Method and device for providing a piece of injury information about an injury of an unprotected road user in the event of a collision with a vehicle

Abstract

A method for providing a piece of injury information about an injury of an unprotected road user in the event of a collision with a vehicle. In this method, a probability value, which represents a probability of a degree of severity of the injury, is determined using a function representing the degree of severity as a function of at least one collision parameter characterizing the collision. A piece of injury information is generated using the probability value, which is finally sent to a receiving station of an emergency call system.

Claims

1. A method for providing a piece of injury information about an injury of an unprotected road user in the event of a collision with a vehicle, comprising the following steps: determining a probability value which represents a probability of a degree of severity of the injury, using a function which represents the degree of severity as a function of at least one collision parameter characterizing the collision; producing the injury information using the probability value; and sending the injury information to a receiving station of an emergency call system to provide the injury information; wherein in the step of determining, at least one additional probability value, which represents a probability of an additional degree of severity of the injury, is determined using: (i) the function and/or (ii) a further function representing a further degree of severity of the injury, as a function of the collision parameter, the injury information being generated in the step of generating using the additional probability value; wherein in the step of determining, the additional probability value is determined by subtracting a sum of the probability value and a further probability value from a maximum probability value representing a maximum probability.

2. The method as recited in claim 1, wherein in the step of determining, the probability value is determined as the collision parameter as a function of a collision speed, with which the vehicle and the road user collide with one another.

3. The method as recited in claim 2, wherein in the step of determining, the probability value is determined using the following function: $f\left(v_c\right)=\frac{1}{1+e^{\left(a+b{v}_c\right)}},$ a standing for a first function parameter, b standing for a second function parameter and v.sub.c standing for the collision speed.

4. The method as recited in claim 1, further comprising the following step: reading in an activation signal, which represents an activation of at least one passenger protection device of the vehicle, the probability value being determined in the step of determining using the activation signal in response to the activation.

5. The method as recited in claim 1, further comprising the following step: ascertaining the collision parameter using an additional piece of information relating to the road user received via a communication interface of the vehicle.

6. The method as recited in claim 1, wherein the person protection device is a liftable engine hood or an airbag external to the vehicle.

7. A device configured to provide a piece of injury information about an injury of an unprotected road user in the event of a collision with a vehicle, the device configured to: determine a probability value which represents a probability of a degree of severity of the injury, using a function which represents the degree of severity as a function of at least one collision parameter characterizing the collision; read in an activation signal, which represents an activation of at least one person protection device of the vehicle, the probability value being determined using the activation signal in response to the activation; produce the injury information using the probability value; and send the injury information to a receiving station of an emergency call system to provide the injury information; wherein for the determination, at least one additional probability value, which represents a probability of an additional degree of severity of the injury, is determined using: (i) the function and/or (ii) a further function representing a further degree of severity of the injury, as a function of the collision parameter, the injury information being generated in the step of generating using the additional probability value; wherein for the determination, the additional probability value is determined by subtracting a sum of the probability value and a further probability value from a maximum probability value representing a maximum probability.

8. A non-transitory machine-readable memory medium on which is stored a computer program for providing a piece of injury information about an injury of an unprotected road user in the event of a collision with a vehicle, the computer program, when executed by a computer, causing the computer to perform the following steps: determining a probability value which represents a probability of a degree of severity of the injury, using a function which represents the degree of severity as a function of at least one collision parameter characterizing the collision; reading in an activation signal, which represents an activation of at least one person protection device of the vehicle, the probability value being determined in the step of determining using the activation signal in response to the activation; producing the injury information using the probability value; and sending the injury information to a receiving station of an emergency call system to provide the injury information; wherein in the step of determining, at least one additional probability value, which represents a probability of an additional degree of severity of the injury, is determined using: (i) the function and/or (ii) a further function representing a further degree of severity of the injury, as a function of the collision parameter, the injury information being generated in the step of generating using the additional probability value; wherein in the step of determining, the additional probability value is determined by subtracting a sum of the probability value and a further probability value from a maximum probability value representing a maximum probability.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) Exemplary embodiments of the present invention are depicted in the figures and are described in greater detail below.

(2) FIG. 1 schematically shows a representation of a vehicle including an example device according to one specific embodiment.

(3) FIG. 2 schematically shows a representation of an example device from FIG. 1.

(4) FIG. 3 shows diagrams for illustrating a distribution of an injury probability for determining different degrees of severity of an injury.

(5) FIG. 4 shows a diagram for illustrating a probability of an injury risk for a crossing pedestrian in a car-primary collision.

(6) FIG. 5 shows a diagram for illustrating functions for determining a probability value using a device according to one exemplary embodiment.

(7) FIG. 6 shows a block diagram of an example sequence when providing a piece of injury information using a device according to one exemplary embodiment.

(8) FIG. 7 shows a flow chart of an example method according to one exemplary embodiment.

(9) In the following description of preferred exemplary embodiments of the present invention, identical or similar reference numerals are used for elements which are represented in the various figures and act similarly, a repeated description of these elements being omitted.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

(10) FIG. 1 schematically shows a representation of a vehicle 100 including an example device 102 according to one exemplary embodiment. An unprotected road user 104, here for example, a pedestrian, which is detectable by a surroundings sensor 106 of vehicle 100, is located in front of vehicle 100. A collision between vehicle 100 and pedestrian 104 is imminent. Multiple pedestrians or unprotected road users, which are to be assessed in a situation, may also be detected in front of vehicle 100. If, for example, a crowd of people is approached, such an estimation may then also be made for multiple road users. Surroundings sensor 106 transmits a sensor signal 108 representing road user 104 to device 102, which is designed to ascertain a parameter characterizing the imminent collision, for example, a probable collision speed using sensor signal 108. Based on the parameter, device 102 determines a probability of one or of multiple different predefined degrees of severity of an injury of road user 104 caused by the collision. The determination of the probability takes place using a suitable injury risk function representing the respective degree of severity, which assigns a particular value of the parameter a particular probability value for the respective degree of severity. As a result of this determination, device 102 generates a piece of injury information 110, which represents, for example, a probable injury severity of the injury or other rescue-relevant data relating to road user 104, and sends this information via a suitable communication interface 112 of vehicle 100 wirelessly to an external receiving station 114 of an emergency call system 116, which initiates a rescue chain for rescuing road user 104 based on injury information 110, for example, by automatically notifying a rescue coordination center 118.

(11) FIG. 2 schematically shows a representation of a device 102 from FIG. 1. Device 102 includes a determination unit 210 for determining a probability value 212 representing the probability of the respective degree of severity using the function representing the respective degree of severity, and a collision parameter 214 characterizing the collision, also referred to below as parameter. A generating unit 220 is designed to generate injury information 110 using probability value 212 and to send it to the receiving station of the emergency call system.

(12) FIG. 3 shows two diagrams 300, 302 for illustrating a distribution of an injury probability for determining different degrees of severity of an injury using a device according to one exemplary embodiment, for example, the device described previously with reference to FIGS. 1 and 2. Left diagram 300 shows the injury probability as a function of one particular injury criterion. Plotted by way of example is a tolerance threshold 304 as well as seven degrees of severity 0, 1, 2, 3, 4, 5, 6 of a simplified rating scale, also referred to as abbreviated injury scale or AIS for short. Right diagram 302 shows the respective probability of the various degrees of severity 0, 1, 2, 3, 4, 5, 6.

(13) FIG. 4 shows a diagram 400 for illustrating a probability of an injury risk for a crossing pedestrian in a car-primary collision. A first area 402 characterizes the probability of a slight injury of the pedestrian, a second area 404 characterizes the probability of a severe injury of the pedestrian and a third area 406 characterizes the probability of a fatal injury of the pedestrian. The estimated injury severity of the pedestrian based on the collision speed from n=255 video-documented accidents is shown by way of example. The number n is plotted on the x-axis of diagram 400.

(14) If a threshold of greater than 50% is estimated as a measure of an injury severity, then the example illustrated in FIG. 4 shows that in 69% of the accidents, a probability of 50% of the pedestrians are slightly injured, 29% are severely injured and 2% are fatally injured. A driver typically initially runs out in order to provide first aid to the pedestrian or the generally unprotected road user. Here, valuable minutes for activating the rescue chain so that first aid measures may be immediately initiated have already elapsed. With knowledge of a possible injury severity, degree of severity according to body region, a walking direction and/or speed and, if necessary, a gender or age of the pedestrian, the rescue service already has valuable pieces of information at hand in order to provide first aid. Besides the primary collision and secondary collision with objects or the road, tertiary collisions, such as the hitting or running over by another previously uninvolved road user, may be included in the calculation.

(15) For example, the pedestrian is struck at a collision speed of approximately 48 km/h. With the above approach, this results in a probability that the pedestrian suffers up to 65% severe injuries and up to 7% fatal injuries. In the case of a frontal collision, the pedestrian suffers very severe injuries to the head and hip area, for example. If the device has a video sensor system, the walking direction or the gender may, as previously mentioned, be used to estimate the injury severity. Alternatively, the pieces of information about the walking direction of the pedestrian may also be provided via a radar sensor system. Another sensor system or a communication with mobile terminals is also possible.

(16) FIG. 5 shows a diagram 500 for illustrating function values 502, 504 of two functions for determining a probability value using a device according to one exemplary embodiment. Injury risk functions for severe and fatal injuries in the event of a collision of a pedestrian with a forward moving car are shown, for example. A y-axis represents a probability p of 0.00 to 1.00. The collision parameter, here a collision speed v.sub.c in km/h, is plotted on an x-axis. The probability values are each delineated with a double arrow as a percentage for three different degrees of severity “slightly injured” (p.sub.slight=32%), “severely injured” (p.sub.severe=68%) and “fatally injured” (p.sub.fatal=5.8%) at a collision speed v.sub.c=45 km/h. In this case, the curve of function value 502 is determined by a first function representing the degree of severity “severely injured” and “fatally injured,” whereas the curve of function value 504 is determined by a second function representing the degree of severity “fatally injured.”

(17) FIG. 6 shows a block diagram of an example sequence when providing a piece of injury information using an example device according to one exemplary embodiment. In this case, the injury severity of the road user is ascertained in a block 600 as a function of the activation of assisting or active or passive protection devices for unprotected road users of the vehicles in a block 602, as a function of the provision of vehicle data in a block 604 or as a function of data from an EEPROM or other memory in a block 606. The injury severity ascertained in block 600 is transmitted in a block 608 to the emergency call system.

(18) FIG. 7 shows a flow chart of an example method 700 according to one exemplary embodiment. The method may be carried out, for example, by a device described previously with reference to FIGS. 1 through 6. In this case, the probability of a particular degree of severity of the injury of the road user is determined in a step 710 by assigning the collision parameter to a particular probability value with the aid of a suitable function. In a step 720, the injury information, which represents an injury severity, for example, is generated as a function of the probability ascertained in step 710. Finally, the injury information is sent in a step 730 to the receiving station of the emergency call system.

(19) Various exemplary embodiments of the approach presented herein are described once again below in other terms with reference to FIGS. 1 through 7.

(20) The injury severity, also referred to previously as degree of severity, characterizes the magnitude of changes of a physiological or structural nature. To describe the accident severity, a distinction is generally made between various body regions and types of injuries. These injury severities allow for a comparable and transparent reference to which rescue personnel, medical doctors or also vehicle engineers may refer. Thus, the injury severity is useful as a reference value in algorithms.

(21) Different injury mechanisms may be the cause in one and the same body region. Thus, different tolerance thresholds also exist for different persons. These tolerances may be statistically established in the event of a collision of a vehicle with a pedestrian or another unprotected road user.

(22) This dependency is reflected in an injury risk function, as it is shown, for example, in FIG. 3. In this case, AIS refers to the degree of severity of the injury, AIS=0 standing for uninjured and AIS=6 standing for fatally injured. The probability of an injury from 0 through 6 is then ascertained as a function of the injury criterion.

(23) Device 102 is designed to ascertain, to store and to provide this injury probability as information to other control units or to an emergency call unit, for example, on the basis of pieces of vehicle information in the event of a collision with one or multiple unprotected road users.

(24) The following pieces of information, for example, are used as a criterion for ascertaining such an injury probability: trigger decision for passive pedestrian protection; driving direction of the ego vehicle (forward, sideways or backward); initial speed or collision speed of the ego vehicle; preceding activation of an emergency braking system or evasion system of the ego vehicle; estimated age or gender of the unprotected road user; type of collision walking or driving direction or estimated walking speed or driving speed of the road user and entry angle and collision angle; pieces of car-to-VRU information such as age, gender, weight or previous illnesses of the road user, which are unblocked in an emergency situation and are conveyed, for example, via a smartphone or are stored on another device; pieces of information of a vehicle sensor system of the vehicle, which represent, for example, an actual position of the road user relative to the vehicle; pieces of information relating to disabilities of the road user, for example, directly or indirectly about a pace of the road user via detection of a wheelchair, of a cane or of a walking aid.

(25) The pieces of information are used to improve the determination of the injury severity depending on the availability of the sensor system.

(26) According to one exemplary embodiment, a one-dimensional injury risk function is used for this purpose. The underlying injury criterion, also previously referred to as collision parameter, is the collision speed. The functional correlation to the degree of the injury for a pedestrian is shown by way of example in FIG. 4. A collision speed of 45 km/h results, for example, in a probability of 5.8% for a fatal injury of the pedestrian. The pedestrian suffers severe or light injuries at a probability of 68% or 32%.

(27) The underlying functional correlation is established, for example, with the aid of a logit function. The formula in this case is:

(28) $f\left(v_c\right)=\frac{1}{1+e^{\left(a+b{v}_c\right)}},$

(29) The parameters a and b are variables, which are provided in a memory, for example. The variable v.sub.c refers to the collision speed and is obtained from vehicle data.

(30) According to one exemplary embodiment, assisting or active or passive safety devices are initially activated with the aid of device 102 in the event of an imminent collision. In this case, a corresponding sensor system supplies a piece of information about the collision with the road user. An injury probability is ascertained with the aid of vehicle data, here the collision speed at the point in time of the contact and their functional correlation. For this purpose, a corresponding table is provided for an implementation in a control unit. Alternatively or in addition, a calculation takes place via an approximation of the efunction.

(31) According to FIG. 6, the injury severity is ascertained using a trigger signal of an assisting or active or passive protection device. The information of the collision speed is obtained from vehicle data. In the same way, the activation of the corresponding protection devices takes place on the basis of these data. Parameters for describing the functional correlation are provided from a memory, for example. The following functional parameters are used, for example, for determining the probability of the degrees of severity “fatally injured” (fatal) and “severely and fatally injured” (fatal and severe).

(32) TABLE-US-00001 Fatal Fatal and severe a −6.9218 −1.7761 b 0.0917 0.0627

(33) The result is then provided as information for other systems, for example, for an eCall system.

(34) According to another exemplary embodiment of the present invention, an additional sensor system from the active safety or driver assistance is used. In this case, further pieces of information such as walking direction or speed of the road user are ascertained based on a pedestrian identification or bicycle identification. The line of sight of the road user is optionally also identified. Thus, it is possible to estimate whether the potentially struck road user is detected by the vehicle, is or is not involved in a collision. This has indirect consequences for muscle tension and thus also for the injury severity to be expected. These additional pieces of information are then used to estimate the injury severity. A multidimensional injury risk function, for example, is created for this purpose and stored in the memory in a parameterized manner. In this way, the quality of the calculation is improved.

(35) If an exemplary embodiment includes an “and/or” linkage between a first feature and a second feature, this is to be read in the sense that the exemplary embodiment according to one specific embodiment includes both the first feature and the second feature, and according to another specific embodiment, either only the first feature or only the second feature.