. In an Electrical Multiple Unit train driver simulator, 40 drivers tested three different speed profiles of a 19 km railway line. It can be concluded that differences in running time are small, that these small time-gains implies a high energy consumption cost, and that drivers tend to drive close to the indication braking curve in the beginning of the braking phase. Further, the drivers rated the driver task workload low for all filter conditions. Accordingly, a certain filter level is required to get capacity and energy effects.