Automatic Braking System Research Articles

Pressing Mechanism Design and Performance Analysis for Brake Pedal System at Low-Speed Driving

This article focuses on the modelling and analysis of an automatic braking mechanism for low-speed driving scenarios. The study aims to replicate the force exerted by a driver on the brake and accelerator pedals by strategically placing the suitable actuator within the pedal subsystem assembly. The primary objective is to model the pedal pressing mechanism for the braking subsystem and gain insights into driver ‘s behaviour during traffic congestion, thereby ensuring the effective operation of the automatic braking system. The actuator was modelled using 3D virtual environment, while the car body or dynamics was modelled using SimScape in MATLAB environment, and the simulation performance analysis was employed to evaluate the performance outcomes. It has shown that the mechanism designed mimics the actual manual pedal pressing mechanism of driver’s leg where the results show that the actuator managed to produce 150 N to reach speed at 8.5 km/h at 48 mm linear actuator stroke. Therefore, the research findings are anticipated to contribute significantly to the advancement of automatic braking systems, particularly during low speed in road traffic delay, which may help in reducing the fatigue among drivers, enhancing vehicle safety, and providing valuable insights for the development of more reliable and efficient systems within the automotive industry.

Real-time testing of AI enabled automatic emergency braking system for ADAS vehicle using 3D point cloud and precise depth information

At the forefront of automobile safety technology, Automatic Emergency Braking (AEB) represents a major advancement in collision avoidance systems. The cutting-edge technology provides an additional layer of security at pivotal times, making it a vital part of the changing landscape of vehicle safety. This research introduces an effective system for automatic emergency braking in ADAS-equipped or Autonomous vehicles using a combination of 3d lidar and stereo vision camera for a swift and robust system in the vehicle that is faster than human drivers in cases of unexpected emergencies. Utilizing the power of clustering algorithms on 3d point clouds and state-of-the-art computer vision algorithms on an RGB image mapped to a depth frame from the stereo vision camera, the system as a whole provides a comprehensive system adding to the safety of the vehicle and the passengers. Further, the efficiency of the system is studied based on various parameters. The data from an external inertial measurement unit is also utilized to derive results that support the claims of the study. The system has been developed and implemented on a passenger car that has been modified into an electric vehicle and further tested in real-world traffic conditions in autonomous driving mode. The findings of the study were having exceptionally good precision in split-second decision-making in emergency maneuvers.

Research on AEB-P Control Strategy Considering Braking Process Comfort

A control approach for the pedestrian-oriented automobile automatic emergency braking system (AEB-P) that takes driving comfort into account is suggested in an effort to further optimize the AEB-P system's control algorithm. Firstly, for the AEB-P assessment standard of C-NCAP, a braking safety distance model that takes the driver's comfort into account during braking is created. The next step was to construct a hierarchical controller using PID and MPC principles. Lastly, CarSim and Matlab/Simulink are used to build and validate the test scenarios and control methods. The simulation results demonstrate that both ride comfort and driving safety can be guaranteed by the suggested control technique.

Integrating security in hazard analysis using STPA-Sec and GSPN: A case study of automatic emergency braking system

Hazard analysis is a vital step in developing intelligent connected vehicles, aiming to eliminate or control hazards in the initial stages of system development and to provide theoretical support for the system’s safety design. However, conventional hazard analysis methods, such as Fault Tree Analysis and Failure Mode and Effects Analysis, suffer from two shortcomings: they do not account for the impact of cybersecurity factors on system safety and do not provide sufficient quantification of hazard scenarios. To this end, we propose a quantifiable hazard analysis method with security consideration, which integrates System Theoretic Process Analysis for Security (STPA-Sec) and Generalized Stochastic Petri Net (GSPN), supporting the extraction, modeling, and quantification of hazards. Specifically, we employ STPA-Sec for qualitative analysis to identify causal scenarios, safety requirements, security requirements, and the corresponding mitigations. Then, based on the identified causal scenarios, a GSPN model is established to quantify system-level hazards. A case study on a real open-source test vehicle demonstrates that the proposed method not only offers a comprehensive analysis of hazards but also provides a quantitative assessment. Comparative assessments suggest that the proposed method exhibits an advantage in terms of analysis processes (integrating security) and results (quantification).

Automatic Braking System

With the proliferation of vehicles and the integration of technology into daily transportation, ensuring road safety has become paramount. Traffic accidents, often resulting in substantial damage and casualties, persist as a global concern. The Automatic Braking System (ABS) stands as a pivotal safety innovation adopted by vehicle manufacturers worldwide. This paper explores the significance and functionality of ABS in preventing wheel lock-up during braking, thereby enabling drivers to maintain steering control. Through an examination of ABS technology, its effectiveness, limitations, and potential advancements, this research aims to contribute to the ongoing discourse on enhancing road safety measures in the modern automotive landscape.

EMERGENCY BRAKING SYSTEM IN FOUR-WHEELER

Automatic Emergency Braking (AEB) is a braking system which helps to apply brakes automatically under the condition of emergency and helps to mitigate the severity of a crash. This paper deals with the study of designing and working of automatic emergency braking system using the various fundamentals of mechanical and electronics engineering popularly known as ‘Mechatronics’. In this system Ultrasonic Sensors with the combined use of Stereo Cameras will detect an obstacle in front of the vehicle and it will tell us the relative distance between the obstacle and the vehicle. The ECU will then judge if an accident is likely to happen or not and the brakes will be applied automatically under this system. Key Words: Heat exchanger, Perforated rings, Reynolds number, Overall heat transfer coefficient.

Advanced Driver Assistance System (ADAS)

The motivations are art, elegance, and ethics. Human error and negligence cause 90% of road accidents, making the human the important part in this process while also being the weakest link. Every year, numerous accidents are reported as a result of excessive speed and poor decision-making. According to a logical concept, every 1% increase in velocity increases the probability of a fatal collision by 4% and the risk of a serious collision by 3%. Modern automobiles are a symbiosis of several electrical subsystems that work together to provide a safe and enjoyable driving experience. One of the technologies used in automotive development is the Advanced Driver Assistant System (ADAS). It is a system that monitors different parameters related to the vehicle and its surroundings in order to detect potentially dangerous circumstances early on. Sensors with sophisticated electronics, known as Sensor Network (SN), are already installed in many cars today to accomplish (ADAS) technology. A well-known ADAS is the Adaptive Speed Control system (ASC), Automatic Brake System (ABS), Warning Collision System (WCS), and Legal Restriction System, which are used to avert high-speed accidents. In addition, there are Lane Keeping Systems (LKS) and Lane Change Systems (LCS).

Method of brake mechanisms thermal load control under the influence of automated braking system load modes

Introduction (problem statement and relevance). Most of the kinetic energy of a vehicle equipped with an anti-lock braking system (ABS) is damped due to friction work in the brakes. Besides the ABS function, the automated braking system implements a lot of functions of active safety and driver assistance systems by using regular service brake mechanisms as an actuator. Overheating of brakes, namely of their friction pairs, leads to the phenomenon of critical fading, which is accompanied by a sharp decrease in braking torque. Reducing the impact of this phenomenon is a very difficult task in terms of both taking into account the cost of the braking mechanism and its minimum complexity. The proposed procedure makes it possible to assess the influence of operation of various active safety and driver assistance systems on the thermal load of the brake mechanisms. The prospect of its expansion when creating new vehicle designs is also covered.The purpose of the study is to increase the reliability of brake mechanisms, braking characteristics stability by improvement of the procedure for calculation and reduction of the brake mechanism thermal load of the wheeled vehicles equipped with the automated brake system.Methodology and research methods. Theoretical and experimental studies were conducted in the paper. In the theoretical study, the main computational tool is the finite element method (FEM) with using of licensed software packages Abaqus CAE and SolidWorks Simulink.Scientific novelty and results. The thermal load estimation procedure for brake mechanisms of vehicles that are equipped with an automated brake system has been created. The calculation procedure developed takes into account the increase in the proportion of the vehicle kinetic energy that is extinguished in the brake mechanism of the vehicle equipped with different subsystem functions.Practical significance. Application of the developed complex calculation procedure with any thermal calculation complexes on the FEM basis will allow brake system developers to reduce the costs for intermediate tests for thermal load at the design stage, and upon that, the influence of functional subsystems of the automated brake system will be taken into account. Application of the suggested improvement of the monitoring and control systems for the automated brake system will reduce the probability of fading phenomenon occurrence.

Design, Simulation, Building, and Testing of a Microcontroller-Based Automatic Drowsiness Detection, Vehicle Braking, and Alert System

Aims: The premier practical aspect of this research drive is to propose, build, simulate, and evaluate an Arduino-built automatic vehicle driver drowsiness detection and alert system.
 Study Design: The pivotal role of a reliable braking system in vehicular safety cannot be overstated, particularly considering the escalating frequency of traffic accidents, notably prevalent in Indonesia where human factors take center stage as the primary accident catalyst. An insightful poll underscores physical fatigue or drowsiness while driving as the foremost concern. Acknowledging the nuanced disparities between conventional air systems and electric systems, this research strategically directs its attention toward crafting a new system characteristic that mirrors the efficiency of the existing systems.
 Place and Period of Study: The research action engaged by the authors in a bunch of two students under the command of a professor as a part of one of his course capstone projects for the Bachelor of Science in Electrical and Electronic Engineering degree at the American International University Bangladesh (AIUB), Dhaka, Bangladesh. The authors performed their investigative tasks at AIUB from June 2023 to January 2024.
 Methodology: Recognizing the imperative to fortify vehicular safety, the integration of artificial intelligence emerges as a vital solution to assist drivers in ensuring the safety of individuals both within and outside the vehicle. This focused study, tailored specifically for Electric Vehicles (EVs), centers on the innovative application of object and distance identification methodologies to provide a comprehensive braking action indicator. Leveraging a minicomputer and a sophisticated neural network approach, images captured by a stereo camera undergo meticulous machine learning processes. This facilitates the precise categorization and measurement of distances between objects, with subsequent priority decisions determining the optimal degree of braking action. Employing an intelligent methodology, specifically fuzzy logic, the study demonstrates a successful outcome by constructing a curve while concurrently enhancing the dynamics of the pre-existing system. Moreover, a finely tuned Pulse Width Modulation (PWM) signal for braking, lasting 10 milliseconds, is intricately devised based on the study's discerning results.
 Results: The system is simulated in Proteus and tested in real time to check its functionality. The outcomes of the test showed that the system can produce the appropriate signals based on the detection of any kind of driver’s drowsiness and can stop the car.
 Conclusion: This comprehensive approach ensures the seamless replacement of components while concurrently elevating the overall performance of the braking system

Optimizing Vehicle Safety: Fuzzy Logic-Controlled Automatic Braking Systems

Optimizing Vehicle Safety: Fuzzy Logic-Controlled Automatic Braking Systems

Quantitative Testing and Analysis of Non-Standard AEB Scenarios Extracted from Corner Cases

Existing testing methods for Automatic Emergency Braking (AEB) systems mostly rely on standard-based qualitative analysis of specific scenarios, with a focus on whether collisions occur. To explore scenarios beyond the standard conduct, a comprehensive testing model construction and analysis, and provide a more quantitative evaluation of AEB performance, this study extracted three typical hazardous driving scenarios from the KITTI (The Automated Driving dataset was created by the Karlsruhe Institute of Technology in Germany and the Toyota Institute of Technology in the United States) naturalistic driving dataset using kinematic data. A DME (Data Missing Estimation) scene construction method was proposed, and these scenarios were simulated and reconstructed in PRESCAN (PRESCAN is an automotive simulation software owned by Siemens, Munich, Germany). A C-AEB (Curve-Automatic Emergency Braking) testing model was developed and tested based on simulations. Finally, a BCEM (Boundary collision evaluation model) was proposed to quantitatively evaluate AEB performance. The focus of the analysis was on the identified cornering scenario A (severely failed AEB scenario). A C-AEB testing model was constructed based on the DME scene construction method for this cornering AEB failure scenario, and it was evaluated using the BCEM. The study found that the average performance degradation rate (performance degradation rate refers to the ratio of AEB performance in the current scenario compared to the standard straightaway test) of the AEB system in this cornering scenario reached 75.44%, with a maximum performance degradation rate of 89.47%. It was also discovered that the severe failure of AEB in this cornering scenario was mainly caused by sensor system perception defects and limitations of traditional AEB algorithms. This fully demonstrates the effectiveness of our testing and evaluation methodology.

Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests

In this paper, we propose a method for the generation of simulated test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtually simulated scenarios and functional scenario libraries for autonomous driving, which can be used for the simulated verification of different ADAS functions. Firstly, the operational design domain (ODD) of a functional scenario is selected, and the weight values of the ODD elements are calculated. Then, a combination test algorithm based on parameter weights is improved to generate virtually simulated autonomous driving test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm. Then, the traffic participant elements in each test case are sampled and clustered so as to obtain hazard-specific scenarios. Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard-specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.

Study of AEB and active seat belt on driver injury in vehicle–vehicle frontal oblique crash

The safety of vehicle occupants in oblique collision scenarios continues to pose challenges, even with the implementation of Automatic Emergency Braking (AEB) systems. While AEB reduces collision risks, studies indicate it may heighten injury risks for out-of-position (OOP) occupants. To counteract this issue, the integration of active seat belts in vehicles equipped with AEB systems is recommended. Firstly, this study established an oblique angle collision scenario post-AEB activation using data from the Chinese National Automobile Accident In-depth Investigation System (NAIS) database, analyzed through Prescan software. The dynamic response of the vehicle was examined. Following this, finite element (FE) models were validated to assess the effects of collision overlap rate, AEB braking strategy, and active seat belt pre-tensioning on occupant injuries and kinematics. Under specific collision conditions, the impact of the timing and amount of seat belt pre-tensioning, as well as airbag deployment timing on occupant injuries, was also explored. Findings revealed that a 75% collision overlap rate significantly increases driver injury risk. Active seat belts effectively mitigate injuries caused by OOP statuses during AEB interventions, with the lowest Weighted Injury Criterion (WIC) observed at a pre-tensioning time of 200 ms for active seat belts. The study further suggests that optimal results in reducing occupant injuries are achieved when active pre-tensioning seat belts are complemented by appropriately timed airbag deployment.

Automatic Parking Brake System for Four Wheeler- A Review

Automatic Parking Brake System for Four Wheeler- A Review

Drivers’ trust & reliance on automated braking at intersections: Effects of automation braking profile & in-vehicle displays

An online, video-based, within-subjects experiment was conducted to investigate the effects of three different automation braking profiles and three in-vehicle display designs on drivers’ trust and reliance on automated braking systems. 36 participants watched videos of automated braking at intersections (with different braking times and displays) and rated their trust in the automation and comfort with the automation’s braking time. They then watched the same videos and were asked to press the space bar and pause the video if they believed they should take over from the automation and brake. The braking profile had a significant effect on the trust and comfort ratings, while both the braking profile and display had a significant effect on the takeover time. The findings provide insights on drivers’ trust and reliance on automation that is designed to avoid lateral collision hazards at intersections.

Design and Development of a Prototype for a Pascal’s Law & IoT applied Automatic Hydraulic Braking System

The widespread adoption of autonomous vehicles continues to increase along with various scientific and technological advancements, and this is necessitating the application of essential accident prevention measures. This research presents the design and development of an innovative hydraulic braking system utilizing readily available components and materials. The Pascal’s law of pressure is very applicable in modern hydraulic braking mechanisms. The system employs an ultrasonic sensor, a repurposed disk, a motor, and a solenoid lock controlled by a relay to create an effective braking mechanism. The primary objective of this research is to demonstrate the feasibility of constructing a functional braking system based on Pascal’s law of pressure, using locally sourced materials, catering to resource-constrained environments. The proposed hydraulic braking system functions as follows: when the ultrasonic sensor detects an object within its proximity (approximately 20 centimeters), it triggers the relay, which activates the solenoid lock. The solenoid lock's action initiates the movement of a syringe compressor, exerting pressure on the hydraulic fluid. This pressure is then transmitted to the repurposed DVD (Digital Versatile Disk), which serves as the braking element. As the disk is pressed against a surface, the resulting friction decelerates the system, effectively applying the brake. Finally, data is evaluated in the IoT (Internet of Things) platform ThingSpeak to identify safe and harmful states.

Research on Automatic Emergency Braking System Based on Target Recognition and Fusion Control Strategy in Curved Road

To address the issue of incorrect recognition in the automatic emergency braking (AEB) systems on curved roads, a target recognition model is proposed to obtain the road curvature and to calculate the relative lateral distance. Based on the information from the ego vehicle and the preceding vehicles, the accurate selection of the hazardous target is accomplished. After identifying the dangerous target, a control strategy based on the fusion algorithm is proposed, because the safety distance model and the Time-to-Collision (TTC) model both have their limitations and cannot ensure driving safety and comfort simultaneously. The TTC model is optimized according to the actual relative distance between two vehicles on curved roads, the graded warning strategy and braking intervention time are established by the TTC model. And then the graded braking strategy is designed according to the safety distance model. The simulation platform is built based on Carsim and Simulink for verification and analysis. The results demonstrate that the proposed AEB control strategy on curved roads can accurately and efficiently identify the target vehicles on curved roads, avoid false triggering issues, and improve the AEB system’s reliability. And effectively avoid collisions with target vehicles that are in the same lane, improving driving safety and comfort.

Functional Safety for Automatic Emergency Braking based on ISO 26262

The popularity of electronic systems had increased in automobiles. A lot of ECUs, electronics sensors, bus are used in automobile systems which has made the system complex. Safety analysis should be done to ensure functional safety. IEC has introduced the standard, ISO 26262 for safety analysis of electrical/electronic/programmable systems in automobiles to reduce and control systematic faults. ISO 26262 give guidelines to define and follow the techniques strategically for the entire product life cycle of the vehicle. It does not explain how functional safety is done but it will guide us throughout the process. In this paper, we have analysed the Automatic Emergency Braking system as per the ISO 26262 guidelines. AEB is a significantly important active safety system which relies on electronic sensors, ECU and electronic actuators. The proper functioning of these electronic components could save the hazard from happening. We have determined the ASIL level, we determine safety goal and safety requirement, Fault Tree Analysis (FTA), Failure mode and Effect analysis. Also, we performed an HW safety analysis.
 Keywords: Electronic Sensors, functional safety, ISO 26262, Automatic Emergency Braking, Medini Analyze, Fault Tree analysis (FTA), Electro-Hydraulic Braking System, Failure Mode and Effect Analysis, Model-Based Safety Analysis, Safety Goals, HARA, FTA

Measurement of road friction coefficient using strain on tire sidewall

If road friction coefficients can be measured in real time, the performance of advanced driver-assistance systems (ADASs), such as anti-lock braking systems (ABS) and automatic braking systems, can be improved. Generally, ADASs use information obtained from wheel speed sensors and acceleration sensors. However, it is difficult to accurately measure the road friction coefficients using these sensors. Therefore, many studies have been conducted to measure road friction coefficients using strains at the inside surface of a tire tread, which are the only places that ground road surfaces. However, sensors installed on the inside surface of a tire tread, where significant deformation occurs locally because of protrusions on road surfaces, can easily be peeled off or damaged. Therefore, this study investigated a method for measuring road friction coefficients using strains on the sidewalls of the tires. If the sidewalls of tires are used instead, stable measurements can be expected because the sidewalls of tires are harder to deform locally compared with the inside surface of a tire tread. First, using an experimental device that simulates the complete slip state of a tire, we measured the relationship between the triaxial direction loads acting on the ground surface of the tire and the strain induced on the sidewall of the tire. Subsequently, we established experimental formulas that express the relationship and devised methods for measuring the road friction coefficient. It was confirmed that the friction coefficients of multiple friction surfaces, even under triaxial direction loads, could be accurately measured using the proposed method. In conclusion, the proposed method was confirmed to be able to measure the load acting on a tire and friction coefficient of the tire grounding surface at low speeds and under full-slip conditions.

EEG-based emergency braking intention detection during simulated driving

BackgroundCurrent 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.MethodsTo 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.ResultsWe 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.ConclusionsThe 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.