Automatic Emergency Braking System Research Articles

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).

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.

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.

Analysis of Automated Emergency Braking System to Investigate Forward Collision Condition Using Scenario-Based Virtual Assessment

In the recent trend of automotive technologies, active safety systems for vehicles have become one of the key elements to reduce road traffic conditions. Automated vehicles are known as one of the active safety systems to minimize road traffic congestion and unwanted road hazardous situations. Generally, automated vehicles are designed using advanced driving assistance system (ADAS) technology to enhance the safety capability of the vehicles. Moreover, automated vehicles are designed to adopt multiple scenarios with different types of traffic situations. Generally, the performance of automated vehicles is evaluated to adapt with various road conditions and different type of traffic conditions, autonomously. Nonetheless, most of the safety testing was conducted in a controlled environment and with less traffic conditions. Moreover, this technology is tested in developed countries and mostly evaluated for highway driving scenarios, with less pedestrians and motorist’s road users. On the other hand, in developing countries such as Malaysia, most of the automotive researchers have initiated research related to automated vehicle based on controlled environment only. One of the primary focuses for the current automotive researchers is to reduce road accidents due to frontal collision. Thus, automated emergency braking systems have been heavily investigated by most developers to minimize road accidents. Most of the researchers analyze the system in terms of theoretical based simulation and tested using actual vehicle for physical testing. However, this type of testing is not sufficient to optimize the performance of automatedemergency braking systems for developing countries. Therefore, this study focuses on scenario-based virtual assessment to evaluate the capability of autonomous vehicles using automated emergency braking system without causing road casualties with the distance range is 4.5m to 0.5m depending on vehicle speed.

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

Driver Injury from Vehicle Side Impacts When Automatic Emergency Braking and Active Seat Belts Are Used.

As an advanced driver assistance system, automatic emergency braking (AEB) can effectively reduce accidents by using high-precision and high-coverage sensors. In particular, it has a significant advantage in reducing front-end collisions and rear-end accidents. Unfortunately, avoiding side collisions is a challenging problem for AEB. To tackle these challenges, we propose active seat belt pretensioning on driver injury in vehicles equipped with AEB in unavoidable side crashes. Firstly, records of impact cases from China's National Automobile Accident In-Depth Investigation System were used to investigate a scenario in which a vehicle is impacted by an oncoming car after the vehicle's AEB system is triggered. The scenario was created using PreScan software. Then, the simulated vehicles in the side impact were devised using a finite element model of the Toyota Yaris and a moving barrier. These were constructed in HyperMesh software along with models of the driver's side seatbelt, side airbag, and side curtain airbag. Moreover, the models were verified, and driver out-of-position instances and injuries were evaluated in simulations with different AEB intensities up to 0.7 g for three typical side impact angles. Last but not least, the optimal combination of seatbelt pretensioning and the timing thereof for minimizing driver injury at each side impact angle was identified using orthogonal tests; immediate (at 0 ms) pretensioning at 80 N was applied. Our experiments show that our active seatbelt with the above parameters reduced the weighted injury criterion by 5.94%, 22.05%, and 20.37% at impact angles of 90°, 105°, and 120°, respectively, compared to that of a conventional seatbelt. The results of the experiment can be used as a reference to appropriately set the collision parameters of active seat belts for vehicles with AEB.

Design and simulation of automotive radar for autonomous vehicles

Modern automobile technology is pushing towards maximizing road safety, connected vehicles, autonomous vehicles, etc. Automotive RADAR is core sensor technology used for ADAS (Advanced Driver Assistance Technology), ACC (Adaptive Cruise Control), AEB (Automatic Emergency Braking System), traffic assistance, parking aid, and obstacle/pedestrian detection. Despite being inexpensive, RADAR technology provides robust results in harsh conditions such as harsh weather, extreme temperature, darkness, etc. However, the performance of these systems depends on the position of the RADAR and its characteristics like frequency, beamwidth, and bandwidths. Moreover, the characterization of varied materials like layers of paint, polish, primer, or layer of rainwater needs to be analyzed. This performance can be predicted through real-time simulation using advanced FEM software like Altair FEKO&WinProp. These simulations can provide valuable insight into the performance of the system, allowing engineers to optimize the system for specific use cases. For example, simulation can be used to determine the optimal parameters of the RADAR system for a given application. This information can then be used to design and build a physical model or prototype that is optimized for the desired performance. These simulations play a prominent role in determining appropriate data collection and sensor fusion, which reduces the cost and time required for the development of a physical model or prototype. The continued growth and demand for advanced safety features in vehicles further highlight the importance of RADAR technology in modern automobile technology. By accurately characterizing the environment and simulating the system's behavior in real time, engineers can optimize RADAR systems for specific use cases, contributing to safer and more efficient driving experiences

Automatic Emergency Braking System for Trucks for Two-Wheeler Longitudinal Collision Avoidance

Among traffic crashes, truck crashes are usually more severe and cause greater harm to other involved traffic participants, particularly vulnerable road users such as the drivers of two-wheelers. Automatic emergency braking (AEB), also known as autonomous emergency braking, has been demonstrated to be a safe, efficient, and high benefit–cost ratio advanced driving assistance system that can reduce driver errors and collisions, but at present, AEB research is mainly carried out for passenger cars with little specific attention to trucks. In this study, a three-stage AEB algorithm was designed to control longitudinal collision scenarios between trucks and two-wheelers. A total of 121 crashes were collected from the China In-Depth Accident Study Database (CIDAS). The crashes were reconstructed and verified in the MATLAB/Simulink simulation platform. The experimental results show that our proposed method avoided 35% of the collisions. Analysis of the simulation results showed that the scenarios in which the proposed system failed to avoid collisions mainly had the characteristics of rapid increase in the urgency of the conflict, insufficient truck deceleration time, and insufficient braking distance. Random forest was used to analyze the factors affecting the AEB’s collision avoidance, and it was found that collision angle, reflecting the urgency of two-wheeler lane changing, was the most significant factor. The results have at least two practical implications. It is of significance to (1) educate two-wheeler drivers to carefully observe the road conditions before making decisions and (2) develop multi-sensor AEB systems for trucks.

Effects on crash risk of automatic emergency braking systems for pedestrians and bicyclists

Objective The first automatic emergency braking (AEB) system was presented in 2003 and aimed to mitigate or reduce rear-end crashes. Since then, several AEB systems aimed to reduce other collision types have been introduced and studies have shown that they reduce crash risks. The aim with this study was to evaluate crash reductions of cars fitted with AEB systems with pedestrian detection and those with bicyclist detection. Methods The study is based on the Swedish Traffic Accident Data Acquisition that includes road traffic accidents reported by the police and by emergency hospitals. Crashes occurring between 2015 and 2020 and with cars from model years 2015 to 2020 were included. The statistical analysis used odds ratio calculations with an induced exposure approach where the outcomes of sensitive and nonsensitive crashes were studied. The sensitive crashes were hit pedestrians and bicyclists, respectively. The nonsensitive crash type in both comparisons was struck vehicles in rear-end crashes. Evaluations were also made for different light and weather conditions and for high and low speed roads. Results Seven hundred and twelve hit pedestrians and 1,105 hit bicyclists were included, and the nonsensitive crashes consisted of 1,978 vehicles. The overall reduction on crash risk for AEB with pedestrian detection was 8% (±15%; ns) and for AEB with bicyclist detection it was 21% (±17%). When separating for light conditions, no reduction in crash risk for AEB with pedestrian detection nor for AEB with bicyclist detection could be seen in darkness. However, in daylight and twilight conditions, AEB with pedestrian detection reduced pedestrian crash risk by 18% (±19%; ns) and AEB with bicyclist detection reduced bicyclist crash risk by 23% (±19%). No significant reductions could be seen when separating for weather conditions except for a 53% (±31%) reduction for bicyclists in rain, fog, and snowfall. A larger reduction on high-speed roads (50–120 km/h) compared with low-speed roads (10–40 km/h) was also found. Conclusions AEB systems with bicyclist detection were found to reduce the numbers of hit bicyclists, especially in daylight and twilight conditions. In darkness, no reduction for hit pedestrians or bicyclists was found.

Research on Hierarchical Control Strategy of Automatic Emergency Braking System

In order to improve the active safety of vehicles, the control strategy of the vehicle automatic emergency braking system is studied. The hierarchical control idea is used to model the control strategy. The upper controller is a collision time model for the decision-making of vehicle braking deceleration, and the collision time threshold is determined under the condition of considering comfort. According to the braking deceleration output by the upper controller, the lower controller controls the vehicle by adjusting the throttle opening and braking pipeline pressure through PID control. Based on the typical test conditions of C-NCAP, a joint simulation test of CarSim and Matlab/Simulink for hierarchical control strategy is carried out. In order to achieve further verification, several groups of test conditions are conducted, and finally its effectiveness is verified, which can ensure the safety of drivers.

Evaluation of Automatic Emergency Braking Systems in Two-Wheeler Crash Scenarios

The two-wheeler (TW) is a popular means of transportation in China, but TWs often suffer from serious traffic crashes because of their highly flexible trajectory and low detectability. Therefore, they present a challenge for the sensing and decision-making systems in autonomous vehicles (AVs). Collision avoidance systems, such as automatic emergency braking (AEB), have provided an effective way for AVs to avoid collisions with different objects, including TWs. The effectiveness of the AEB system is highly dependent on its parameter configurations, however, which vary among TW crash scenarios. This study, therefore, evaluates the AEB parameters, including time to collision (TTC), deceleration, and detection area (including detection range, field of vision, and trigger width) in two AEB systems: one-stage AEB and three-stage AEB. A total of 243 crashes extracted from the China In-Depth Accident Study database were simulated in Matlab’s Simulink. Results show: (i) one-stage AEB crash avoidance rates range from 15.2% to 81.5%, while three-stage AEB has crash avoidance rates as high as 87.2%; (ii) deceleration, TTC, and detection area all have significant main effects on crash rate, but detection area has less influence in longitudinal than in crossing scenarios; (iii) higher crash avoidance rates resulted in lower traffic efficiency for both AEB systems, but resulted in greater speed reduction only for one-stage AEB; and (iv) collisions are less likely to be avoided in scenarios with high initial speed of TW and AV. This study demonstrates the performance of AEB algorithms in multiple actual crash scenarios and provides a reliable basis for the development of AEB systems.

Estimation of Road Crash Reduction by Installing Automatic Emergency Braking Systems for Elderly Drivers

Estimation of Road Crash Reduction by Installing Automatic Emergency Braking Systems for Elderly Drivers

Design and Comparative Analysis of Several Model Predictive Control Strategies for Autonomous Vehicle Approaching a Traffic Light Crossing

Recent advancements in automated driving technology and vehicle connectivity are associated with the development of advanced predictive control systems for improved performance, energy efficiency, safety, and comfort. This paper designs and compares different linear and nonlinear model predictive control strategies for a typical scenario of urban driving, in which the vehicle is approaching a traffic light crossing. In the linear model predictive control (MPC) case, the vehicle acceleration is optimized at every time instant on a prediction horizon to minimize the root-mean-square error of velocity tracking and RMS acceleration as a comfort metric, thus resulting in a quadratic program (QP). To tackle the vehicle-distance-related traffic light constraint, a linear time-varying MPC approach is used. The nonlinear MPC formulation is based on the first-order lag description of the vehicle velocity profile on the prediction horizon, where only two parameters are optimized: the time constant and the target velocity. To improve the computational efficiency of the nonlinear MPC formulation, multiple linear MPCs, i.e., a parallel MPC, are designed for different fixed-lag time constants, which can efficiently be solved by fast QP solvers. The performance of the three MPC approaches is compared in terms of vehicle velocity tracking error, root-mean-square acceleration, traveled distance, and computational time. The proposed control systems can readily be implemented in future automated driving systems, as well as within advanced driver assist systems such as adaptive cruise control or automated emergency braking systems.

Investigation on AEB Key Parameters for Improving Car to Two-Wheeler Collision Safety Using In-Depth Traffic Accident Data

Automatic emergency braking (AEB) system play an essential role in avoiding traffic collisions and minimizing impact strengths, which contributes to improving road traffic safety. However, the present research of the AEB system is mainly concentrated on avoiding the car to car and car to pedestrian collisions. China has typical mixed traffic characteristics, especially the frequent car to two-wheeler collision accidents. Research on the AEB system to prevent car to two-wheeler collisions in complex multi-traffic scenarios is of great significance to further improve the comprehensive performance of the AEB system. In this article, based on the cases of the car to two-wheeler collisions from China In-Depth Accident Study (CIDAS) database, the effects of three key parameters of the AEB system, braking deceleration, braking advance time, and detection range, on the accident rate and severity are studied by accident reconstruction and virtual experiment. Results show that the theoretical optimal detection range of the AEB system is 180°–35 m for the car to two-wheeler collision prevention. Moreover, the current feasible optimal detection range is 120°–35 m due to the limitation of environmental awareness technology. When the braking advance time is 1 s, the detection angle is 120°, and the detection distance is 35 m, the installation of the AEB system can avoid 22.3% of the car to two-wheeler collision accidents. Meanwhile, in the inevitable accidents, the average collision speed of vehicles decreases from 33.93 to 21.34 km/h, which effectively reduces the collision strengths. Findings of this study could help car manufacturers and makers of AEB hardware select the appropriate detection radar. They can also provide ideas and data support for formulating relevant laws and regulations of the AEB system, future development of AEB system, or other driver assistance systems.

A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car

The automatic emergency braking (AEB) system of the passenger car is responsible for auxiliary braking judgment and decision-making in an emergency. Due to the inevitable pressure response delay of passenger car pneumatic braking systems, a large number of verification tests should be carried out to propose appropriate strategies and algorithms. To realize the rapid verification of the AEB control algorithm, a verification system integrating software-in-the-loop (SIL) and hardware-in-the-loop (HIL) was proposed for a two-axle passenger car. It can verify the logic feasibility of the control algorithm through SIL testing, and can verify the implementation effect of the control algorithm through HIL testing. The verification system is composed of IPG, dSPACE, and a pneumatic braking bench. Considering the influence of pneumatic braking delay, it is well-matched with the actual vehicle AEB system. The AEB hierarchical control algorithm was verified under three typical test conditions. The results show that the SIL testing results of speed and relative distance are in good agreement with the HIL testing results, and the average relative deviation of relative distance is only 1.7 m. The single test time of the SIL testing is about 228 s, which can meet the requirements of rapid verification of the AEB control algorithm of the passenger car.

IS THE MALAYSIAN DRIVER READY TO USE AN AUTOMATIC EMERGENCY BRAKING (ABS) SYSTEM?

Autonomous Emergency Brake (AEB) is the safety system technology for the vehicle that functions to prevent a crash or reduce the high impact of injury or accident. AEB systems are designed to assist the driver only in emergencies situation and to ensure that the driver is always in control of the vehicle. This study aims to identify influencing factors of drivers that continuance intention to use an Automatic Emergency Braking (AEB) system in Malaysia. This study is supported by Technology Acceptance Model (TAM). This study shows that 3 factors have significantly related to continuance intention; perceived ease of use, attitude, and perceived usefulness. In contrast, the safety of drivers was found to be insignificantly related to the continuance intention to use automatic emergency braking among drivers in Malaysia. The AEB system is a sophisticated system that helps alert the driver to a lack of human response. Therefore, the successful use of this system needs the full support of the government and industries.