To improve the anti-lock braking system (ABS) performance, the control algorithm is the core, and the slip ratio and optimal slip ratio should be determined firstly. However, tyre wear may cause a deviation in the slip ratio calculation, while road conditions affect the optimal slip ratio. To enhance the ABS control performance, while considering tyre wear, a Harris hawk optimisation (HHO) sliding mode control method is proposed in this paper. First, the effective tyre radius is estimated using an adaptive extended Kalman filter (AEKF) and slip ratio correction to reflect variations in tyre wear, and the actual slip ratio is calculated using the estimated tyre radius. Then, the road adhesion coefficient is estimated with AEKF method, and the optimal slip ratio is determined by establishing its mapping relationship with the road adhesion coefficient. Finally, a sliding mode controller is designed with the HHO algorithm based on the obtained effective tyre radius and optimal slip ratio, and the proposed control strategy is verified on a dSPACE hardware-in-the-loop simulation platform under various tyre wear and road conditions. The results show that the proposed control strategy significantly shortens the braking distance. The maximum shortening ratio reaches 7.82%.

Fixed-time fault-tolerant control for electro-mechanical braking systems under input saturation and time-delay.

Estimating the safety impact of mandatory ABS legislation for motorised two-wheelers in India using interrupted time series

Interval type-2 fuzzy reservoir cerebellar model articulation controller design for antilock braking systems

This study investigates the impact of urban meteorological factors on road crash severity in Dhaka, Bangladesh. Using police crash data, and meteorological data from NASA POWER database for years 2011–2022, a generalized ordered logit model was used to analyze crash severity, and interpreted using odds ratio, log odds ratio, predicted probabilities and marginal effects. The results show that land surface temperature (LST), relative humidity, precipitation, surface pressure, and wind speed have significant association with crash severity. Relative humidity, surface pressure and LST exhibited positive relation with higher severity levels of crashes, whereas precipitation had a negative relation. We recommend three actions to lessen the severity of crashes during inclement weather based on the findings: (i) weather-responsive transport safety policies, which incorporate real-time weather data into intelligent transport systems; (ii) law enforcement-oriented policy implications, which include using automated speed cameras and red-light violation cameras to improve compliance consistency and updating driver training courses to include modules on risk perception across various environmental conditions; and (iii) infrastructure and vehicle-related policy implications, which include designing road geometries and surface conditions to prevent the effects of adverse weather conditions and utilizing safety equipment, such as electronic stability control and anti-lock braking systems.

Brake pedals and wheel braking units are mechanically decoupled in brake-by-wire systems. This causes the driver to lose the familiar pedal feel. To address this issue, this paper designed an active braking pedal simulator based on the long-travel Halbach-array linear motor. Firstly, this paper conducted both qualitative and quantitative analyses on the pedal characteristics of a traditional hydraulic braking system and used them as a reference. A dual-coil independent control strategy was designed in order to overcome the thrust instability at the junction of the Halbach-array magnetic field. This enables the linear motor to achieve smooth and continuous thrust output throughout the entire travel range. Secondly, this paper also designed a “linear motor + spring” solution to reduce energy consumption and peak motor thrust. By conducting a quantitative analysis of the relationship between the spring stiffness, motor work and peak thrust, the spring stiffness was optimized. The results show that when the spring stiffness is 3.73 N/mm, the motor work can be reduced to 5.92 Joules while significantly reducing the peak thrust. Finally, this paper also established a testing platform. It was used to verify the performance of the proposed pedal simulator under low-intensity, medium-intensity, and high-intensity braking conditions as well as an anti-lock braking system intervention. The testing results show that the pedal simulator can actively adjust the pedal characteristics according to the braking intensity, and it can provide clear vibration feedback during the anti-lock braking system intervention. Therefore, the proposed pedal simulator effectively simulates the pedal feel of hydraulic braking systems while improving energy efficiency and operational stability. It provides a feasible solution for enhancing the driver–vehicle interaction and the driving comfort of brake-by-wire systems.

This study developed and evaluated a STEM-based digital learning module on the Anti-lock braking system (ABS) for Technical and Vocational Education and Training (TVET), implemented in a Light Vehicle Engineering (LVE) program. This pilot study uses a Research and Development (R&D) approach guided by the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. Prior to implementation, expert review was conducted to establish media and content quality. Media experts rated the module at 86.95%, highlighting strengths in interface design, software, and usefulness. Content experts rated the module at 79.68%, indicating satisfactory conceptual accuracy and pedagogical usefulness for supporting automotive competency learning, while identifying curricular alignment as the primary area requiring further improvement. The module was then piloted with 35 vocational students using a one-group pre-post test design. Learning outcomes demonstrated a statistically significant improvement in ABS knowledge, with the average score increasing from 51.49 on the pre-test to 71.81 on the post-test. The average gain was 20.32 points, representing a 39.46% improvement relative to the pre-test mean, with a very large effect size (Cohen's d = 3.67). Assuming a maximum score of 100, the normalized gain was 0.42, indicating a moderate learning gain. Overall, these results provide quantitative evidence that a STEM-oriented digital ABS module can enhance learning outcomes and support structured, interactive, and industry-relevant instruction in vocational automotive education.

Wheel alignment is a critical vehicle maintenance parameter influencing safety, handling stability, tire life, and fuel efficiency. Conventional alignment assessment relies on external alignment bays, making the process reactive and dependent on workshop visits. This paper presents an in vehicle wheel alignment monitoring system that enables proactive detection of misalignment through embedded sensor diagnostics. The proposed system evaluates key alignment parameters—Toe, Camber, and Caster—using alignment sensors integrated into the suspension, supported by vehicle level sensors, steering angle sensors, gear position sensors, and yaw rate sensors. The Anti lock Braking System control unit acts as the master ECU, validating predefined operating conditions, executing alignment algorithms, and comparing real time sensor data with stored reference values. Alignment status and corrective recommendations are communicated to the driver via the vehicle command display. The paper describes the system methodology, sensor working principles, data flow, and system architecture, and compares the proposed approach with traditional alignment methods. This enables real time monitoring, preventive maintenance, improved safety, and customer convenience.

This study conducts a comparative analysis of sliding mode control (SMC) and fractional-order sliding mode control (FOSMC) for application in antilock braking systems (ABS). Based on foundational principles of theoretical mechanics, the ABS dynamics are modeled as a single-input system to analyze wheel-slip regulation under diverse and variable road conditions. Both the conventional SMC and the proposed FOSMC are designed using a Lyapunov-based approach to ensure robust stability, with the latter incorporating fractional-order derivatives to refine the sliding surface and dynamic response. The conventional SMC method, while demonstrating strong robustness and disturbance rejection capabilities, is found to induce persistent chattering during transient phases, which can compromise system reliability and actuator longevity. By contrast, the FOSMC controller enhances transient behavior by attenuating chattering and yielding smoother, more consistent wheel-slip tracking. The inclusion of fractional-order terms contributes to faster convergence and improved adaptation to abrupt changes in road friction, though it introduces increased computational complexity. Numerical simulations validate the performance of both controllers across multiple driving scenarios, including dry, wet, and icy road conditions. Results confirm that FOSMC significantly reduces chattering, accelerates system convergence, and maintains stable braking performance with greater consistency compared to conventional SMC, establishing its potential for implementation in advanced ABS designs.

Pothole detection and mitigation systems are integral to enhancing road safety and optimizing vehicle performance. By leveraging Convolutional Neural Networks (CNN) and Internet of Things (IoT) technologies, these systems enable real-time hazard identification, such as potholes, and facilitate dynamic vehicle adjustments to mitigate potential damage. This study analyzed over 1,000 pothole images, with the CNN model achieving an accuracy rate of 96% to 99%. Edge detection techniques, including Sobel filters, were utilized to assess key pothole attributes such as diameter, depth, and edge sharpness. For potholes with diameters exceeding 50 cm and edge sharpness above 85%, the vehicle's suspension damping was automatically increased by 40%, minimizing the impact on the vehicle's chassis. Additionally, the system dynamically reduced vehicle speed by 10–20 km/h for severe potholes, based on real-time analysis by the Electronic Control Unit (ECU). The ECU also communicated with the Anti-lock Braking System (ABS) to apply braking force when sharp-edged potholes were detected. In scenarios where rear vehicles maintained a safe distance of 50 meters, the braking system was activated, reducing the risk of tire damage and collisions. Through IoT integration, real-time data was stored in the cloud, enabling predictive maintenance and improving repair planning efficiency by 30%. This approach not only enhances passenger safety but also reduces vehicle wear and tear, while improving road infrastructure management efficiency. The combination of CNN and IoT-based solutions marks a significant advancement in automotive safety systems.

Hydraulic braking systems play a pivotal role in modern automobiles by providing the necessary force for vehicle deceleration. These systems function through the conversion of force applied to the brake pedal into hydraulic pressure, which then actuates the brake components. Over the years, hydraulic systems have evolved to enhance performance, safety, and reliability. The design of hydraulic brake systems is a complex process that incorporates considerations such as fluid dynamics, material properties, and actuator mechanisms. Central to this evolution is the shift towards more efficient and responsive braking systems, including the incorporation of anti-lock braking systems (ABS) and electronic stability control (ESC). This paper explores the design principles behind modern hydraulic brake systems, with a focus on system components, fluid dynamics, and the integration of advanced technologies. The importance of optimizing the brake fluid, reducing system weight, and enhancing the durability of brake components is highlighted as critical factors in system design. Moreover, the development of advanced sensors and electronic control systems has significantly improved the performance and safety of hydraulic braking systems. The integration of smart materials and predictive analytics further promises to enhance the future performance of these systems. This review aims to provide an in-depth understanding of the hydraulic brake system design, including the challenges faced by engineers in optimizing system performance and safety while meeting regulatory standards.

The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel speed inputs for anti-lock brake systems (ABS), is essential. Effective detection of wheel speed sensor faults not only improves functional safety, but also plays a vital role in keeping system resilience against potential cyber–physical threats. Although data-driven approaches have gained popularity for system development due to their ability to extract meaningful patterns from historical data, a major limitation is the lack of diverse and representative faulty datasets. This study proposes a novel dual learning model, based on Temporal Convolutional Networks (TCN), designed to accurately distinguish between normal and faulty wheel speed sensor behavior within a hardware-in-the-loop (HIL) simulation platform implemented on an FPGA. To address dataset limitations, a TruckSim–MATLAB/Simulink co-simulation environment is used to generate realistic datasets under normal operation and eight representative fault scenarios, yielding up to 5000 labeled sequences (balanced between normal and faulty behaviors) at a sampling rate of 60 Hz. Two TCN models are trained independently to learn normal and faulty dynamics, and fault decisions are made by comparing the reconstruction errors (MSE and MAE) of both models, thus avoiding manually tuned thresholds. On a test set of 1000 sequences (500 normal and 500 faulty) from the 5000 sample configuration, the proposed dual TCN framework achieves a detection accuracy of 97.8%, a precision of 96.5%, a recall of 98.2%, and an F1-score of 97.3%, outperforming a single TCN baseline, which achieves 91.4% accuracy and an 88.9% F1-score. The complete dual TCN architecture is implemented on a Xilinx ZCU102 FPGA evaluation kit (AMD, Santa Clara, CA, USA), while supporting real-time inference in the HIL loop. These results demonstrate that the proposed approach provides accurate, low-latency fault detection suitable for safety-critical ABS applications and contributes to improving both functional safety and cyber-resilience of braking systems.

This paper presents a comparative vibro-acoustic analysis of two distinct ABS (Anti-lock Braking System) braking unit designs, focusing primarily on their vibrational behaviour under dynamic operating conditions.The study aims to identify and evaluate the differences in vibration transmission paths, frequency response characteristics, and structural resonance phenomena that influence the overall vibro-acoustic performance of the braking system.Both experimental and analytical methodologies were employed to achieve a comprehensive understanding of the systems' behaviour.Experimental tests were conducted using accelerometers and microphones strategically positioned on the hydraulic unit and adjacent structural components to capture vibration and noise data during controlled braking cycles.The acquired signals were processed through spectral and modal analyses, enabling the identification of dominant frequency bands and characteristic vibration modes associated with each design.

This paper presents an in-depth study on the structural integrity enhancement and machining process optimization of Anti-lock Braking System (ABS) hydraulic valve blocks, focusing on the transition from the MK60 to the MK100 design. The research combines finite element analysis (FEA), topology optimization, fixture redesign, and coolant technology improvements to achieve significant performance, productivity, and sustainability gains. The MK100 exhibits a mass reduction of 31.6%, an increase in tensile strength by 29.2%, and a fatigue life extension of 35% compared to the MK60. Pressure losses have been reduced by 38.8%, improving braking system responsiveness. On the manufacturing side, fixture redesign increased production capacity from 240 to 480 parts per shift while reducing cycle time from 16 min to 8 min per lot. The transition from a semi-synthetic emulsion coolant (AquaCut EM-X45) to a bio-based oil (BioLube AL-2200) extended coolant replacement intervals from six months to two years, reduced tooling costs, and increased tool life by 25%. These findings demonstrate the feasibility of integrating computational design methods with advanced machining strategies to achieve measurable mechanical and economic benefits in the automotive industry.

<div>If road friction coefficient can be measured in a car driving, the performance of advanced driver-assistance systems (ADAS) such as antilock braking system (ABS) and automatic braking systems can be improved. Generally, ADAS uses information obtained from wheel speed sensors, acceleration sensors, and the like. However, it is difficult to measure accurately road friction coefficients with these sensors. Therefore, many studies measured road friction coefficients from strain or deformation in the bottom of a tire (tread), which is the only place to contact with a road surface. However, a sensor installed on the bottom of a tire is easy to peel or damage because greater deformation occurs locally on the bottom of a tire. Therefore, this study develops a method of measuring the road friction coefficient from the strain induced in a tire sidewall. If the tire sidewall can be used, stable measurement can be expected because the sidewall is harder to deform locally than the bottom of a tire. It has be previously confirmed that the triaxial direction loads acting on a ground contact surface of a tire and the strain induced in the tire sidewall have almost a linear relationship. By determining the experimental formulas about the relationship, we can measure road friction coefficient during car driving. This article describes the method to determine appropriate formulas with determining the optimal measurement condition of the strains induced in the tire sidewall and confirms the availability with actual driving experiments.</div>

Abstract Vehicle safety has become a priority due to the increasing number of traffic accidents. Among the technologies that contribute to active safety, the Anti-lock Braking System (ABS) stands out for preventing wheel lock and maintaining vehicle control during sudden braking. However, its effectiveness can be affected by improper use and lack of understanding of its operation. In this context, this study aims to theoretically develop an intelligent braking assistant based on Artificial Intelligence (AI), designed to work alongside the ABS system to guide drivers and correct inadequate braking patterns. The research is based on a bibliographic review and conceptual modeling of a system composed of sensors, actuators, an Electronic Control Unit (ECU), and the NXP S32K39 microcontroller, chosen for its performance and compliance with automotive safety standards. Theoretical results suggest that integrating AI into ABS systems can significantly improve response time, reduce braking distance, and increase vehicle stability in critical situations. It is concluded that this proposal represents an important conceptual advance in vehicle safety, paving the way for future developments with physical prototypes and experimental testing. Key-words: ABS; Artificial Intelligence; Vehicle Safety; Microcontroller; Smart Braking.

This research is focused on driving stability issues, which can be caused by specifics of electric vehicle (EV) powertrains. Specific driving conditions, such as intensive road curvature and low grip, require precise control from the driver and very accurate and not delayed vehicle stabilization from its active safety systems. These systems, typically anti-lock braking systems (ABS) and electronic stability programs (ESP), perform their tasks sufficiently well, but new vehicle architectures are forcing a reassessment of their reliability, sometimes requiring additional safety subsystems. In the context of EV architecture and its propulsion systems, a possible lack of stability is anticipated when operating intensive regenerative braking in EVs with a rear–wheel–drive transmission. Experimental research conducted on two popular electric vehicles confirmed this hypothesis, as additional oversteering occurs even when ESP systems have intervened. Based on the experiment, a theoretical simulation model of an EV with regenerative braking on the rear axle was created and validated in MATLAB/Simulink (R2024a). The simulations showed how relevant this issue is and how limited stability systems are; therefore, new strategies were proposed and theoretically tested to ensure car safety. These dedicated regenerative braking control subsystems enable optimal use of regenerative braking and ensure more reliable stability in slippery corners.

Anti-lock braking systems (ABSs) play a vital role in vehicle safety by preventing wheel lockup and maintaining stability during braking. However, their performance is strongly affected by variations in tire–road friction, which often limits the effectiveness of conventional controllers. This research proposes and evaluates a fuzzy logic controller (FLC)-based ABS using a quarter-vehicle model and the Burckhardt tire–road interaction, implemented in MATLAB/Simulink. Two input variables (slip error and slip rate) and one output variable (brake pressure adjustment) were defined, with triangular and trapezoidal membership functions and 15 linguistic rules forming the control strategy. Simulation results under diverse road conditions—including dry asphalt, concrete, wet asphalt, snow, and ice—demonstrate substantial performance gains. On high- and medium-friction surfaces, stopping distance and stopping time were reduced by more than 30–40%, while improvements of up to 25% were observed on wet surfaces. Even on snow and ice, the system maintained consistent, albeit modest, benefits. Importantly, the proposed FLC–ABS was benchmarked against two recent studies: one reporting that an FLC reduced stopping distance to 258 m in 15 s compared with 272 m in 15.6 s using PID, and another where PID outperformed an FLC, achieving 130.21 m in 9.67 s against 280.03 m in 16.76 s. In contrast, our system achieved a stopping distance of only 24.41 m in 7.87 s, representing over a 90% improvement relative to both studies. These results confirm that the proposed FLC–ABS not only demonstrates clear numerical superiority but also underscores the importance of rigorous modeling and systematic controller design, offering a robust and effective solution for improving braking efficiency and vehicle safety across diverse road conditions.

This paper presents a novel methodology for predicting the tire–road friction coefficient in real-time under challenging climatic conditions based on a fuzzy logic inference system. The core innovation of the proposed approach lies in the integration and probabilistic weighting of a diverse set of input data, which includes signals from ambient temperature and precipitation intensity sensors, activation events of the anti-lock braking system (ABS) and electronic stability control (ESP), windshield wiper operation modes, and road marking recognition via a front-facing camera. This multi-sensor data fusion strategy significantly enhances prediction accuracy compared to traditional methods that rely on limited data sources (e.g., temperature and precipitation alone), especially in transient or non-uniform road conditions such as compacted snow or shortly after rainfall. The reliability of the fuzzy-logic-based predictor was experimentally validated through extensive road tests on dry asphalt, wet asphalt, and wet basalt (simulating packed snow). The results demonstrate a high degree of convergence between predicted and actual values, with a maximum modeling error of less than 10% across all tested scenarios. The developed methodology provides a robust and adaptive solution for enhancing the performance of Advanced Driver Assistance Systems (ADASs), particularly Automatic Emergency Braking (AEB), by enabling more accurate braking distance calculations.

The Automatic tyre Pressure Monitoring System (TPMS) is an advanced safety and efficiency-enhancing system designed to continuously monitor the air pressure in vehicle tyres. It utilizes pressure sensors installed within the tyres to detect pressure variations and transmits real-time data wirelessly to a dashboard display or a mobile application. Upon detecting deviations from the optimal pressure range, the system promptly alerts the driver, enabling timely corrective action. By ensuring proper tyre inflation, TPMS enhances road safety, fuel efficiency, and tyre longevity, reducing the risks associated with underinflated or over-inflated tyres, such as blowouts and increased wear. The system operates using direct or indirect monitoring methods—direct TPMS employs dedicated pressure sensors, while indirect TPMS utilizes wheel speed data from the Anti-lock Braking System (ABS) to estimate pressure changes. The implementation of TPMS contributes to enhanced vehicle performance, reduced maintenance costs, and lower environmental impact by optimizing fuel consumption and minimizing carbon emissions. This system is an essential feature in modern automobiles, promoting safer and more efficient driving conditions.