Automatic Emergency Braking Research Articles

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.

Understanding the new trends in pedestrian injury distribution and mechanism through data linkage and modeling

The objectives of this study were to 1) collect and analyze recent pedestrian crash cases for better understanding of the pedestrian injury distribution and mechanism, 2) use computational simulations to reconstruct pedestrian cases and estimate potential benefit of pedestrian automatic emergency braking (PedAEB) in reducing pedestrian injury risks, and 3) estimate how future pedestrian crash distribution might influence priorities for pedestrian protection.Analyses of national crash-injury dataset showed that the overall number of pedestrians in crashes as well as the serious and fatal pedestrian injuries in the U.S. have been increasing in recent years. Striking vehicle type has changed (i.e., decreased proportion of passenger cars and increase of SUVs and pickup trucks) from 20 years ago mirroring changes in the fleet distribution of vehicle sales. A total of 432 pedestrian injury cases were generated by linking the Michigan trauma data and police-reported crash data from 2013 to 2018. Among the linked cases, pickup trucks and SUVs were involved in crashes with more injuries across body regions. Notably, AIS 3+ chest injuries occur at almost the same rate as lower extremity injuries. A method, combining MADYMO simulations (n = 3,500), response surface model, and data mining, was developed to reconstruct 25 linked pedestrian crash cases to estimate the effectiveness of PedAEB. Based on national field data and MADYMO simulations, PedAEB was estimated to be effective in reducing the risk of head and lower extremity injuries but is relatively less effective in reducing the risk of chest injuries.The increased proportions of SUVs and pickup trucks in the vehicle fleet and the higher penetration of PedAEB may highlight the importance of future research into chest injury risk for pedestrian protection.

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.

Synthesizing fatal crashes involving partially automated vehicles and comparing with fatal crashes involving non-automated vehicles

Automated vehicles (AVs) are expected to improve safety by gradually reducing human decisions while driving. However, there are still questions on their effectiveness as we transition from almost 0% AVs to 100% AVs with different levels of vehicle autonomy. The focus of this study, therefore, is on synthesizing and identifying risk factors influencing fatal crashes involving partially automated vehicles (PAVs) i.e., level 1 AVs and level 2 AVs, in the United States. Fatal crashes involving non-AVs (level 0 vehicles) within their vicinity were used for comparison, to minimize unobserved heterogeneity and randomness associated with the influencing risk factors. The fatal crash data for the years 2016 to 2019 is used for the analysis. A partial proportional odds model is developed using crash, road, and vehicle characteristics as independent variables while the fatal crash involving a vehicle with a specific level of automation (0, 1, or 2) is used as the dependent variable. The level of automation was captured by developing tools and identifying advanced features in each vehicle based on the vehicle identification number (VIN). The odds ratios varied for PAVs compared to non-AVs. PAVs are safer but are more likely to be involved in a fatal crash with non-motorists. Pedestrian automatic emergency braking (PAEB) and lane-keeping assistance (LKA) were observed to improve safety by reducing possible collision with a pedestrian and roadside departure, respectively. Contrarily, vehicles with other smart features are still highly likely to be involved in fatal crashes, demanding further research and attention.

The Study of Utilization of Machine Learning in the Automobile Sector

Numerous businesses and institutions are involved in the study, design, manufacture, and sale of automobiles; they are all part of the Automotive Sector. In terms of market capitalization, the automobile industry is among the top five most profitable in the world. Businesses in the car business and those closely related to it are the ones who really get to put wheels on the road. Most of the parts are included in this, including the engine and body, but not the wheels, batteries, or gas. The automotive industry has worldwide influence and significance notwithstanding the problem of excess capacity and poor profitability. The automotive industry is vital to global trade and production. More than half of the world's oil consumption is attributable to the production of about 60 million automobiles and light trucks annually. Self-driving capabilities, in-car Wi-Fi and navigation systems, parking assistance, programmable computers, energy- saving features, night-vision aid, all-around cameras, automatic emergency braking, sensors to detect pedestrians, and driver concentration aids, etc. are just a few of the technologies we use often in our cars. Several features in cars that we use often include autonomous driving capabilities, in-car internet, parking assistance, onboard computers, and parking sensors. Our ride is safe and comfortable because of these features. Automobiles rely on the driver's active participation to function properly. Although, "how can we achieve this, and what is the logic behind the automobile industry?" is the fundamental issue that has to be posed. Computer jargon or programming language may be a good way to describe the answer to this issue. Incorporating new languages into vehicles is now possible because of the abundance of available programming languages. The Automotive Group relies heavily on machine learning and AI to accomplish its goals. Keywords— Artificial Intelligence, Automotive Industry, Deep Learning, Convolutional Neural Network, Technology, Machine Learning

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.

Effects of an integrated safety system for swivel seat arrangements in frontal crash.

Objective: Autonomous vehicles (Avs) have paved the way for the arrangement of swivel seats in vehicles, which could pose a challenge to traditional safety systems. The integration of automated emergency braking (AEB) and pre-pretension (PPT) seatbelts improves protection for a vehicle's occupant. The objective of this study is to explore the control strategies of an integrated safety system for swiveled seating orientations. Methods: Occupant restraints were examined in various seating configurations using a single-seat model with a seat-mounted seatbelt. Seat orientation was set at different angles, from -45° to 45° with 15° increments. A pretension was used on the shoulder belt to represent an active belt force cooperating with AEB. A generic full frontal vehicle pulse of 20mph was applied to the sled. The occupant's kinematics response under various integrated safety system control strategies was analyzed by extracting a head pre-crash kinematics envelope. The injury values were calculated for various seating directions with or without an integrated safety system at the collision speed of 20mph. Results: In a lateral movement, the excursions of the dummy head were 100mm and 70mm in the global coordinate system for negative and positive seat orientations, respectively. In the axial movement, the head traveled 150mm and 180mm in the global coordinate system for positive and negative seating directions, respectively. The 3-point seatbelt did not restrain the occupant symmetrically. The occupant experienced greater y-axis excursion and smaller x-axis excursion in the negative seat position. Various integrated safety system control strategies led to significant differences in head movement in the y direction. The integrated safety system reduced the occupant's potential injury risks in different seating positions. When the AEB and PPT were activated, the absolute HIC15, brain injury criteria (BrIC), neck injury (Nij), and chest deflection were reduced in most seating directions. However, the pre-crash increased the injury risks at some seating positions. Conclusion: The pre-pretension seatbelt could reduce the occupant's forward movement in the rotating seat positions in a pre-crash period. The occupant's pre-crash motion envelope was generated, which could be beneficial to future restraint systems and vehicle interior design. The integrated safety system could reduce injuries in different seating orientations.

Development and Fabrication of Modified Eye Blinking Vehicle Accident Avoiding System

Abstract: The Eye Blinking Car Accident Avoiding System (EBCAAS) is a novel approach to enhance driving safety by detecting and preventing accidents caused by driver drowsiness or distraction. The system uses a camera mounted on the dashboard of the vehicle to continuously monitor the driver's eyes and detect any abnormal blinking patterns that may indicate fatigue or distraction. Upon detection of such patterns, the system sends an alert to the driver in the form of visual or audio cues, reminding them to stay alert and focused on the road. Additionally, the system is equipped with an automatic emergency braking mechanism that can be activated in the event of a potential collision. The EBCAAS has the potential to significantly reduce the incidence of accidents caused by driver fatigue or distraction and improve road safety for all users. Accidents due to drowsiness can be controlled and prevented with the help of eye blink sensor using IR rays. It consists of IR transmitter and an IR receiver. The transmitter transmits IR rays into the eye

Evaluation of macroscopic fundamental diagram characteristics for a quantified penetration rate of autonomous vehicles

BackgroundThe availability of private vehicles with autonomous features is widespread nowadays. Various car manufacturers are providing attributes like collision warning, city automatic emergency braking, adaptive cruise control, pedestrian detection, lane-keeping assistance and lane departure warning, rear cross-traffic and blind-spot warning in their high-end models.PurposeSuch features can automatically manage the macroscopic fundamental traffic parameters such as speed, headway, etc adaptively. Consequently leading to a heterogeneous traffic stream with diverse car-following behaviour comprising completely manual/traditional (TVs) and autonomous vehicles (AVs). This questions the applicability of classic traffic flow theory relationships on such heterogeneous traffic streams.MethodologyThis paper focuses on developing the macroscopic fundamental diagram for such heterogeneous traffic streams based on the quantified penetration rate (QPR) for autonomous vehicles. The penetration rate is devised by taking into account user demographics, land usage and road network properties. QPR is used as an input for heterogeneous urban traffic stream scenarios to calculate the aggregated urban traffic network dynamics of flow and density for the same network. Travel time versus flow characteristics is evaluated based on calibrated hyperbolic urban link travel time function for both interrupted and uninterrupted flows following the aggregated speed and density output from MFDs for heterogeneous traffic streams. Also, two scenarios are generated for comparison to explain the improvement in the network characteristics together with a sensitivity analysis.ResultsCompared to the base scenario there could be 25-35% of AVs on the road networks based on the analysis in coming fifteen years. This increment in usage impacts the capacity of road networks positively by increasing it up to 59%.ConclusionsResults obtained after the application of the suggested model approach to the real network can be used to define a realistic method for multi-vehicle equilibrium assignment models for heterogeneous traffic streams including autonomous vehicles instead of approximating the penetration rates.

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.

Ergo, SMIRK is safe: a safety case for a machine learning component in a pedestrian automatic emergency brake system

Integration of machine learning (ML) components in critical applications introduces novel challenges for software certification and verification. New safety standards and technical guidelines are under development to support the safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and the Assurance of Machine Learning for use in Autonomous Systems (AMLAS) framework. SOTIF and AMLAS provide high-level guidance but the details must be chiseled out for each specific case. We initiated a research project with the goal to demonstrate a complete safety case for an ML component in an open automotive system. This paper reports results from an industry-academia collaboration on safety assurance of SMIRK, an ML-based pedestrian automatic emergency braking demonstrator running in an industry-grade simulator. We demonstrate an application of AMLAS on SMIRK for a minimalistic operational design domain, i.e., we share a complete safety case for its integrated ML-based component. Finally, we report lessons learned and provide both SMIRK and the safety case under an open-source license for the research community to reuse.

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.

Effects of forward collision warning and automatic emergency braking on rear-end crashes involving pickup trucks

Objective Forward collision warning (FCW) and automatic emergency braking (AEB) have found to be effective on cars, SUVs, and large trucks. The objective of this study was to extend prior work and estimate the effects of FCW and AEB on pickups. Methods Rear-end crashes where a pickup with optional FCW or AEB was the striking vehicle were identified in 25 U.S. states during 2017–2020. Quasi-induced exposure was performed with logistic regression to compare rear-end-striking crashes between vehicles with and without the systems relative to being struck in the rear. Rear-end-striking crash rates per registered vehicle year were also compared between equipped and non-equipped pickups with Poisson regression. The association of the systems with severity in the rear-end-striking crashes that occurred was examined with logistic regression. Results In the quasi-induced exposure analysis, AEB was associated with statistically significant 34% reductions in the risk of a rear-end-striking crash of any severity and with any injuries, and a 76% reduction in the risk of a rear-end-striking crash with serious or fatal injuries (p = 0.09). FCW was associated with statistically significant declines of 22% in the risk of a rear-end-striking crash with any injuries and 71% in the risk of a rear-end-striking crash with serious or fatal injuries, but FCW was not associated with a change in all rear-end-striking crashes. Results were similar in Poisson regression models for all but FCW’s effect on all rear-end-striking crashes. The odds that rear-end-striking crashes involving pickups with FCW resulted in any injuries were 26% lower, and serious/fatal injuries were 75% lower, than those involving pickups without the system. AEB was associated with a nonsignificant reduction in the odds of serious/fatal injuries when a rear-end strike occurred and no change in the odds of any injuries. Discussion Consistent benefits for front crash prevention systems have been demonstrated for a wide range of vehicle types, now including a sample of pickups. Gaps in proposed U.S. regulations should be filled so that AEB is required equipment on all new vehicles.

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.

Influence of Speed and Other External Factors on the Functioning of Automatic Emergency Braking During Unexpected Interactions With Pedestrians

Today’s conventionally manufactured vehicles have a range of driving aids that help keep the vehicle safe. The operation of these features depends primarily on the proper functioning of sensors, which continuously monitor surroundings to detect obstacles. However, these sensors are not always reliable. The aim of this article is to investigate the actual limits of automatic emergency braking (AEB) when a pedestrian enters the roadway, which in many cases is unpredictable, sudden, and not automatically controlled. The expected high efficiency of AEB is significantly affected by both the speed of the vehicle and weather conditions. The conclusion of this study, therefore, is used to develop a discussion on urban speed limits and their impacts on vulnerable road users.

Residual risk in Automatic Train Protection systems: evaluation and management

The paper on typical rail operation hazards, such as Signal Passed at Danger (SPAD) and Failed Braking Application by Driver (FBAD). The protection against these hazards is normally by Automatic Train Protection (ATP) systems and emergency braking activation, operated by the onboard systems over a certain Minimum Release Speed (MRS). The determination of such speed is a key design parameter related with the achievable performances in terms of safety and capacity. The present research deal with a systematic analysis of the operational conditions potentially affecting the determination of such speed values by modelling the dynamics of the problem taking into account the features of the infrastructures (tracks layout, gradients, etc.), the vehicles (mass, braking performances, etc.) and the geography of signalling and ATP systems themselves. In fact, the position of main signals protecting the dangerous points (switches, level crossings, etc.) emerges as key player, affecting both the design and the operation of the 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.

Predicting the impact on road safety of an intersection AEB at urban intersections. Using a novel virtual test field for the assessment of conflict prevention between cyclists/pedelecs and cars

With the rise of Advanced Driver Assistant Systems (ADAS) and the introduction of Highly Automated Driving (HAD), understanding and predicting road traffic accidents becomes increasingly important. Especially for the assessment of HAD/ADAS systems and of road safety, the precise prediction of the system’s impact on the occurrence of road traffic accidents is essential.Traffic simulations, as one option of virtual assessment, enable the assessment of safety systems in virtual test fields. By modelling the human driver, it is possible to simulate and predict future accident constellations and accident severities. In addition, the influence of vehicle factors, such as the presence of safety systems or their characteristics, on the occurrence of accidents can be investigated.This article elucidate virtual assessment by executing the whole procedure for an automatic emergency braking (AEB) system capable to detect cyclists at urban intersections. The authors implement a new driver behaviour model (DReaM) to predict road traffic accidents at urban intersections. Using the AEB as an example, the influence of different sensor opening angles on crash frequency is investigated by varying the sensors in three steps: 100°, 180°, and 210°. A total of 240,000 situations are simulated of which 6,674 are crashes. Within the two investigation scenarios, the crash frequency is reduced by up to 88.43%, or 93.92% by introducing the AEB system.The article shows that virtual assessment enables the prediction of new safety systems regarding road safety at early design stages. In addition, different system characteristics can be compared efficiently, e.g., the sensor opening angle.