Automatic Braking System Research Articles

Description and tests of a control system for automatic emergency braking of a car with an algorithm for predicting the adhesion of automobile tires to the road surface

Every year the number of cars in the world is steadily growing, which in turn leads to an increase in road accidents. Russia, as the largest country by area in the world, has a number of traffic-related features that are not typical for other regions, including changing road and climatic conditions. The sharp increase in the number of accidents in the first hours after precipitation is especially noticeable, that indicates the difficulty to adapt to the changing traffic situation for vehicle drivers. More than 60% of collisions, according to the traffic police databases for 2018-2019, occur due to incorrect predictions by drivers of the braking distance of their cars. As a rule, this can happen due to the inexperience of the driver, incorrect assessment of the current adhesion properties of the roadway, distraction to a mobile phone, the climate or multimedia system of the car, movement with insufficient visibility. Preventing collisions occurring in such scenarios is the main task of an intelligent driver assistance system such as the automatic emergency braking system. The main part of this system is an adequate assessment of dynamically changing road and climatic conditions and warning the driver of a possible collision hazard, as well as automatic action on the vehicle’s brake drive to prevent a collision. The article describes control algorithms for assessing and predicting dangerous situations.

Fabrication of brake overrides system with sleep detector

When compared with the olden day’s life span of a human is decreased. The death rate due to accidents is extremely increased because of the rise in the number of vehicles. This automatic braking system will positively lessen the number of accidents due to brake failures. An Ultrasonic sensor is positioned in front of the vehicle and that system consists of an emitter and a receiver. The signal to the ultrasonic sensor module is based upon the distance of the object it actuates the motor shut down or brakes. Concerning the distance of the object from the vehicle the brakes are applied at long-distance input and the motor is shut down at shorter range input. Brakes are actuated by using the 12 V wiper motor. The movement of the vehicle is powered by a 24V DC type box motor which is directly connected to the wheels by a chain-driven mechanism. In addition to the smart stop technology, an IR sensor also detects the movement of the eye and switches off the motor if no movement is found for 3 seconds or more. Even in the case of Autonomous vehicles, this will prevent accidents at the time of unintentional inputs.

Automatic Speed Reduction to Avoid Accidents

Abstract: We analyse the automatic braking system to reduce the speed automatically to avoid the panic action of automobiles. Stop the vehicle when it is in the accidental zone, we applied the velocity calculation according to the weight of the body which can reach to required velocity state to avoid the accident. We are using the electromagnetic braking system to reduce the speed of vehicle it is about meet the accident. In this we achieve a better braking system and also it is relevant to future automobiles. The prototype has been prepared depicting the technology and tested as per the simulated conditions. In future the actual model may be developed depending on its feasibility

Prediction of stopping distance for autonomous emergency braking using stereo camera pedestrian detection

Autonomous vehicle (AV) attracts a lot of research interest because of their ability to reduce road fatalities and save people's lives. Pedestrian detection and collision avoidance are the crucial parts of AVs. The existing pedestrian detection systems don't guarantee better accuracy under complex scenarios, poor light conditions, and low complexity overhead. Moreover, these systems rely on monocular camera object detection. Hence, this paper aims to propose a stereo-vision based pedestrian detection and collision avoidance system for AVs. It uses two cameras fixed at a specific distance apart to scan the environment. Once a pedestrian is detected, the system calculates the distance. The Automatic Emergency Braking System (AEBS) controller algorithm will activate the AEB if the estimated distance is less than 3.3 m, considered safe. MATLAB is used for the implementation, and the experimental results reveal that the proposed method is promising in terms of prediction accuracy and minimizing fatalities.

Differential benefit of sensor system field-of-view and range in pedestrian automated emergency braking systems

Objective Current Pedestrian Automatic Emergency Braking (P-AEB) systems often use a combination of radar and cameras to detect pedestrians and automatically apply braking to prevent or mitigate an impending collision. However, these current sensor systems might have a restricted field-of-view (FOV) which may not detect all pedestrians. Advanced sensors like LiDAR can have a wider FOV that may substantially help improve detection. The objective of this study was to determine the influence of FOV and range on the effectiveness of P-AEB systems to determine the potential benefit of advanced sensors. Methods This study utilized vehicle-pedestrian crashes from the Pedestrian Crash Data Study (PCDS) to calculate pre-crash pedestrian and vehicle trajectories. A computational model was then applied to simulate the crash with a hypothetical P-AEB system. The model was designed to be able to vary the system’s field-of-view (FOV), range, time-to-collision of activation, and system latency. In this study we estimated how the FOV and range of advanced sensors could affect P-AEB system effectiveness at avoiding crashes and reducing impact speed. Sensor range was varied from 25 − 100 m and sensor FOV was varied from ±10° to ±90°. Results Sensors simulated with a range of 50 m or greater performed only approximately 1% better than with a 25 m range. Field-of-view had a larger effect on estimated system avoidance capabilities with a ± 10° FOV sensor estimated to avoid 46-47% of collisions compared to 91-92% for a ± 90° FOV sensor. The system was able to avoid a greater percentage of cases in which the vehicle was traveling straight at sensor FOVs of ±30° and below. Among the unavoided crashes with a sensor FOV of ±90°, the average impact velocity using a 100 m range sensor was 7.4 m/s which was 3.1 m/s lower than a 25 m range sensor. Conclusions Sensor ranges above 25 m were not found to significantly affect estimated crash avoidance potential, but had a small effect on impact mitigation. Sensor FOV had a larger effect on crash avoidance up to a FOV of ±60° with little additional benefit at larger FOVs.

Penerapan Sensor Ultrasonic SRF05 Berbasis Mikrocontroller ATMega 8535 Untuk Sistem Pengereman Otomatis

Transportation technology using electric energy is growing. Electric cars are increasingly being used to reduce fuel oil usage. In addition to the increasingly sophisticated performance of electric cars, the vehicle safety system must also be improved. The existence of Artificial Intelligence is one of the developments in technological sophistication in a world that is increasingly unstoppable and unavoidable. One of the most popular luxury cars in Indonesia, one of which is the Tesla, which is a sophisticated car with the support of an artificial intelligence system, making Tesla an electric car with an automatic control vehicle feature that can run without using a human driver and can provide safety protection for the driver by using automatic braking system in case of an unexpected accident. This journal proposes a future concept design for the forerunner of automatic braking for electric cars using the ATMEGA 8535 microcontroller as the main automatic control and ultrasonic distance sensor. The PWM signal from the microcontroller is used to adjust the speed of the DC motor as the main driving force of the vehicle. The display of the distance between the vehicle and the obstacle can be seen on the LCD and the buzzer is used as an indicator of the safe distance of the car. The experiment was carried out using an RC car with the area to be protected> 500cm (5m), if the SRF05 ultrasonic sensor detects an obstacle at that distance, the SRF05 ultrasonic sensor will send a signal to the microcontroller and turn on an alarm in the form of a buzzer so that the SRF05 ultrasonic sensor will stop signal propagation.

Extracting Rules from Autonomous-Vehicle-Involved Crashes by Applying Decision Tree and Association Rule Methods

Autonomous vehicles (AVs) can dramatically reduce the number of traffic crashes and associated fatalities by eliminating the avoidable human-error-related crash contributing factors. Many companies have been conducting pilot tests on public roads in several states in the U.S. and other countries to accelerate AV mass deployment. AV pilot operations on Californian public roads saw 251 AV-involved crashes (as of February 2020). These AV-involved crashes provide a unique opportunity to investigate AV crash risks in the mixed traffic environment. This study collected the AV crash reports from the California Department of Motor Vehicles and applied the decision tree and association rule methods to extract the pre-crash rules of AV-involved crashes. Extracted rules revealed that the most frequent types of AV crashes were rear-end crashes and predominantly occurred at intersections when AVs were stopped and engaged in the autonomous mode. AV and non-AV manufacturers and transportation agencies can use the findings of this study to minimize AV-related crashes. AV companies could install a distinct signal/display to inform the operational mode of the AVs (i.e., autonomous or non-autonomous) to human drivers around them. Moreover, the automatic emergency braking system in non-AVs could avoid a significant number of rear-end crashes as, often, rear-end crashes occurred as a result of the failure of following non-AVs to slow down in time behind AVs. Transportation agencies can consider separating AVs from non-AVs by assigning “AV Only” lanes to eliminate the excessive rear-end crashes resulting from the mistakes of human drivers in non-AVs at intersections.

Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

In 2015, there were 319,195 police reported vehicle-animal crashes, resulting in 275 vehicle occupant fatalities. Animal-detecting automatic emergency braking (AEB) systems are a promising active safety measure which could potentially avoid or mitigate many of these crashes by warning the driver, utilizing automatic braking, or both. The purpose of this study was to develop and characterize a target population of vehicle-animal crashes applicable to AEB systems and to analyze the potential benefits of an animal-detecting AEB system. The study was based on two nationally representative databases, Fatality Analysis Reporting System and the National Automotive Sampling System’s General Estimates System, and a naturalistic driving study, SHRP 2. The target population was restricted to vehicle-animal crashes that were forward impacts or road departures and involved cars and light trucks, with no loss of control. Crash characteristics which may influence the performance of AEB such as lighting, weather, pre-crash movement, relation to junction, and first and worst harmful events, were analyzed. The study found that the major influences on the effectiveness of animal AEB systems were: weather, lighting, pre-crash movements, and the crash location. Six potential target populations were used to analyze the potential effectiveness of an animal AEB system, with effectiveness ranging between 21.6% and 97% of police reported crashes and between 4.1% and 50.8% of fatal vehicle-animal crashes. An AEB system’s ability to function in low light and poor weather conditions may enable it to avoid a substantially higher proportion of crashes.

AUTOMOTIVE COLLISION AVOIDANCE SYSTEM (ACAS) APPLICATION

Automotive collision avoidance system is a system that avoid collision when danger occur. It is also very helpful in order to alert the drivers and passengers. The system also reacts to danger automatically, faster, and more efficient. The system is implemented on a remote control car. The remote control car can automatically brake when the difference between front ultrasonic sensor and obstacle is less than 35cm. While the system applies automatic braking system a red LED will light on. Furthermore, the system is also surrounded with another 3 ultrasonics which is on both sides and at the back of the prototype. In this project, this system is using 2 controllers which is Raspberry Pi 4B and Arduino Uno. A camera module is connected to Rasberry Pi which detects the corresponding object. Next, the ultrasonic sensor is used to detect distance and send the reading to Arduino Uno to calculate distance. LCD is used to display the calculated distance. This system also uses dc motor to control the movement of the remote control car through the Arduino Uno controller LED’s and a buzzer is used as alerting devices for the prototype.

Implementation paper of Traffic Signal Detection and Recognition using deep learning

Traffic boards and traffic signals are used to maintain proper traffic through busy roads. They help to recognize the rules to follow when driving the vehicle. These signs warn the distracted driver, and prevent his/her actions which could lead to an accident. We have proposed a system which can help recognize these boards and signals at real time thus avoiding major mishap. A real-time automatic sign detection and recognition can help the driver, significantly increasing his/her safety. Lately traffic sign recognition has got an immense interest lately by large scale companies such as Google, Apple and Volkswagen etc. which is driven by the market needs for intelligent applications such as autonomous driving, driver assistance systems (ADAS), mobile mapping, Mobil eye, Apple, etc. Hence, here, we have implemented to do the same with cost efficient manner using Raspberry Pi. The proposed system detects the traffic board or traffic signals, capture its image which through deep learning approach recognizes the same to give result on dashboard as well it gives the measures of distance from front obstacle which helps to implement brake system if obstacle is near. PiCam is used to capture images of traffic sings and is connected to RaspberryPi. Monitor is used to display required output, showing type of sign and distance of collision. This proposal will avoid large number of accidents occurring at bridges and work in progress area due to automated braking system and simultaneous reduce death ratio.

An ultrasonic sensor based automatic braking system to mitigate driving exhaustion during traffic congestion

Traffic congestion has been the most tiresome encounter since the initiation of vehicle advancement. Many braking systems have been designed by researchers in previous studies, but this study is primarily focused on a braking system that is affordable to all because it is based on a simple Arduino-controlled system. Moreover, it is assistive on everyday traffic commutes rather than highway driving, which is relatively rare for normal drivers. As more and more vehicles crowd the roads, the more problematic it is becoming for the drivers, which is ultimately leading toward increment in the frontal car accidents. In this study, an electro-mechanical braking system has been deployed that assists the driver during the jam-packs by measuring the exact distance between the driven and forthcoming vehicle or any obstacle by applying the brakes without the driver pressing the brake pedal to ultimately bring the vehicle to a halt without any fear of vehicle collision. An ultrasonic sensor is used at the front bumper grille that measures the distance between the two closing vehicles. The total time to halt the vehicle has been calculated as 0.6 s, while the critical distance for the sensor has been set as 1-m. Furthermore, the stepper motor drivers are set at the maximum current output of 2.5 amps with a 12-volt battery connected in parallel to the motors. It is found that theoretical stopping time is in good agreement with the experimental stopping time to completely press the brake pedal and halt the car.

Field testing the applicability of motorcycle autonomous emergency braking (MAEB) during pre-crash avoidance maneuver

Objective Autonomous Emergency Braking (AEB) is a promising technology for crash avoidance or pre-crash impact speed reduction through the automatic application of braking force. Implementation of AEB technology on motorcycles (MAEB) is still problematic as its interaction with the rider may compromise the safety. In previous studies, MAEB interventions at low decelerations were shown to be easily manageable by common riders in straight line condition, but they were not previously tested in lateral maneuvers such as lane change and swerving, which are common in pre-crash situations. The objective of this paper is to assess the applicability of MAEB activation during lateral avoidance maneuver and to estimate its benefits in this scenario. Methods Field tests were carried out involving common riders as participants, using a test protocol developed on the experience of previous studies. The test vehicle was a sport-touring motorcycle equipped with an automatic braking system that could be activated remotely by researchers to simulate MAEB intervention. The motorcycle was equipped with outriggers to prevent capsizing. The Automatic Braking (AB) interventions using a nominal deceleration of 0.3 g were deployed at pseudo-random times in conditions of straight-line travel and a sharp lane-change maneuver emulating a pre-crash avoidance action. The straight-line trials were used as the reference condition for analysis. Results Thirty-one participants experienced AB interventions in straight-line and lane-change at an average speed of 44.5 km/h. The automatic braking was deployed in all the key phases of the avoidance maneuver. The system reached a deceleration of 0.3 g for a time of intervention of approximately 1 s. The participants were consistently able to control the vehicle during the automatic braking interventions and were always able to complete the lane-change maneuver. The speed reductions obtained with the AB interventions during lane change were very similar to those obtained in the straight-line conditions. Conclusions MAEB interventions with decelerations up to 0.3 g can be easily managed by motorcycle riders not only in straight-line conditions but also during an avoidance maneuver. Further investigations using higher deceleration values are now possible.

Model-Based Systemic Hazard Analysis Approach for Connected and Autonomous Vehicles and Case Study Application in Automatic Emergency Braking System

Model-Based Systemic Hazard Analysis Approach for Connected and Autonomous Vehicles and Case Study Application in Automatic Emergency Braking System

Automatic braking system using fuzzy logic method

This research is design an autonomous car control system that can reduce the rate of traffic accident. Car prototype is designed by using Fuzzy Logic Control. Fuzzy logic is as main component of artificial intelligence has significantly influence the design controlled system. Fuzzy logic system is integrated with the arduino Mega 2560 microcontroller as the central control car prototype. In this paper, the Fuzzy logic used Mamdani method and 28 rule base. The fuzzy system was made based on fuzzy mechanism which are fuzzification, inference and defuzzification. Fuzzy logic has a variable input and output variables. Input variables used in this research is the distance between car and obstacle; the speed of car prototype and the output variable is the brake angle. The test is done with 5 variations of distance that is 100 cm, 125 cm, 150 cm, 175 cm, and 200 cm and two variations of speed that are 54 cm/s, and 63 cm/s. The test results showed that the prototype of the car was able to stop with a range of 22 cm - 35 cm from obstacle. The highest deceleration value at the speed of 63 cm/s is - 49.22 cm/s2 and the accuracy of braking on a system that has been designed is between 99.91% -100%.

Collision Prediction and Optimal Trajectory Generation for Collision Avoidance Systems in Trackless Mobile Machines

The continued high number of fatalities associated with Trackless Mobile Machines (TMMs) in South Africa have led to the introduction of Collision Avoidance System (CAS) regulations in the Mine Health and Safety Act in 2015. This has lead to the profusion of technologically-immature CASs from third-party vendors, all of which are centered on automatic stopping and braking systems. These braking systems often result in trivial or ineffective solutions, proving costly to mining operations. The combination of braking and steering control in CASs may substantially increase the solution space and provide far safer and more efficient manoeuvres. A recursive non-linear collision prediction estimator and optimal trajectory generation model was developed to evaluate the potential contribution of the addition of steering to CASs. Three independent optimal trajectory generation models are proposed to compete against one another in an attempt to synthesize the safest, most predictable, and efficient trajectory. A deep reinforcement learning, lattice optimization and Monte Carlo hyper sampling path planning model’s trajectories are evlauated using the Earth Moving Equipment Safety Round Table (EMESRT) interaction scenarios. Initial results indicate increased CAS solution spaces in collision-avoiding scenarios, providing safer and more effective solutions in high velocity vehicle interactions.

Research on Virtual Test of Automobile Automatic Emergency Braking System on Ice-Snow Road

In view of the severe weather conditions in cold regions, the basic characteristics and braking distance of ice snow covered pavement are analyzed. This paper uses the PreScan/CarSim/Simulink software co-simulation method to test the automatic emergency braking (AEB) system on ice-snow roads. Through the appropriate adjustment of the time to collision (TTC) threshold, the car makes automatic emergency braking on the road with low friction coefficient system can achieve the effect of collision avoidance and injury reduction.

Research on subjective evaluation method of automatic emergency braking (AEB) for passenger car

With the continuous development of automotive active safety technology, automatic emergency braking technology has been widely used, playing an important role in avoiding rear-end collisions or collisions with pedestrians and other traffic participants. This article analyzes the technical characteristics of the automatic emergency braking system, put forward subjective evaluation index, select vehicle-to-vehicle, vehicle -to-pedestrian and other typical scenes for automatic emergency braking subjective evaluation and actual vehicle verification, and constructed a subjective evaluation system for automatic emergency braking of passenger car.

The safety potential of automatic emergency braking and adaptive cruise control and actions to improve the potential

The study investigates the potential of Automatic Emergency Braking (AEB) and Adaptive Cruise Control (ACC) systems to prevent fatal rear-end, intersection and pedestrian crashes in Finland. The sy...

An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle

Driver’s intention of the front vehicle plays an important role in the automatic emergency braking (AEB) system. If the front vehicle brakes suddenly, there is potential collision risk for following vehicle. Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. According to the recognized driver’s intention transmitted by Internet of vehicles, an AEB model for the following vehicle is proposed, which can dynamically change the critical braking distance under different driving conditions to avoid rear-end collision. In order to verify the performance of the proposed models, we conducted driver’s intention recognition and AEB simulation tests in the cosimulation environment of Simulink and PreScan. The simulation test results show that the average recognition accuracy of the proposed BP-HMM model was 98%, which was better than that of the BP and HMM models. In the Car to Car Rear moving (CCRm) and Car to Car Rear braking (CCRb) tests, the minimum relative distance between the following vehicle and the front vehicle was within the range of 1.5 m–2.7 m and 2.63 m–5.28 m, respectively. The proposed AEB model has better collision avoidance performance than the traditional AEB model and can adapt to individual drivers.

Attention and Threat Detection: Warnings and How They Affect Takeover Performance

Vehicle automation is becoming more advanced in helping drivers understand and react to the environment around them. Many newer vehicle models come standard with backup cameras, blind spot detection, and warning signals. It’s important to identify if such features significantly improve driver performance. The current study investigated the relationship between the presentation of a warning indicating a threat and whether or not that signal helped the participants detect the threat. Findings suggested that participants asked to detect when an automated braking system engaged were significantly more accurate at noticing the system engaged than those asked to manually take-over the vehicle when a threat emerged. In both groups, those given visual warnings that a threat was about to occur were faster in taking over the vehicle when needed than those who did not receive a warning. However, accuracy was low across all conditions groups.