1. A method of calculating a potential collision severity index, the method comprising:

determining related information of a plurality of second vehicles around the unmanned first vehicle according to the pre-acquired environmental point cloud, wherein the related information of the second vehicles comprises: a speed of the second vehicle relative to the first vehicle, a heading angle of the second vehicle, a position of the second vehicle relative to the first vehicle, and a distance of the second vehicle relative to the first vehicle;

and calculating the potential collision severity index by adopting a preset potential collision severity index algorithm according to the pre-acquired first vehicle course angle and the related information of the plurality of second vehicles.

2. The method for calculating the potential collision severity index according to claim 1, wherein the calculating the potential collision severity index by using a preset potential collision severity index algorithm according to the pre-obtained first vehicle course angle and the related information of the plurality of second vehicles comprises:

calculating the collision severity, a first weighting coefficient and a second weighting coefficient of each second vehicle according to the pre-acquired related information of the second vehicles respectively;

and calculating the potential collision severity index of the first vehicle by adopting a preset potential collision severity index algorithm according to the calculated collision severity of the plurality of second vehicles, the plurality of first weighting coefficients and the plurality of second weighting coefficients.

3. The method of calculating the potential collision severity index according to claim 2, wherein said calculating the collision severity, the first weighting factor and the second weighting factor of each second vehicle based on the pre-acquired information about the second vehicle, respectively, comprises:

if the first vehicle and the second vehicle collide, calculating a collision angle between the first vehicle and the second vehicle according to a first vehicle course angle and a second vehicle course angle which are acquired in advance;

calculating the collision severity corresponding to the collision angle of the second vehicle according to a preset fitting formula;

applying a preset first weighting coefficient formula to calculate a first weighting coefficient corresponding to the second vehicle according to the first vehicle course angle and the position of the second vehicle relative to the first vehicle;

and applying a preset second weighting coefficient formula to calculate a second weighting coefficient corresponding to the second vehicle according to the vehicle speed of the second vehicle relative to the first vehicle and the distance between the second vehicle and the first vehicle.

4. The method for calculating the potential collision severity index according to claim 3, wherein calculating the collision angle of the first vehicle and the second vehicle according to the pre-obtained first vehicle heading angle and the second vehicle heading angle if the first vehicle and the second vehicle collide comprises:

if the first vehicle and the second vehicle collide, calculating the collision angle between the first vehicle and the second vehicle by adopting an angle formula according to a first vehicle course angle and a second vehicle course angle which are obtained in advance, wherein the angle formula is as follows:

θ_ci＝|π-θ_ti-θ|

in the above formula, θ represents a first vehicle heading angle, θ_tiIndicating the ith second vehicle heading angle, θ_ciThe vehicle collision angle is represented by the collision angle between the first vehicle and the ith second vehicle, i represents the number of the second vehicle, and i is a positive integer greater than or equal to 1.

5. The method of calculating a potential collision severity index according to claim 3 wherein said fitting formula is:

f(x)＝a₀+a₁ cos(x·w)+b₁ sin(x·w)+a₂ cos(2x·w)+b₂ sin(2x·w)+a₃ cos(3x·w)+b₃ sin(3x·w)+a₄ cos(4x·w)+b₄ sin(4x·w)+a₅ cos(5x·w)+b₅ sin(5x·w)+a₆ cos(6x·w)+b₆ sin(6x·w)

wherein, a₀-a_j、b₁-b_jAnd w is a preset parameter; wherein j ∈ 1 ~ 6, x represents the impact angle of the first vehicle and the ith second vehicle.

6. Method for calculating a potential collision severity index according to claim 5, characterized in that a₀Set to-16.12, a₁Set to-19.06, a₂Set to 11.46, a₃Is set to 19.86, a₄Set as 7.853, a₅Set to-1.154, a₆Set to-1.33; w is set to 1.371; b₁Set as 28.91, b₂Set to 26.71, b₃Is set to be 3.564, b₄Is set to-8.256, b₅Is set to-4.983, b₆Set to-0.4932.

7. The method of calculating a potential collision severity index according to claim 3 wherein said first weighting factor is formulated as:

in the above formula, x_tiAnd y_tiAre respectively the firsti coordinate values in the X direction and the Y direction of the position of the second vehicle with respect to the first vehicle; w_1iAnd representing the ith second vehicle first weighting coefficient, and theta is the first vehicle heading angle.

8. The method of calculating a potential collision severity index according to claim 7 wherein said second weighting factor is formulated as:

wherein the content of the first and second substances,L_riindicating the distance of the ith second vehicle relative to the first vehicle; v_riRepresenting a vehicle speed of an ith second vehicle relative to the first vehicle; k is an adjustable parameter and a is the maximum braking deceleration of the first vehicle on the current road surface.

9. The method for calculating the potential collision severity index according to claim 8, wherein calculating the potential collision severity index of the first vehicle according to the calculated collision severity of the plurality of second vehicles, the plurality of first weighting coefficients and the plurality of second weighting coefficients by using a preset potential collision severity index algorithm comprises: calculating a potential collision severity index using the following formula:

where n is the number of second vehicles, PCSI represents a potential crash severity index, f (θ)_ci) Indicating the severity of the collision of the first vehicle with the ith second vehicle.

10. An apparatus for calculating a potential collision severity index, the apparatus comprising: a memory and a processor; wherein the memory is used for storing a program for calculating the potential collision severity index, and the processor is used for reading and executing the program for calculating the potential collision severity index and executing the method of any one of claims 1-9.

11. A collision severity index calculation system, the system comprising: laser radar, GPS, ECU and CAN bus;

wherein the ECU comprises a 3D object detection module and means for calculating a potential collision severity index as claimed in claim 10; the laser radar is connected with the ECU in a one-way mode, the GPS is connected with the ECU in a one-way mode, and the ECU is connected with the CAN bus in a one-way mode; the 3D object detection module and the apparatus for calculating a potential collision severity index according to claim 10 are connected in one direction.

Background

The Potential Collision Severity Index (PCSI) plays an important role in the field of unmanned safety, and provides a basis for selecting a path with low collision Severity for an unmanned vehicle when an unavoidable collision is about to occur. In 2019, Hong Wang proposed a potential severity index calculation method in a paper Crash differentiation in Motion Planning for Autonomous Vehicles, but the method only gives a specific determined value to each section in a piecewise function mode. The original continuous system is converted into a discrete system, so that the subsequent optimization solution of the continuous system is converted into an optimization solution of a hybrid system with continuous and discrete coexistence, the calculation amount and the calculation time are greatly increased, and the real-time solution is difficult.

Disclosure of Invention

The application provides a method, a device and a system for calculating a potential collision severity index.

The application provides a method for calculating a potential collision severity index, comprising:

determining related information of a plurality of second vehicles around the unmanned first vehicle according to the pre-acquired environmental point cloud, wherein the related information of the second vehicles comprises: a speed of the second vehicle relative to the first vehicle, a heading angle of the second vehicle, a position of the second vehicle relative to the first vehicle, and a distance of the second vehicle relative to the first vehicle;

and calculating the potential collision severity index by adopting a preset potential collision severity index algorithm according to the pre-acquired first vehicle course angle and the related information of the plurality of second vehicles.

In an exemplary embodiment, the calculating the potential collision severity index by using a preset potential collision severity index algorithm according to the pre-acquired first vehicle heading angle and the related information of the plurality of second vehicles includes:

calculating the collision severity, a first weighting coefficient and a second weighting coefficient of each second vehicle according to the pre-acquired related information of the second vehicles respectively;

and calculating the potential collision severity index of the first vehicle by adopting a preset potential collision severity index algorithm according to the calculated collision severity of the plurality of second vehicles, the plurality of first weighting coefficients and the plurality of second weighting coefficients.

In an exemplary embodiment, the calculating the collision severity, the first weighting coefficient, and the second weighting coefficient for each of the second vehicles based on the pre-acquired information about the second vehicles, respectively, includes:

if the first vehicle and the second vehicle collide, calculating a collision angle between the first vehicle and the second vehicle according to a first vehicle course angle and a second vehicle course angle which are acquired in advance;

calculating the collision severity corresponding to the collision angle of the second vehicle according to a preset fitting formula;

applying a preset first weighting coefficient formula to calculate a first weighting coefficient corresponding to the second vehicle according to the first vehicle course angle and the position of the second vehicle relative to the first vehicle;

and applying a preset second weighting coefficient formula to calculate a second weighting coefficient corresponding to the second vehicle according to the vehicle speed of the second vehicle relative to the first vehicle and the distance between the second vehicle and the first vehicle.

In an exemplary embodiment, if the first vehicle and the second vehicle collide, the calculating the collision angle between the first vehicle and the second vehicle according to the first vehicle heading angle and the second vehicle heading angle acquired in advance includes:

if the first vehicle and the second vehicle collide, calculating the collision angle of the first vehicle and the second vehicle by adopting an angle formula according to a first vehicle course angle and a second vehicle course angle which are obtained in advance, wherein the angle formula is as follows:

θ_ci＝|π-θ_ti-θ|

in the above formula, θ represents a first vehicle heading angle, θ_tiIndicating the ith second vehicle heading angle, θ_ciIndicating a first vehicle and a second vehicleThe collision angles of i second vehicles, i represents the numbers of the second vehicles, and i is a positive integer greater than 1.

In an exemplary embodiment, the fitting formula is:

wherein, a₀-a_j、b₁-b_jAnd w is a preset parameter; wherein j ∈ 1 ~ 6, x represents the impact angle of the first vehicle and the ith second vehicle.

In an exemplary embodiment, a₀Set to-16.12, a₁Set to-19.06, a₂Set to 11.46, a₃Is set to 19.86, a₄Set as 7.853, a₅Set to-1.154, a₆Set to-1.33; w is set to 1.371; b₁Set as 28.91, b₂Set to 26.71, b₃Is set to be 3.564, b₄Is set to-8.256, b₅Is set to-4.983, b₆Set to-0.4932.

In an exemplary embodiment, the first weighting factor is formulated as:

in the above formula, x_tiAnd y_tiCoordinate values in the X direction and the Y direction which are the positions of the ith vehicle and the second vehicle relative to the first vehicle, respectively; w_1iAnd representing the ith second vehicle first weighting coefficient, and theta is the first vehicle heading angle.

In an exemplary embodiment, the second weighting factor is formulated as:

wherein the content of the first and second substances,L_riindicating the distance of the ith second vehicle relative to the first vehicle; v_riRepresenting a vehicle speed of an ith second vehicle relative to the first vehicle; k is an adjustable parameter and a is the maximum braking deceleration of the first vehicle on the current road surface.

In an exemplary embodiment, the calculating the potential collision severity index of the first vehicle according to the calculated collision severity of the plurality of second vehicles, the plurality of first weighting coefficients and the plurality of second weighting coefficients by using a preset potential collision severity index algorithm includes: calculating a potential collision severity index using the following formula:

where n is the number of second vehicles, PCSI represents a potential crash severity index, f (θ)_ci) Indicating the severity of the collision of the first vehicle with the ith second vehicle.

The present application also provides an apparatus for calculating a potential collision severity index, the apparatus comprising: a memory and a processor; the memory is used for storing a program for calculating the potential collision severity index, and the processor is used for reading and executing the program for calculating the potential collision severity index and executing the method in any one of the above embodiments.

The present application also provides a collision severity index calculation system, the system comprising: laser radar, GPS, ECU and CAN bus;

wherein the ECU comprises a 3D object detection module and means for calculating a potential collision severity index as claimed in claim 10; the laser radar is connected with the ECU in a one-way mode, the GPS is connected with the ECU in a one-way mode, and the ECU is connected with the CAN bus in a one-way mode; the 3D object detection module is connected with the device for calculating the potential collision severity index in a one-way mode in the embodiment.

Compared with the related art, the method, the device and the system for calculating the potential collision severity index in the application comprise the following steps: determining related information of a plurality of second vehicles around the unmanned first vehicle according to the pre-acquired environmental point cloud, wherein the related information of the second vehicles comprises: a speed of the second vehicle relative to the first vehicle, a heading angle of the second vehicle, a position of the second vehicle relative to the first vehicle, and a distance of the second vehicle relative to the first vehicle; and calculating the potential collision severity index by adopting a preset potential collision severity index algorithm according to the pre-acquired first vehicle course angle and the related information of the plurality of second vehicles. In the embodiment of the disclosure, by adopting the method for calculating the collision severity index, not only can continuous potential collision severity index values be obtained, but also the subsequent solving speed can be faster.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. Other advantages of the present application may be realized and attained by the instrumentalities and combinations particularly pointed out in the specification and the drawings.

Drawings

The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.

FIG. 1 is a flow chart of a method for calculating a potential collision severity index according to an embodiment of the present application;

FIG. 2 is a schematic diagram of an apparatus for calculating a potential collision severity index according to an embodiment of the present application;

fig. 3 is a block diagram of a collision severity index calculation system according to an embodiment of the present application.

Detailed Description

The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.

The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.

The embodiment of the present disclosure provides a method for calculating a potential collision severity index, as shown in fig. 1, the method includes:

s100, determining relevant information of a plurality of second vehicles around the unmanned first vehicle according to the pre-acquired environmental point cloud;

and S110, calculating the potential collision severity index by adopting a preset potential collision severity index algorithm according to the pre-acquired first vehicle course angle and the related information of a plurality of second vehicles.

In this embodiment, the information about the second vehicle includes: a speed of the second vehicle relative to the first vehicle, a heading angle of the second vehicle, a position of the second vehicle relative to the first vehicle, and a distance of the second vehicle relative to the first vehicle.

The method for calculating the potential collision severity index is applied to an unmanned potential collision severity index calculation system, and the structure diagram of the unmanned potential collision severity index calculation system is shown in FIG. 3 and comprises the following steps: laser radar, GPS, ECU and CAN bus.

In the present embodiment, the lidar acquires the point cloud Pe of the environment around the unmanned vehicle (first vehicle), and transmits it to the ECU. The GPS acquires a self-vehicle heading angle theta of the unmanned vehicle (first vehicle), namely a first vehicle heading angle, and sends the self-vehicle heading angle theta to the ECU. The 3D target detection module integrated in the ECU acquires the environmental point cloud P sent to the ECU by the laser radar_eDetecting information related to the second vehicle, the information including: the speed of the second vehicle relative to the first vehicle, the heading angle of the second vehicle, the position of the second vehicle relative to the first vehicle, and the distance of the second vehicle relative to the first vehicle are sent to a potential collision severity index calculation module. A3D target detection module integrated in an ECU acquires environmental point cloud P transmitted to the ECU by a laser radar based on a method proposed by Lang A H in 2019 in a paper Fast encoders for object detection from point clouds_eInformation related to an ith other vehicle (second vehicle) around the unmanned vehicle (first vehicle) is detected.

In an exemplary embodiment, the calculating the potential collision severity index by using a preset potential collision severity index algorithm according to the pre-acquired first vehicle heading angle and the related information of the plurality of second vehicles includes:

calculating the collision severity, a first weighting coefficient and a second weighting coefficient of each second vehicle according to the pre-acquired related information of the second vehicles respectively;

and calculating the potential collision severity index of the first vehicle by adopting a preset potential collision severity index algorithm according to the calculated collision severity of the plurality of second vehicles, the plurality of first weighting coefficients and the plurality of second weighting coefficients.

In an exemplary embodiment, the calculating the collision severity, the first weighting coefficient, and the second weighting coefficient for each of the second vehicles based on the pre-acquired information about the second vehicles, respectively, includes:

if the first vehicle and the second vehicle collide, calculating a collision angle between the first vehicle and the second vehicle according to a first vehicle course angle and a second vehicle course angle which are acquired in advance;

calculating the collision severity corresponding to the collision angle of the second vehicle according to a preset fitting formula;

applying a preset first weighting coefficient formula to calculate a first weighting coefficient corresponding to the second vehicle according to the first vehicle course angle and the position of the second vehicle relative to the first vehicle;

and applying a preset second weighting coefficient formula to calculate a second weighting coefficient corresponding to the second vehicle according to the vehicle speed of the second vehicle relative to the first vehicle and the distance between the second vehicle and the first vehicle.

In an exemplary embodiment, if the first vehicle and the second vehicle collide, the calculating the collision angle between the first vehicle and the second vehicle according to the first vehicle heading angle and the second vehicle heading angle acquired in advance includes:

if the first vehicle and the second vehicle collide, calculating the collision angle of the first vehicle and the second vehicle by adopting an angle formula according to a first vehicle course angle and a second vehicle course angle which are obtained in advance, wherein the angle formula is as follows:

θ_ci＝|π-θ_ti-θ|

in the above formula, θ represents a first vehicle heading angle, θ_tiIs shown asi second vehicle heading angles, θ_ciThe vehicle collision angle is represented by the collision angle between the first vehicle and the ith second vehicle, i represents the number of the second vehicle, and i is a positive integer greater than or equal to 1.

In an exemplary embodiment, the fitting formula is:

wherein, a₀-a_j、b₁-b_jAnd w is a preset parameter; wherein j ∈ 1 ~ 6, x represents the impact angle of the first vehicle and the ith second vehicle.

In an exemplary embodiment, a₀Set to-16.12, a₁Set to-19.06, a₂Set to 11.46, a₃Is set to 19.86, a₄Set as 7.853, a₅Set to-1.154, a₆Set to-1.33; w is set to 1.371; b₁Set as 28.91, b₂Set to 26.71, b₃Is set to be 3.564, b₄Is set to-8.256, b₅Is set to-4.983, b₆Set to-0.4932.

In an exemplary embodiment, the first weighting factor is formulated as:

in the above formula, x_tiAnd y_tiCoordinate values in the X direction and the Y direction which are the positions of the ith vehicle and the second vehicle relative to the first vehicle, respectively; w_1iAnd representing the ith second vehicle first weighting coefficient, and theta is the first vehicle heading angle.

In an exemplary embodiment, the second weighting factor is formulated as:

wherein the content of the first and second substances,L_riindicating the distance of the ith second vehicle relative to the first vehicle; v_riRepresenting a vehicle speed of an ith second vehicle relative to the first vehicle; k is an adjustable parameter and a is the maximum braking deceleration of the first vehicle on the current road surface.

In an exemplary embodiment, the calculating the potential collision severity index of the first vehicle according to the calculated collision severity of the plurality of second vehicles, the plurality of first weighting coefficients and the plurality of second weighting coefficients by using a preset potential collision severity index algorithm includes: calculating a potential collision severity index using the following formula:

where n is the number of second vehicles, PCSI represents a potential crash severity index, f (θ)_ci) Indicating the severity of the collision of the first vehicle with the ith second vehicle.

The embodiment of the present disclosure also provides an apparatus for calculating a potential collision severity index, as shown in fig. 2, the apparatus including: a memory 210 and a processor 220; the memory is used for storing a program for calculating the potential collision severity index, and the processor is used for reading and executing the program for calculating the potential collision severity index and executing the method in any one of the above embodiments.

The disclosed embodiment also provides a collision severity index calculation system, which includes: laser radar, GPS, ECU and CAN bus; wherein the ECU comprises a 3D object detection module and the means for calculating the potential collision severity index as described in the above embodiments; the laser radar is connected with the ECU in a one-way mode, the GPS is connected with the ECU in a one-way mode, and the ECU is connected with the CAN bus in a one-way mode; the 3D object detection module is unidirectionally connected with the means for calculating the potential collision severity index as described in the above embodiments. The laser radar acquires a point cloud Pe of the surrounding environment of the unmanned vehicle and sends the point cloud Pe to the ECU. The GPS acquires the self-vehicle course angle theta of the unmanned vehicle and sends the self-vehicle course angle theta to the ECU. The ECU receives the environmental point cloud Pe sent by the laser radar and the vehicle course angle theta sent by the GPS, calculates the potential collision severity index PCSI by utilizing the 3D target detection module and the potential collision severity index calculation module which are integrated in the environment point cloud Pe and the vehicle course angle theta, and sends the potential collision severity index PCSI to the CAN bus.

The collision severity index calculation system can obtain continuous potential collision severity index values, meanwhile, the indexes are corrected according to the course angle of the first vehicle and the relative position of the second vehicle of the unmanned vehicle, and weighting is carried out according to the relative speed. Compared with the existing discrete potential collision severity index, the method is friendly to subsequent unmanned path planning and solving, is high in solving speed, and avoids the interference of the index on the subsequent system optimization solving when no potential collision exists.

Example 1

The example provides a process for calculating a potential collision severity index, which is applied to an unmanned potential collision severity index calculation system, and the unmanned potential collision severity index calculation system comprises: laser radar, GPS, ECU and CAN bus. The ECU comprises a 3D object detection module and a device for calculating the potential collision severity index. The method comprises the following steps:

the method comprises the following steps: laser radar obtains unmanned vehicle surrounding environment point cloud P_eAnd sent to the ECU. The unmanned vehicle is used as a first vehicle; a plurality of vehicles around the unmanned vehicle serve as second vehicles.

Step two: the GPS acquires a course angle theta of the first vehicle and sends the course angle theta to the ECU.

Step three: the 3D target detection module integrated in the ECU determines related information of a plurality of second vehicles around the unmanned first vehicle by acquiring environmental point cloud sent to the ECU by the laser radar, wherein the related information of the second vehicles comprises: a speed of the second vehicle relative to the first vehicle, a heading angle of the second vehicle, a position of the second vehicle relative to the first vehicle, and a distance of the second vehicle relative to the first vehicle.

Step four: the potential collision severity index calculation device calculates a potential collision severity index PCSI, and is particularly subdivided into the following steps:

step 4.1: if the first vehicle and the second vehicle collide, calculating the collision angle of the first vehicle and the second vehicle by adopting an angle formula according to a first vehicle course angle and a second vehicle course angle which are obtained in advance, wherein the angle formula is as follows:

θ_ci=|π-θ_ti-θ|

in the above formula, θ represents a first vehicle heading angle, θ_tiIndicating the ith second vehicle heading angle, θ_ciThe collision angle of the first vehicle and the ith second vehicle is shown, i represents the number of the second vehicle, and i is a positive integer larger than 1.

Step 4.2: the potential collision severity index calculation device calculates the severity of the collision at different angles according to a fitting formula, wherein the preferable fitting formula is as follows:

wherein, a₀-a_j、b₁-b_jAnd w is a preset parameter; wherein j ∈ 1 ~ 6, x represents the impact angle of the first vehicle and the ith second vehicle.

Preferably, the parameters are as follows:

a₀set to-16.12, a₁Set to-19.06, a₂Set to 11.46, a₃Is set to 19.86, a₄Set as 7.853, a₅Set to-1.154, a₆Set to-1.33; w is set to 1.371; b₁Set as 28.91, b₂Set to 26.71, b₃Is set to be 3.564, b₄Is set to-8.256, b₅Is set to-4.983, b₆Set to-0.4932.

Step 4.3: the means for calculating the severity of potential collision index is based on a first vehicle heading angle theta and a position P of the second vehicle relative to the first vehicle_tiCalculating a first weighting coefficient W corresponding to the second vehicle by applying a preset first weighting coefficient formula_1i，

Wherein, W_1iComprises the following steps:

in the above formula, x_tiAnd y_tiCoordinate values in the X direction and the Y direction which are the positions of the ith vehicle and the second vehicle relative to the first vehicle, respectively; w_1iAnd representing the ith second vehicle first weighting coefficient, and theta is the first vehicle heading angle.

Step 4.4: the potential collision severity index calculation device calculates a second weighting coefficient corresponding to the second vehicle by applying a preset second weighting coefficient formula according to the vehicle speed of the second vehicle relative to the first vehicle and the distance of the second vehicle relative to the first vehicle.

For example: according to the relative speed V of the ith second vehicle relative to the first vehicle_riAnd the distance L of the second vehicle relative to the first vehicle_riCalculating a weighting coefficient W corresponding to the ith second vehicle_2i. Wherein the second weighting coefficient formula is:

wherein the content of the first and second substances,

L_riindicating the distance of the ith second vehicle relative to the first vehicle; v_riRepresenting a vehicle speed of an ith second vehicle relative to the first vehicle; k is an adjustable parameter, and the greater the value of k, the greater the weighting coefficient W₂The more drastic the change; a is the maximum braking deceleration of the first vehicle on the current road surface, and can be obtained through a common road adhesion coefficient algorithm.

Step 4.5: the potential collision severity index calculation device calculates the potential collision severity index of the first vehicle by using a preset potential collision severity index algorithm according to the calculation results (the calculation results include the collision severity of the plurality of second vehicles, the plurality of first weighting coefficients and the plurality of second weighting coefficients) of step 4.1 to step 4.4, and includes: calculating a potential collision severity index using the following formula:

where n is the number of second vehicles, PCSI represents a potential crash severity index, f (θ)_ci) Indicating the severity of the collision of the first vehicle with the ith second vehicle.

Step five: and the device for calculating the potential collision severity index outputs the PCSI value to the CAN bus.

In the above example, successive values of potential crash severity indices may be obtained, with the indices also being modified based on the heading angle of the first vehicle and the relative position of the second vehicle of the drone vehicle, and weighted based on relative velocity. Compared with the existing discrete potential collision severity index, the method is friendly to subsequent unmanned path planning and solving, is high in solving speed, and avoids the interference of the index on the subsequent system optimization solving when no potential collision exists.

It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.