Braking Assistance Research Articles

A Systematic Review of Emergency Braking Assistant System to Avoid Accidents Using Pulse Width Modulation and Fuzzy Logic Control Integrated with Antilock Braking

The increasing number of fatalities in nations across the world because of a deficiency of protective technology in automobiles has created a chaotic scene in recent years. However, the car driver’s Perception-Reaction Time (PRT) plays an important variable during such accidents and emergencies. The Anti-Lock Braking System (ABS) seems a viable technology, which today is used to prevent tires from sliding during quick brakes, whereas EBS is designed for braking assistance during tuning or emergency braking. Physical weight, rotational rigidity, diameter, and tire material strength of the vehicle are all used to simulate them. Advance Driver Assistance Systems (ADAS), which include Anti-lock brakes (ABS) and Emergency Braking Systems (EBS), are the foremost viable technique for minimizing the environmental impact and uncertainties of driving road transportation. The presented systematic review aims to deliver a ground-level analysis that can be used to enhance the safety of motor vehicle driving, reduce wheel slip to achieve the best possible stopping distance in commercial and specialized vehicles, and influence future transportation. In this study, the most widely utilized technologies for ADAS have been reviewed and discussed. Various sensors used to improve braking and vehicle performance have been systematically studied in the context of low power-consuming techniques like pulse width modulation. An analysis of emergency braking procedures performed by riders with varying degrees of braking expertise was conducted using previously collected experimental data, and the results were used to conclude potential loss of control situations.

Development of an Advanced Brake Assist System for City Buses

Development of an Advanced Brake Assist System for City Buses

EEG-Based Emergency Braking Prediction Using Data Ablation and SVM Classification

Contemporary advanced driver assistance system (ADAS) features for semi-autonomous vehicles include braking assistance during collision avoidance. Although pre-collision detection typically relies on sensing systems to enable production vehicles to perceive oncoming road obstacles, the physiological state of the driver is not measured to predict emergency braking. On the other hand, previous driving simulation experiments have demonstrated the ability of regularized linear discriminant analysis (RLDA) to predict pre-collision braking using brain signals from multiple electroencephalogram (EEG) electrodes. In contrast, the current study used EEG data from these previous experiments to determine the quality of support vector machine (SVM) predictions as a first step towards realizing a brain-computer interface for emergency braking. Power spectral density (PSD) features were extracted from the EEG of one electrode to train and evaluate an SVM. Through a novel data ablation analysis, the optimal number of PSD components was determined to optimize model classification quality measured by the area under the curve (AUC). A comparison of the proposed model to the previous RLDA and other machine learning methods indicated that the SVM had a superior AU C. Thus, the proposed model is a candidate for assisting ADASs with pre-collision detection. Moreover, since the proposed model only utilized one electrode, our study potentially contributes to the facilitation of brain-computer interfaces for autonomous vehicles.

Motorcycle emergency steering assistance: A systematic approach from system definition to benefit estimation and exploratory field testing

Braking assistance systems are already contributing to improving motorcyclists’ safety, however, research on emergency systems acting on the steering is lacking. These systems, already available for passenger cars, could prevent or mitigate motorcycle crashes in which safety functions based only on braking are ineffective. The first research question was to quantify the safety impact of diverse emergency assistance systems acting on the steering of a motorcycle. For the most promising system, the second research question was to assess the feasibility of its intervention using a real motorcycle.Three emergency steering assistance systems were defined in terms of Functionality, Purpose, and Applicability: Motorcycle Curve Assist (MCA), Motorcycle Stabilisation (MS), and Motorcycle Autonomous Emergency Steering (MAES). Experts evaluated each system’s applicability and effectiveness based on the specific crash configuration (using Definitions for Classifying Accidents — DCA), the Knowledge-Based system of Motorcycle Safety (KBMS), and the In-Depth Crash Reconstruction (IDCR). An experimental campaign was conducted with an instrumented motorcycle to assess the rider’s reaction to external steering input. A surrogate method for an active steering assistance system imparted external steering torques in correspondence with a lane change to analyse the effect of the steering inputs on motorcycle dynamics and rider controllability.MAES globally got the best score for each assessment method. MS received better evaluations than MCA in two out of three methods. The union of the three systems covered a sizeable fraction of the crashes considered (maximum score in 22.8% of the cases). An estimation of the injury potential mitigation, based on injury risk functions for motorcyclists, was made for the most promising system (MAES). The field test data and video footage showed no instability or loss of control, despite the high intensity (>20Nm) of the external steering input. The rider interviews confirmed that the external action was intense but manageable.For the first time, this study presents an exploratory assessment of the applicability, benefits, and feasibility of motorcycle safety functions acting on the steering. MAES, in particular, was found applicable to a relevant share of crashes involving motorcycles. Remarkably, applying an external action to produce a lateral avoidance manoeuvre proved feasible in a real-world test setting.

Centralized and distributed coupling traction control of electric vehicles on split ramps

The pass ability on split ramps is an important dynamics performance of electric vehicles (EVs). However, the current traction control system (TCS) requires the vehicle to have a high-intensity driving capability and sometime the braking assistance control is required, which is a waste of cost and energy. To improve the traction control ability and reduce the power demands, a centralized and distributed coupling TCS based on the designed dual-mode coupling drive system is proposed. The coupling traction control mechanism is analyzed, the TCS based on S-line control and optimal slope-seeking algorithm is designed, and the traction control effect is verified by the co-simulation and real vehicle road test. The research results show that adopting coupling traction control can give full play to system power to improve the pass ability, the optimal slip rate control can be realized and the control difficulty also be reduced. It laid a theoretical basis for the pass ability improvement of EVs on split ramps and the new application of dual-mode coupling drive system.

User-Adaptive Brake Assist System for Rolling Walkers

Rolling walkers are popular mobility aids for older adults. A rolling walker usually has two swivel front wheels and two non-swivel rear wheels. It is designed to improve stability while walking and reduce the risk of falling. However, a considerable number of users have come close to or experienced falling. We developed a user-adaptive brake assist system for the walker. In the system, the usage of a walker is modeled in combination with the walking speed and the distance from the walker to the user. A brake pattern is generated based on usage data interpolated using the inverse distance weighting method. The pattern is referenced to activate brakes with the corresponding strength while walking. The applicability was confirmed by analyzing the walking data of two older adults, and the usability was positively evaluated in experiments with seven young adults wearing elderly simulation suits.

Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist.

Many advanced driver assistance systems (ADAS) are currently trying to utilise multi-sensor architectures, where the driver assistance algorithm receives data from a multitude of sensors. As mono-sensor systems cannot provide reliable and consistent readings under all circumstances because of errors and other limitations, fusing data from multiple sensors ensures that the environmental parameters are perceived correctly and reliably for most scenarios, thereby substantially improving the reliability of the multi-sensor-based automotive systems. This paper first highlights the significance of efficiently fusing data from multiple sensors in ADAS features. An emergency brake assist (EBA) system is showcased using multiple sensors, namely, a light detection and ranging (LiDAR) sensor and camera. The architectures of the proposed ‘centralised’ and ‘decentralised’ sensor fusion approaches for EBA are discussed along with their constituents, i.e., the detection algorithms, the fusion algorithm, and the tracking algorithm. The centralised and decentralised architectures are built and analytically compared, and the performance of these two fusion architectures for EBA are evaluated in terms of speed of execution, accuracy, and computational cost. While both fusion methods are seen to drive the EBA application at an acceptable frame rate (~20 fps or higher) on an Intel i5-based Ubuntu system, it was concluded through the experiments and analytical comparisons that the decentralised fusion-driven EBA leads to higher accuracy; however, it has the downside of a higher computational cost. The centralised fusion-driven EBA yields comparatively less accurate results, but with the benefits of a higher frame rate and lesser computational cost.

Review of Vehicle Active Safety Systems and Their Coordinated Control

Background:It is obvious that the safety concern associated with a vehicle is greatly valued by all, whether it is now or in the future, the automobile safety issue is the hotspot and the focus of the research by experts and scholars. The continuous increase in car ownership brings convenience to people's life and also poses a threat to people's life and property. Vehicles’ active safety system is the hotspot of current research and development, which plays an important role in automobile safety. Firstly, the vehicle’s active safety technology and its development are introduced. Then, a review is carried out examining the Anti-Lock Brake System (ABS), the Electronic Brake force Distribution (EBD/CBC), the Brake Assist System (BAS/EBA/BA), the Traction Control System (TCS/ASR), the Vehicle Stability Control (VSC/ESP/DSC), etc. At present, there are many patents on the control of each subsystem, but few patents on the integrated control for the active safety of vehicles.Objective:The main contents of this paper are as follows: the control strategies and methods of different active safety systems, the strategies to improve the stability of a vehicle control system and ensure the effectiveness of active safety system control. It provides a reference for the development of active safety control technology and patent.Methods:Through the analysis of different control algorithms and control strategies of Anti-lock and braking force distribution systems, it is pointed out that the switching of EBD/ABS coordinated control strategy according to slip rate can make full use of slip rate and road adhesion coefficient to improve the safety of the system. For the BAS, the slip problem is solved through the combination of the Mechanical Assistant Braking System (MABS) and Electronic Braking Assistant (EBA) system by measuring the distance and the speed of the vehicle ahead. The optimal slip rate control is realized by different control algorithms and control strategies of the traction control system. It is pointed out that the adaptive fuzzy neural controller should be used to control the yaw angular velocity and centroid side angle of the Electronic Stability Program (ESP), which has a good effect on maintaining vehicle stability. A sliding mode variable structure controller combined with constant speed control and law control is used to control the yaw moment.Results:Through the coordinated control strategy of EBD/ABS, the slip rate and road adhesion coefficient were fully utilized by switching according to the slip rate. The problem of the sliding slope was solved by MABS with EBA system. The ESP should use an adaptive fuzzy neural controller to control the yaw angular velocity and centroid side angle, and adopt the joint sliding mode variable structure controller which combines the ABS control and the yaw moment control. Through the optimal control theory, the coordinated control of each subsystem can significantly improve driving stability, riding comfort, fuel economy and so on.Conclusion:This study adopts different control strategies and control algorithms for different active safety control systems and makes full use of the tire-road friction coefficient and slip ratio optimal slip ratio. These focus on accurate control of control variables such as yawing angular velocity, centroid side-slip angle, yawing moment and finally ensure the vehicle braking stability, robustness of the controller and the lateral stability of the vehicle.

Concept of Grip Assistive Braking on Four Wheelers

Braking systems in all the segments of vehicles seems to be one of the most crucial developing systems. Many research and developments are carried out in terms of braking of a vehicle in order to enhance the braking efficiency and reduction in stopping distance. This article depicts the concept of assistive braking on four wheelers where we elaborately describe the Braking distance, Thinking distance and Stopping distance after which our concept of braking is proposed with all the technical aspects along with the various components and its efficiency in our real time use. This research article mainly focuses on technical aspects that aim to reduce the reaction time and reflex actions of the driver which in turn is used as a factor to reduce the stopping distance of the vehicle. Various types of biological reactions by a human are observed and then a particular muscular reaction is chosen as a source of intimation for our braking system. When an object is spotted by the driver, the reflex action takes place via a receptor, sensory neurons, spinal cord and from where the muscular reaction takes place. A unique steering with an array of grip sensors is designed and incorporated also with various functions of the vehicle but for this instance, we mainly focus on braking assistance of the vehicle. Thus the unique steering wheel plays the role of connection between vehicle and the driver.

Path Planning and Tracking for Vehicle Collision Avoidance in Lateral and Longitudinal Motion Directions

In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies. In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.

Path Planning and Tracking for Vehicle Collision Avoidance in Lateral and Longitudinal Motion Directions

In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies. In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.

Analysis of the Possibilities of Using a Driver’s Brain Activity to Pneumatically Actuate a Secondary Foot Brake Pedal

The study deals with the use of the driver’s brain activity for wireless remote control of the pneumatic actuator exerting pressure on the secondary foot brake pedal. The conducted experimental tests confirm that bioelectrical signals (BES) induced by muscle tension within the head can be used for wireless remote control of a pneumatic actuator to exert a pressure force on a foot brake pedal for disabled drivers during car emergency braking. It has been shown that the BES artefacts generated by muscular tension inside the head (e.g., movement of the face and eyelids, clenching of jaws, and pressing the tongue on the palate) are the easiest to control of the pneumatic systems. The proposed car braking assistance system controlled by the driver’s brain activity can improve the driving safety of disabled people, e.g., by reducing the reaction time of pneumatically assisted emergency braking.

Better Safety with Emergency Brake Assist for Motorcycles

Better Safety with Emergency Brake Assist for Motorcycles

Research on Intellirent Brake Assist System Based on Motor Drive and ECU Control

Autopilot is divided into three parts: they are perceptual positioning, planning decision and execution control. In this project, we study its implementation control part, the intelligent brake boosting system. The intelligent brake boosting system is the most advanced intelligent braking system in the world. It is an essential and key component for autonomous driving and driverless driving. At the same time,it is the inevitable trend and the best solution for the next generation braking system of automobiles. This project describes the composition, functions and advantages of the intelligent brake boosting system, and analyzes the key component controllers, electric brake actuators and product performance.

An Active Safety System for Low-Speed Bus Braking Assistance

Accidents in which buses or coaches are involved cause thousands of injuries and fatalities every year. To reduce their number and severity, this paper describes an advanced driver assistance system (ADAS) based on a haptic throttle pedal and emergency braking. It also proposes a computationally efficient algorithm with a methodology based on three main concepts: a simplified but accurate vehicle model, an efficient collision detection system considering the driver's intention and pedestrians wandering around the vehicle, and a risk evaluation system to generate warnings and emergency braking signals. Finally, the performance of the proposed ADAS is validated using a driving simulation cabin with a very realistic urban scenario and original elements from real buses. The results show a statistically significant improvement in safety, as the number of collisions and high-risk situations are clearly minimized, the reaction time to press the brake pedal is improved, and the time to collision increased in emergency situations. Implementation of the proposed ADAS into city buses would potentially improve safety, reducing the frequency and severity of accidents in pedestrians.

Risk Predictive Braking Assistance Control Based on Driving Intelligence

Risk Predictive Braking Assistance Control Based on Driving Intelligence

Brake Assist Mechanism for Reducing Landing Impact on Descending a Mountain by Foot

Brake Assist Mechanism for Reducing Landing Impact on Descending a Mountain by Foot

Design and Development of Vehicle Reversing Brake Assist System

Through the analysis of the current vehicle reversing braking system, the paper introduces the safety hazard of the vehicle in the reversing state and the factors that affect reversing braking effect and the weakness of control system. Aiming at the existing problems, designing and development of vehicle reverse brake assist system. This method that uses the STC12C5A60S2 single-chip receives GPS vehicle speed signal, and reversing radar distance signal to determine the safety status of the vehicle reversing can realize the accelerator pedal and brake control, and improve the safety of vehicle reversing. To avoid the driver accidentally stamped the accelerator or delayed braking caused vehicle accident is of great significance to the optimization of vehicle braking system.

A New Decentralized Bayesian Approach for Cooperative Vehicle Localization Based on Fusion of GPS and VANET Based Inter-Vehicle Distance Measurement

Accurate and reliable vehicle localization is a key component of numerous automotive and Intelligent Transportation System (ITS) applications, including active vehicle safety systems, real time estimation of traffic conditions, and high occupancy tolling [1]. Various safety critical vehicle applications in particular, such as collision avoidance or mitigation, lane change management or emergency braking assistance systems, rely principally on the accurate and reliable knowledge of vehicles? positioning within given vicinity.

Detection of braking intention in diverse situations during simulated driving based on EEG feature combination

Objective. We developed a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We further investigated to what extent these neural correlates can be used to detect a participantʼs braking intention prior to the behavioral response. Approach. We measured electroencephalographic (EEG) and electromyographic signals during simulated driving. Fifteen participants drove a virtual vehicle and were exposed to several kinds of traffic situations in a simulator system, while EEG signals were measured. After that, we extracted characteristic features to categorize whether the driver intended to brake or not. Main results. Our system shows excellent detection performance in a broad range of possible emergency situations. In particular, we were able to distinguish three different kinds of emergency situations (sudden stop of a preceding vehicle, sudden cutting-in of a vehicle from the side and unexpected appearance of a pedestrian) from non-emergency (soft) braking situations, as well as from situations in which no braking was required, but the sensory stimulation was similar to stimulations inducing an emergency situation (e.g., the sudden stop of a vehicle on a neighboring lane). Significance. We proposed a novel feature combination comprising movement-related potentials such as the readiness potential, event-related desynchronization features besides the event-related potentials (ERP) features used in a previous study. The performance of predicting braking intention based on our proposed feature combination was superior compared to using only ERP features. Our study suggests that emergency situations are characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by neurotechnology based braking assistance systems.