Statistical means of the assessment of the passive safety of road vehicles

Abstract

The article discusses the main criteria for assessing the design of vehicles in the field of passive safety. Road safety is influenced by a large number of factors, among the most negative factors are road accidents and their consequences. All categories of the population die and get injured in road accidents, which entails a decrease in the population of the country and the level of health, life expectancy, length of the working period, and distortion of the age pyramid. Existing methods for assessing measures to improve passive safety do not fully allow establishing a correspondence between the characteristics of vehicle structural elements that ensure its passive safety and the severity of the consequences in an accident. This situation is mainly due to the impossibility of assessing the passive safety of the car as a whole, by one indicator, since the applied assessment is based on the requirements of regulatory documents. In this case, the most optimal analysis will be the analysis of the consequences of real road accidents, which allows to identify the most dangerous conditions of road accidents and traumatic elements of the structure of vehicles. The result of the assessment of measures to improve passive safety is an accurate assessment of the severity of human injury in an accident and the risk of an accident. The existing and prospective criteria governing passive safety indicators cover a small number of all types of road accidents. Therefore, expanding the scope of application of safety criteria to the widest possible range of types of road accidents and taking into account the anthropological parameters of a person is the most significant potential way to reduce the number and severity of injuries in road accidents.

Injury prediction algorithm for rear‐seat occupants in advanced automatic crash notification systems

“…Chest AIS 3+ risk function can be expressed in terms of D max as (5) where P chest denotes the AIS probability of the chest, and P chest3+ indicates the AIS 3+ probability of the chest.…”

“…Most of the existing prediction algorithms input the accident data into a regression model. These data encompass the driver injury level, whether the driver wore a seat belt, whether an airbag was deployed, and the type of crash, which can aid in developing a corresponding driver injury prediction algorithm [3][4][5].…”

“…Most of the existing prediction algorithms input the accident data into a regression model. These data encompass the driver injury level, whether the driver wore a seat belt, whether an airbag was deployed, and the type of crash, which can aid in developing a corresponding driver injury prediction algorithm [3–5]. Typically, logistic regression is the most mature and widely used approach in AACN algorithms; it has been applied in the OnStar [6], URGENCY [7] and OTDA [8] algorithms.…”