Autonomous Emergency Braking System Research Articles

Vehicle-to-Cyclist Collision Prediction Models by Applying Machine Learning Techniques to Virtual Reality Bicycle Simulator Data

The study of vulnerable road users (VRUs) behavior is key to designing and optimizing driving assistance systems, such as the autonomous emergency braking (AEB) system. These kinds of devices could help lower the VRU accident rate, which is of particular interest to cyclists, who are the subject of this research. To better understand cyclists’ reaction patterns in frequently occurring collision scenarios in urban environments, this paper focuses on developing a virtual reality (VR) simulator for cyclists (VRBikeSim) that incorporates eye-tracking functionality. The braking and steering systems were calibrated by means of on-track tests with a sensorized bicycle in order to improve the accuracy of the bicycle virtual model. From the data obtained in the virtual tests, a battery of predictive models was built using supervised machine learning classifiers. All of them exhibited an accuracy higher than 85%, especially the K-Nearest Neighbors model. This model allowed us to obtain the best balance between the prediction of avoidance and collision cases, as well as enabling computationally lower times to be incorporated into the decision-making algorithm of an AEB system.

Co-simulation of giant magnetostrictive autonomous emergen-cy braking system for intelligent vehicles based on Prescan/Simulink /CarSim

The combination of giant magnetostrictive actuator (GMA) and autonomous Emergency brak-ing system (AEB) can achieve the lightweight body and fast braking of intelligent vehicle. In this essay, simulation scenarios are established in PreScan, AEB algorithm and GMA actuator are combined in simulink, vehicle dynamics model is provided by CarSim, and co-simulation analysis of CCRm, CCRs and CCRb working conditions is carried out. When AEB adopts TTC algorithm only, in the stationary vehicle scenario (CCRs) where the vehicle approaches the front at a certain speed, the relative distance between the two vehicles is between 1.86 and 2.85m until stopping. When the front vehicle is driving slowly and the ego vehicle is faster than the front (CCRm), the relative distance between the two vehicles until stopping is between 2.28 and 2.83m; When the ego vehicle is driving at the same speed as the front, the front vehicle suddenly braking (CCRb), and the relative distance between the two vehicles until stopping is 1.98~2.54m; The above scenarios can achieve effective collision avoidance, and the safety distance is within an effective and reasonable range. In the scenario of two vehicles following at the same speed and close distance, TTC algorithm fails. AEB adopts TTC algorithm and Seungwukmoonalgo-rithm for collaborative control to achieve effective collision avoidance. The co-simulation re-sults show that the AEB system can intervene in time and avoid collisions effectively under the above three working conditions, and the relative distance between the two vehicles until stop-ping is between 1.86 and 2.85m, which verifies the timeness and effectiveness of the braking of the giant magnetostrictive actuator when AEB algorithm intervenes, and significantly improves the active safety performance of the vehicle.

Proactive Braking Control System for Collision Avoidance during Right Turns with Occluded Vision at an Intersection

This paper describes the development of an Advanced Driver Assistance System (ADAS) which will allow drivers to avoid collisions with an oncoming vehicle from an occluded area when turning right at intersections in left-hand traffic. Connected vehicles, in coordination with infrastructure, represent one of the commercialized ADAS technologies for collision avoidance. However, the coverage of the ADAS will be limited to designated intersections only, as communication equipment needs to be installed in both the vehicle and infrastructure to enable the assistance. This paper proposes an ADAS using on-board sensors, independent of infrastructure facilities, to control the vehicle velocity to avoid collisions. Most current intersection assistance, by using an Autonomous Emergency Braking System (AEBS), allows the driver to avoid a collision with oncoming vehicles when there is clear vision without occlusion. However, many accidents occur when the vehicle detects the oncoming vehicle too late because of occlusion in the intersection, such as a vehicle in the opposite lane. This system calculates the hazardous speed criteria of the ego vehicle, which might result in a high risk of collision when darting out occurs, and provides speed control assistance to allow the driver to escape from the hazardous speed region. The simulation results reveal that the proposed system effectively reduces the possibility of collisions compared to conventional AEBS.

Complying with ISO 26262 and ISO/SAE 21434: A Safety and Security Co-Analysis Method for Intelligent Connected Vehicle.

A cyber-physical system (CPS) integrates communication and automation technologies into the operational processes of physical systems. Nowadays, as a complex CPS, an intelligent connected vehicle (ICV) may be exposed to accidental functional failures and malicious attacks. Therefore, ensuring the ICV's safety and security is crucial. Traditional safety/security analysis methods, such as failure mode and effect analysis and attack tree analysis, cannot provide a comprehensive analysis for the interactions between the system components of the ICV. In this work, we merge system-theoretic process analysis (STPA) with the concept phase of ISO 26262 and ISO/SAE 21434. We focus on the interactions between components while analyzing the safety and security of ICVs to reduce redundant efforts and inconsistencies in determining safety and security requirements. To conquer STPA's abstraction in describing causal scenarios, we improved the physical component diagram of STPA-SafeSec by adding interface elements. In addition, we proposed the loss scenario tree to describe specific scenarios that lead to unsafe/unsecure control actions. After hazard/threat analysis, a unified risk assessment process is proposed to ensure consistency in assessment criteria and to streamline the process. A case study is implemented on the autonomous emergency braking system to demonstrate the validation of the proposed method.

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles.

The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.

An Improved YOLOv5 Algorithm for Vulnerable Road User Detection.

The vulnerable road users (VRUs), being small and exhibiting random movements, increase the difficulty of object detection of the autonomous emergency braking system for vulnerable road users AEBS-VRUs, with their behaviors highly random. To overcome existing problems of AEBS-VRU object detection, an enhanced YOLOv5 algorithm is proposed. While the Complete Intersection over Union-Loss (CIoU-Loss) and Distance Intersection over Union-Non-Maximum Suppression (DIoU-NMS) are fused to improve the model's convergent speed, the algorithm also incorporates a minor object detection layer to increase the performance of VRU detection. A dataset for complex AEBS-VRUS scenarios is established based on existing datasets such as Caltech, nuScenes, and Penn-Fudan, and the model is trained using migration learning based on the PyTorch framework. A number of comparative experiments using models such as YOLOv6, YOLOv7, YOLOv8 and YOLOx are carried out. The results of the comparative evaluation show that the proposed improved YOLO5 algorithm has the best overall performance in terms of efficiency, accuracy and timeliness of target detection.

Monocular Distance Estimation-based Approach using Deep Artificial Neural Network

Those in authority are evaluating the test evaluation for threat assessments currently in place. Since people often depend on their feelings and moods, this may create inequality. Therefore, this study suggested applying deep learning for Autonomous Emergency Steering (AES) and Autonomous Emergency Braking (AEB) assessments in the safety rating protocol. The suggested method for the test in situation-based threat assessments is a monocular distance estimation-based approach. The camera's objective is to make it simple to conduct assessments using only an onboard dash camera. This study proposes a method based on a monocular distance estimation-based approach for test methodology in the situational-based threat assessments using deep learning for the AES system to complement the AEB system for active safety features. Then, the accuracy of the distance estimation models has validated with the ground truth distances from the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset. Thus, the output of this study can contribute to the methodological base for further understanding of drivers the following behaviour with a long-term goal of reducing rear-end collisions.

Holistic pedestrian safety assessment for average males and females.

An integrated assessment framework that enables holistic safety evaluations addressing vulnerable road users (VRU) is introduced and applied in the current study. The developed method enables consideration of both active and passive safety measures and distributions of real-world crash scenario parameters. The likelihood of a specific virtual testing scenario occurring in real life has been derived from accident databases scaled to European level. Based on pre-crash simulations, it is determined how likely it is that scenarios could be avoided by a specific Autonomous Emergency Braking (AEB) system. For the unavoidable cases, probabilities for specific collision scenarios are determined, and the injury risk for these is determined, subsequently, from in-crash simulations with the VIVA+ Human Body Models combined with the created metamodel for an average male and female model. The integrated assessment framework was applied for the holistic assessment of car-related pedestrian protection using a generic car model to assess the safety benefits of a generic AEB system combined with current passive safety structures. In total, 61,914 virtual testing scenarios have been derived from the different car-pedestrian cases based on real-world crash scenario parameters. Considering the occurrence probability of the virtual testing scenarios, by implementing an AEB, a total crash risk reduction of 81.70% was achieved based on pre-crash simulations. It was shown that 50 in-crash simulations per load case are sufficient to create a metamodel for injury prediction. For the in-crash simulations with the generic vehicle, it was also shown that the injury risk can be reduced by implementing an AEB, as compared to the baseline scenarios. Moreover, as seen in the unavoidable cases, the injury risk for the average male and female is the same for brain injuries and femoral shaft fractures. The average male has a higher risk of skull fractures and fractures of more than three ribs compared to the average female. The average female has a higher risk of proximal femoral fractures than the average male. A novel methodology was developed which allows for movement away from the exclusive use of standard-load case assessments, thus helping to bridge the gap between active and passive safety evaluations.

Adaptive autonomous emergency braking model based on weather conditions

Objective Vehicle active safety systems can improve vehicle security by avoiding collisions. An autonomous emergency braking (AEB) system’s safety distance calculation is usually based on normal weather conditions. The AEB system’s early warning capabilities decrease during adverse weather conditions. Methods A multilayer perceptron (MLP) model is used to obtain data from accident and weather data sets. The MLP model is trained and the severity of accidents is predicted. The severity is used as a parameter to build an adaptive AEB system algorithm that considers adverse weather conditions. Results The adaptive AEB system algorithm increases safety and reliability under adverse weather conditions. Prescan and a driver-in-the-loop system are used to test the adaptive AEB model. Both tests show that the adaptive AEB model has better performance under adverse weather than the traditional AEB model. Conclusions The experimental results demonstrate that the adaptive AEB system can increase the safety distance in rainy weather and avoid collisions under hazy conditions.

Autonomous Emergency Braking of Electric Vehicles With High Robustness to Cyber-Physical Uncertainties for Enhanced Braking Stability

Modern autonomous emergency braking (AEB) system is a typical safety-critical cyber-physical system (CPS) synthesizing the vehicular communications, control, and proception technologies. However, the control performance of braking can be easily deteriorated by the road adhesion saturation in physical environment and the multi-hop communication network-induced delays in cyber systems. Motivated by this, a new multi-hop loop delay analysis method and its associated upper-bound expression is proposed to scrutinize the system uncertainties, within the scope of CPS. Following this endeavor, a hierarchical cyber-physical control scheme for the AEB system is proposed to mitigate the adverse effects of road adhesion saturation and multi-hop communication network-induced delays. At the upper layer, a μ-adaptive time-to-collision (TTC) planning strategy is adopted to generate the desired acceleration for collision risk avoidance considering the road adhesion saturation. At the lower layer, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -based linear quadratic regulator (LQR) is designed for acceleration tracking with strong robustness to the uncertainties of cyber system. Hardware-In-Loop (HIL) experiments validate that the proposed method is superior in terms of braking accuracy and the robustness to the system uncertainties.

Characteristics of Dangerous Scenarios between Vehicles Turning Right and Pedestrians under Left-Hand Traffic

Pedestrian deaths account for the highest percentage of fatality caused by traffic accidents in Japan. Increasing pedestrian safety is a key objective for reducing such deaths. For pedestrian fatality caused by vehicles at low speed, turning the vehicle toward the right was the most common behavior under left-hand traffic. Autonomous emergency braking (AEB) systems for pedestrian safety have great potential to mitigate pedestrian injuries and fatalities in traffic accidents. However, pedestrian-AEB systems, especially for vehicles turning right, are still under development. This study identified the characteristics of dangerous traffic scenarios between vehicles turning right and pedestrians, focusing on two directions of pedestrian crossing: from the left to the right side (Left-Pedestrian) and from the right to the left side (Right-Pedestrian). The ego vehicle recorded near-miss incidents using a drive recorder. The results revealed that the Left-Pedestrian and Right-Pedestrian scenarios had different features for both the width of roads going to and through the intersection and the average of the travel speeds of the ego vehicles. They had similar characteristics in terms of the presence of other vehicle categories, but differences in the relationship of numbers and/or travel directions of other vehicles. The findings of this study will contribute to the development and evaluation of safety systems for preventing collisions between right-turning vehicles and pedestrians at intersections.

Evaluation of autonomous emergency braking by using a theoretical formula and dual cameras

According to data gathered by the Korean National Police Agency, 1,028,777 vehicle-to-vehicle traffic accidents and 125,597 vehicle-to-pedestrian traffic accidents occurred in 2020. To decrease the occurrence possibility of traffic accidents, advanced driver-assistance system (ADAS), which provides assistance in regions that drivers do not recognize, have attracted increasing interest. Among the representative functions of ADAS, autonomous emergency braking (AEB) is a valuable system for accident prevention and mitigation. In this study, to evaluate AEB by using dual cameras, actual vehicle tests were conducted. In addition, the theoretically calculated values, results obtained using dual cameras, and results obtained using measuring instruments were compared and analyzed. The result values using the proposed theoretical formula and those of the actual vehicle test using the measurement instruments were compared and analyzed. The minimum and maximum error rates were 0.11% and 4.45%, respectively. For results obtained using dual cameras and measurement instruments, the minimum and maximum error rates were 1.80% and 8.89%, respectively. In the development stage of ADAS especially when developing AEB system, the use of the theoretical formula and dual cameras can decrease the cost burden compared to that associated with testing.

Cost-Benefit Analysis to Assess the Effectiveness of an External Airbag and Autonomous Emergency Braking System

One of the recent developments in safety systems is an external airbag installed on the front bumper of a vehicle and autonomous emergency braking system. In this paper, we propose a framework for a cost-benefit analysis of the external airbag and autonomous emergency braking system in order to validate its commercialization. Road traffic crash data obtained from the National Automotive Sampling System/Crashworthiness Data System (NASS/CDS) was used, and three different crash types related to frontal damage in vehicles were extracted to estimate the safety performance of an external airbag. An ordinal logistic regression model was applied to estimate the safety performance in terms of the reduced maximum abbreviated injury scale (MAIS) based on a reduction in the total delta-v following the installation of an external airbag. Given the estimated safety performance of the external airbag, a cost-benefit analysis is conducted. According to the results, the external airbag system saves 46% of occupants with MAIS 3+ injuries and prevents 40% of fatalities. Moreover, the benefit/cost ratios of the external airbag system range from 0.496 to 0.509 depending on the scenario. Lastly, sensitivity analyses were performed with important parameters, including the initial and maximum market penetration ratio and the price of the system.

A Spatiotemporal Deep Learning Architecture for Road Surface Classification Using LiDAR in Autonomous Emergency Braking Systems

A Spatiotemporal Deep Learning Architecture for Road Surface Classification Using LiDAR in Autonomous Emergency Braking Systems

A Review on Test Method Development for Motorcycle Autonomous Emergency Braking Systems

A Review on Test Method Development for Motorcycle Autonomous Emergency Braking Systems

An autonomous emergency braking strategy based on non‐linear model predictive deceleration control

AbstractSurrogate safety measures (SSM) are used to assess the risk for autonomous emergency braking system (AEBS). Developing appropriate SSM and accurately executing the braking request are the key issues. Time‐to‐collision (TTC) is a typical time‐based SSM with limitations. By analyzing the braking process, this paper proposes a new SSM based on deceleration rate to avoid collision (DRAC). As the brake actuator, vehicle electronic stability control (ESC) system has many problems, such as large overshoot and pressure fluctuation. Considering the model of hydraulic control unit (HCU) and vehicle, a deceleration controller based on non‐linear model predictive control (NMPC) is proposed. Based on this, a layered AEBS architecture is proposed. The upper‐layer AEBS controller calculates the expected deceleration based on modified DRAC (MDRAC), and transmits it to the lower‐layer NMPC deceleration controller. Finally, the simulation and experimental tests are carried out. The results show that the system has a fast and stable response. In addition, the performance of the proposed AEBS strategy is tested according to the Euro‐NCAP test protocol. Comparing the results with the TTC method, the proposed method can improve the stability of the distance margin by more than 0.55 m, which ensures the safety and improves the stability of the vehicle.

Application of Machine Learning Techniques for Predicting Potential Vehicle-to-Pedestrian Collisions in Virtual Reality Scenarios

The definition of pedestrian behavior when crossing the street and facing potential collision situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in commercial vehicles. To this end, this article proposes the generation of classification models through the deployment of machine learning techniques that can predict whether there will be a collision depending on the type of reaction, the lane where it occurs, the visual acuity the level of attention, and consider the most relevant factors that determine the cognitive and movement characteristics of pedestrians. Thereby, the inclusion of this type of model in the decision-making algorithm of the AEB system allows for modulating its response. For this purpose, relevant information on pedestrian behavior is obtained through experiments made in an ad-hoc, Virtual Reality (VR) environment, using a portable backpack system in three urban scenarios with different characteristics. Database generation, feature selection, and k-fold cross-validation generate the inputs to the supervised learning models. A subsequent analysis of the accuracy, optimization, error measurement, variable importance, and classification capability is conducted. The tree-based models provide more balanced results for the performance metrics (with higher accuracy for the single decision tree case) and are more easily interpretable and adaptable to the algorithm. From them it is deduced the high importance of the reaction type and the relative position where it occurs, coinciding with the high significance of these factors in the analyzed collisions.

Calibrating and Comparing Autonomous Braking Systems in Motorized-to-Non-Motorized-Vehicle Conflict Scenarios

Assuring the safety of all road users, including non-motorized vehicles, is important in the autonomous driving environment. Autonomous emergency braking (AEB) systems have provided an effective way for automated vehicles to avoid collisions with the less easily detectable non-motorized vehicles. Automatic preventive braking (APB) is a new method proposed by Mobileye that promises to reduce crashes without reducing traffic throughput, but APB’s effectiveness has not yet been evaluated. This study therefore calibrates and compares the performance of APB with that of one-stage and three-stage AEB braking systems in safety-critical events (SCEs) between motorized and non-motorized vehicles, using SCEs extracted from the Shanghai Naturalistic Driving Study and simulated in MATLAB’s Simulink. The evaluation results, which consider both safety and conservativeness, show that 1) one-stage AEB with a deceleration of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.5\,\text {m/s}^{2}$ </tex-math></inline-formula> and a time-to-collision threshold of 1.6 seconds can prevent all SCEs from becoming crashes; 2) APB has the best driving stability but its safety performance is inferior to that of the two AEB systems; 3) APB’s deceleration process is easily affected by its pre-defined parameters and changing kinetic parameters, which may be one cause of its crashes; 4) AEB’s time-triggered braking process is more consistent and reliable than APB’s distance-triggered process.

Collision-free emergency planning and control methods for CAVs considering intentions of surrounding vehicles

Autonomous emergency braking (AEB) systems are able to control vehicles as needed to avoid vehicle rear-end collisions. However, these systems are ineffective in scenarios with laterally cut-in vehicles and rapidly-changing dangerous scenes. This paper proposes a novel collision-free emergency braking system (CFEBS) that can enable intelligent connected vehicles (CAVs) to plan and execute a more conservative safety trajectory for the braking process in dangerous scenes by considering the longitudinal and lateral motion intentions of the surrounding vehicles. An intention identification model for surrounding vehicles is proposed based on long–short term memory (LSTM) networks and conditional random fields (CRFs). By considering the surrounding vehicles as risk sources and quantifying the risk with the speed of the risk flow, a potential risk flow model is built to calculate the potential risk map (PRM) around the ego vehicle. The global safest trajectory is generated via the PRM using the discrete method. The output trajectory profile is regarded as the reference for a model predictive controller (MPC). Simulation results show that the proposed CFEBS can predict vehicle intention with 91.6% accuracy and control the ego vehicle to perform effective collision-free braking operations in emergency traffic environments.

Comparative analysis of AEB effectiveness based on typical and atypical scenarios of electric two-wheeler accidents in China

Electric two-wheeler (ETW) accidents are one of the most important types of traffic accidents in China. Currently, the China New Car Assessment Program (C-NCAP) has proposed test scenarios to evaluate the effectiveness of autonomous emergency braking (AEB) for ETWs, including several common typical crash scenarios, but other atypical scenarios have not been considered. To determine the performance of AEB in real accidents, 16 in-depth accident cases with typical scenarios and 11 cases with atypical scenarios were selected based on a proposed C-NCAP typical scenario set and reconstructed using the virtual simulation tools MADYMO and PC-Crash. The crashes were re-simulated with a car equipped with an AEB system while varying the sensor field of view (FOV), time-to-collision (TTC), sensor delay time (SDT), and lateral trigger width ( W). The results show that for almost all combinations of AEB parameters, the crash avoidance rate was much higher in the typical scenario than that in the atypical scenario. When using an AEB with a FOV of 90° (±45°), all ETW accidents were avoided in typical scenarios, while even with the most efficient AEB system (FOV = ±60°, TTC trigger value = 1.5 s, SDT = 0.1 s), only 82% of crashes were avoided in atypical scenarios. Further considering the effect of lateral width, increasing the width from 2 to 5 m, the maximum avoidance rate of the AEB system increased by 43% in the typical scenarios and 18% in the atypical scenarios. The findings suggest that the typical AEB test scenarios proposed for C-NCAP were useful for other crash scenarios, but that including additional test scenarios may better reflect real world crash scenarios. It is recommended that atypical scenarios should be considered in C-NCAP, particularly perpendicular crash scenarios with the car or ETW turning, to better describe real accidents and improve vehicle safety.