Estimated benefit of automated emergency braking systems for vehicle–pedestrian crashes in the United States

Abstract

Objective: The objective of this research study is to estimate the benefit to pedestrians if all U.S. cars, light trucks, and vans were equipped with an automated braking system that had pedestrian detection capabilities. Methods: A theoretical automatic emergency braking (AEB) model was applied to real-world vehicle-pedestrian collisions from the Pedestrian Crash Data Study (PCDS). A series of potential AEB systems were modeled across the spectrum of expected system designs. Both road surface conditions and pedestrian visibility were accounted for in the model. The impact speeds of a vehicle without AEB were compared to the estimated impact speeds of vehicles with a modeled pedestrian detecting AEB system. These impacts speeds were used in conjunction with an injury and fatality model to determine risk of Maximum Abbreviated Injury Scale of 3 or higher (MAIS 3þ) injury and fatality. Results: AEB systems with pedestrian detection capability, across the spectrum of expected design parameters, reduced fatality risk when compared to human drivers. The most beneficial system (time-to-collision [TTC] ¼ 1.5 s, latency ¼ 0 s) decreased fatality risk in the target population between 84 and 87% and injury risk (MAIS score 3þ) between 83 and 87%. Conclusions: Though not all crashes could be avoided, AEB significantly mitigated risk to pedestrians. The longer the TTC of braking and the shorter the latency value, the higher benefits showed by the AEB system. All AEB models used in this study were estimated to reduce fatalities and injuries and were more effective when combined with driver braking.

Recognizing Hazard Perception in a Visual Blind Area Based on EEG Features

“…To prevent accidents caused by the lack of hazard perception, researchers have developed a variety of driving assistance systems to help drivers in detecting hazards [36]- [39] or rely on visual intervention to attract the attention of drivers [40], [41]. For typical pedestrian collisions, the augmented reality (AR) technique has been used to detect the sudden appearance of a pedestrian and warn drivers [42], [43], or the car is stopped with an autonomous emergency braking system (AEB) when sensors detect a potential collision with a pedestrian [44], [45]. However, these systems do not perform well in emergency situations, and even the most intelligent level of the L3 autopilot system has failed in the 2019i-VISTA test.…”

Evidence for the Crash Avoidance Effectiveness of Intelligent and Connected Vehicle Technologies

“…Generally, the number of typical crashes used in a SIM study is between 500 and 1000. A technical model of and ICV technology is created by directly defining the main characteristics of the technology, such as sensor parameters and braking deceleration [10,11]. The other method uses the actual performance of the technology in the FOT to build a technical model [2,12].…”

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

“…When analyzing pedestrian-vehicle accidents, up to 73% of accidents could have been avoided with automated driving (Utriainen, 2021). Other researchers modeled that if all vehicles were equipped with an automatic emergency braking system, both the fatality risk and the injury risk of pedestrians could be decreased drastically by up to 87% (Haus et al, 2019). However, these advantages can only come into play when automated vehicles are understood, trusted, and used (Walch et al, 2017).…”