Automatic Emergency Braking System Research Articles

Road target recognition based on radar range-Doppler spectrum with GS – ResNet

ABSTRACT Road target recognition ability of automotive millimetre-wave (mmWave) radar is crucial in areas such as autonomous driving, advanced driver assistance systems (ADAS), and automated emergency braking systems (AEBS). Current mmWave radar systems are primarily utilized for speed, distance, and angle measurements, with limited capability or unsatisfactory performance in target classification. In response to this limitation, this paper proposes a novel method for road target recognition, using radar range-Doppler spectrum as input data and introducing a global attention mechanism and SCConv module. First, the range-Doppler spectrum is processed by the clutter removal algorithm block to remove background clutter and vibration interference. Second, considering the small features and susceptibility to noise interference in the range-Doppler spectrum, we have devised a deep residual network based on a global attention mechanism, significantly enhancing the model’s accuracy in range-Doppler spectrum classification. Finally, we introduce the SCConv module and improve the downsampling module in ResNet to enhance the model’s classification accuracy. Experimental results demonstrate that the model achieves an average accuracy of 99.79% in classifying six types of road targets, significantly outperforming other methods. This research is of significant importance in advancing the understanding of road traffic conditions by autonomous driving systems and enhancing system safety.

Assessing advanced driver assistance systems in police vehicles under demanding conditions

Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers’ safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers’ driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers’ driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.

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.

Pedestrian automatic emergency braking responses to a stationary or crossing adult mannequin during the day and night

Objective Automatic emergency braking systems with pedestrian detection (PAEB) are effective at preventing pedestrian crashes, but the safety benefits are not observed at night. This study used the Insurance Institute for Highway Safety (IIHS) PAEB test data to characterize PAEB responses in different lighting conditions and for different rated systems. Methods Data from 6,919 IIHS PAEB tests were retrieved from IIHS databases. Invalid trials, trials without AEB, and trials with outliers were removed leaving 5,894 trials from 212 model year 2018 to 2023 vehicles for analysis. PAEB responses were characterized by computing the time-to-collision (TTC) of forward collision warning (FCW) and AEB; brake threat number (BTN); mean deceleration; maximum deceleration; and maximum jerk. A linear mixed-effects model was used to predict each dependent measure with scenario (crossing adult, stationary adult), speed, rating (superior, basic/advanced), lighting (day, night with high beams, night with low beams), and their interactions. Vehicle was included as a random effect. A Bonferroni correction was applied to maintain a family-wise type-1 error rate of 0.05 across 138 total hypothesis tests. Results PAEB system warnings were later and automatic braking occurred later as speed increased at night with low beams but changed little at night with high beams and during the day (p < 0.0004). BTN increased more rapidly as speed increased at night with low beams compared with high beams and during the day (p < 0.0004). Based on the BTN model, on average, PAEB systems can brake to avoid the adult mannequin (BTN < 1) when closing speed is less than 67 km/h during the day and at night with high beams but only when closing speed is less than 49 km/h at night with low beams. Superior-rated PAEB systems warned and braked earlier compared with basic/advanced-rated systems (p < 0.0004). Conclusions Increased lighting from high beams made nighttime performance resemble daytime performance in controlled testing. Increasing output from vehicle low beams, increasing the use of high beams, and enhancing overhead lighting around crosswalks and pedestrian areas are all methods for increasing lighting, improving pedestrian conspicuity, and enhancing PAEB performance to prevent pedestrian crashes.

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.&#x0D; 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&amp;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