Abstract
In this study, we selected four real-world rear-end crash scenarios with different crash characteristics. The vehicles involved in those crashes were not equipped with any crash avoidance systems. We then used the accident reconstruction method to build those crash scenarios in PC-Crash software. Then, different FCW/AEB safety algorithms have been defined for a subject vehicle model in each crash scenario and each scenario was simulated for a set of input parameters such as vehicle speed, brake intensity, and driver reaction time. The range and distribution of input parameters were extracted from the related field crash data and available literature. A total number of 16000 simulations have been conducted which produced input-output datasets for further investigations. Finally, the effects of input parameters on simulation outcomes including crash occurrence, AEB activation, injury risk, and vehicle damage have been quantified using the Boruta algorithm. The results indicated that the overall effectiveness of the AEB system was a 57% reduction of rear-end crashes, a 52% reduction of injury severity (striking vehicle’s passengers), and a 47% reduction of damages for striking vehicles. The results also showed that the available AEB algorithms were more effective for the average speed equal to or less than 80 kmph. The speed of the subject vehicle, type of AEB algorithm, sensor detection range, and driver reaction time were the most important parameters on crash outcomes. In addition, the results indicated that the performance of FCW had a direct impact on the effectiveness of the AEB system for the integrated FCW + AEB system.
Highlights
In this study, we selected four real-world rear-end crash scenarios with different crash characteristics. e vehicles involved in those crashes were not equipped with any crash avoidance systems
Studies on forward collision warning (FCW)/autonomous emergency brake (AEB) systems can be classified into the following areas: (i) Algorithm studies (AS): they are called vehicleperformance-based studies that focused on developing the FCW/AEB algorithms and evaluating their performance to control the longitudinal dynamics of the vehicle. e main purpose of AS studies was to gain a better understanding of the driver characteristics, vehicle dynamic factors, detection, perception, and prediction technologies and incorporating those factors into their algorithms to achieve the best performance of the FCW/AEB systems
In the AEB activation model, the subject vehicle (SV) vehicle’s speed had the highest level of importance, whereas in the crash model, the type of FCW/AEB system (ADAS) had the highest importance. e results showed that the injury model mostly depends on the Vsv. is is because the velocity change (∆Vsv) was the metric to calculate the injury severity which its value depends on impact characteristics. e following paragraphs will explain the effect of these important parameters on the statistical models
Summary
We selected four real-world rear-end crash scenarios with different crash characteristics. e vehicles involved in those crashes were not equipped with any crash avoidance systems. En, different FCW/AEB safety algorithms have been defined for a subject vehicle model in each crash scenario and each scenario was simulated for a set of input parameters such as vehicle speed, brake intensity, and driver reaction time. E speed of the subject vehicle, type of AEB algorithm, sensor detection range, and driver reaction time were the most important parameters on crash outcomes. The kinematic-based models use deceleration rate, driver reaction time, sensor’s delay, and speed data to determine the DTC or TTC for the FCW/AEB activation. E main purpose of AS studies was to gain a better understanding of the driver characteristics (e.g., reaction time and braking intensity), vehicle dynamic factors (e.g., tire-road interaction), detection, perception, and prediction technologies and incorporating those factors into their algorithms to achieve the best performance of the FCW/AEB systems (i) Algorithm studies (AS): they are called vehicleperformance-based studies that focused on developing the FCW/AEB algorithms and evaluating their performance to control the longitudinal dynamics of the vehicle. e main purpose of AS studies was to gain a better understanding of the driver characteristics (e.g., reaction time and braking intensity), vehicle dynamic factors (e.g., tire-road interaction), detection, perception, and prediction technologies and incorporating those factors into their algorithms to achieve the best performance of the FCW/AEB systems
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