CN114639232A - Module and method for assessing the level of risk of lateral collision of a target vehicle - Google Patents
Module and method for assessing the level of risk of lateral collision of a target vehicle Download PDFInfo
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- G—PHYSICS
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Abstract
The present application relates to a method of assessing a level of risk of a lateral collision of a target vehicle, comprising: acquiring real-time scene information of a target vehicle and transversely adjacent vehicles which are adjacent in the transverse direction, wherein the real-time scene information comprises vehicle running parameter information; calculating a future travel track of each vehicle based on the vehicle travel parameter information, determining a dangerous vehicle to be in lateral collision with the target vehicle, and travel parameter information of each vehicle when the target vehicle and the dangerous vehicle are in lateral collision; determining a severity level of a lateral collision of a target vehicle based on traveling parameter information of the target vehicle when the target vehicle and a dangerous vehicle have the lateral collision; estimating or querying from a list an exposure level of the target vehicle for a lateral collision based on the severity level, based on an estimation model; and deriving the level of the target vehicle lateral collision risk based on the severity level, the exposure rate level, and an assumed specific controllability level.
Description
Technical Field
The present application relates to the field of vehicle safety, and in particular to a module and method for assessing the level of a target vehicle's lateral collision risk.
Background
With the increasing number of electronic/electrical systems (E/E) on vehicles, the road vehicle functional safety standard ISO provides guidelines for the development of automotive safety-related systems for the functional safety design of these systems. When working Safety design is performed on these systems in the ISO standard, an important step in the early stage is to perform hazard analysis and risk assessment on the systems, identify the hazards of the systems, and evaluate the risk level of the hazards, asil (automatic Safety Integration level). ASIL has four grades, a, B, C, D, where a is the lowest risk grade and D is the highest risk grade. At least one security objective is determined for each hazard, system-level security requirements are derived from the security objectives, and the security requirements are distributed to hardware and software.
The ASIL level determines the requirement on the system security, and the higher the ASIL level, the higher the security requirement on the system, and accordingly the higher the price paid for realizing the higher security, which means that the higher the diagnostic coverage of software/hardware is, the stricter the development flow and technical requirements are, the corresponding development cost is increased, and the development period is prolonged. ISO26262 proposes a method for reducing the ASIL level on the premise that the safety objective is met.
However, no measures or methods exist for evaluating the risk of a lateral collision of a vehicle, and no measures or methods for reducing the ASIL level in this respect can be developed.
Disclosure of Invention
It is an object of the present application to provide a measure and a method for assessing the level of risk of a lateral collision of a target vehicle.
According to a first aspect of the present application, there is provided a method of assessing a level of risk of a lateral collision of a target vehicle, comprising:
acquiring real-time scene information of the target vehicle in a state of running along a longitudinal direction and transversely adjacent vehicles adjacent in a transverse direction perpendicular to the longitudinal direction, wherein the real-time scene information comprises vehicle running parameter information;
calculating a future travel track of each vehicle based on the vehicle travel parameter information, determining a dangerous vehicle to be in lateral collision with the target vehicle, and travel parameter information of each vehicle when the target vehicle and the dangerous vehicle are in lateral collision;
determining a severity level of a lateral collision of a target vehicle based on traveling parameter information of the target vehicle when the target vehicle and a dangerous vehicle have the lateral collision;
estimating or querying from a list an exposure level of the target vehicle for a lateral collision based on the severity level, based on an estimation model; and
deriving the level of the target vehicle lateral collision risk based on the severity level, the exposure rate level, and an assumed specific controllability level.
According to one embodiment, the vehicle driving parameter information comprises at least the following real-time variability parameter information: a real-time velocity, a real-time relative acceleration, and a real-time position between the laterally adjacent vehicle and the target vehicle.
According to one embodiment, the position information of the laterally adjacent vehicle is expressed as its abscissa and ordinate in a vehicle coordinate system with the specified position point of the target vehicle as an origin of coordinates and with the lateral direction and the longitudinal direction as X-axis and Y-axis.
According to one embodiment, the designated location point of the target vehicle is a midpoint of a length of a rear axle of the target vehicle in the width direction.
According to one embodiment, the vehicle driving parameter information further comprises non-variable fixed parameter information, and/or the real-time scene information further comprises road type information and road surface state information.
According to one embodiment, the severity level of a lateral collision of a target vehicle is determined based on the lateral relative speed of the target vehicle and a dangerous vehicle at the time of the lateral collision.
According to one embodiment, the assumed specific controllability level is the highest controllability level representing almost uncontrollable.
According to one embodiment, the sensor sensing the real-time scene information is disposed on an autonomous vehicle that is traveling in synchronization with the target vehicle.
According to one embodiment, where the exposure level is estimated based on an estimation model, the estimation model is expressed as:
wherein E represents the exposure level to be determined; sx (x is 0, 1, 2, or 3) represents the previously determined severity level. The numerator ∑ c (Sx) represents the number of measurement cycles corresponding to the previously determined severity level Sx of all measurement cycles; the denominator ∑ c (sall) represents the number of all measurement cycles.
According to a second aspect of the present application, there is provided a module for assessing the level of risk of a lateral collision of a target vehicle, comprising: a processor; and a memory storing executable instructions that, when executed, cause the processor to perform the above-described method.
According to a third aspect of the present application, there is provided a readable storage medium having stored thereon executable instructions that, when executed, cause a machine to perform the above-described method.
According to the method and the device, the severity grade S and the exposure grade E are determined by collecting real-time scene information of the running of the target vehicle and specifically based on the relative speed and the acceleration between the target vehicle and the transverse adjacent vehicle, and the grade of the transverse collision risk of the target vehicle is obtained based on the given controllability grade C and the determined severity grade S and the determined exposure grade E. This determination of the lateral collision risk level provides the possibility for subsequent development of risk-reducing measures and methods, so that the design requirements on software and hardware and the associated costs can be reduced, without having to design for coping with excessively high risk levels.
Drawings
FIG. 1 is a schematic illustration of a real-time scenario of target vehicle travel assumed in the method for assessing a level of risk of a lateral collision of a target vehicle of the present application;
FIG. 2 illustrates a flow chart of a method of the present application for assessing a level of risk of a lateral collision of a target vehicle; and
fig. 3 schematically shows a block diagram of modules for performing the method of fig. 1.
Detailed Description
The modules and the method are used for evaluating the level of the lateral collision risk of the vehicle based on the real-time scene information of the running of the target vehicle. The evaluation of the Level of the target vehicle lateral collision risk can be used as a part of an automobile Safety integrity Level (ASIL Level) evaluation function, so that the ASIL Level evaluation function is more comprehensive, the cost of software and hardware design or research and development is saved, and the cost of system functional verification is greatly saved.
Like the ASIL rating assessment, the present application also considers the assessment of the risk of a lateral collision of a target vehicle in three aspects, namely Severity (Severity), Exposure (Exposure), and Controllability. These three aspects are represented by a severity level S, an exposure level E, and a controllability level C, respectively.
The degree of controllability level indicates how much the driver of the vehicle can take active measures to avoid damage before the risk of lateral collision occurs, in relation to the length of reaction time between the driver receiving information that the vehicle is at risk and taking over the vehicle and initiating control of the vehicle (changing the vehicle from autonomous driving mode to driver driving mode). Thus, the degree of controllability class C and the reaction time t for the driver to take over the vehiclereactDetermining in a correlated manner: c ═ f (t)react) Wherein t isreactIs a human-averaged reaction time, which may be a hypothetical value obtained experimentally or in the literature, for example.
The degree of controllability C is typically derived from statistical analysis of the lateral crash incident records for the traffic management department or other relevant departments. The controllability grade C is generally divided into 4 grades C0, C1, C2 and C3, wherein the C0 controllability risk is lowest, namely the target vehicle is in a fully controllable state; the C3 controllability risk level is highest, i.e. the target vehicle is hardly controllable.
Severity level refers to the extent to which personnel, property, will be harmed in the risk of a lateral collision. The severity level at which a longitudinal vehicle collision occurs is primarily associated with the relative speed or acceleration between colliding vehicles, as well as a lateral collision. Thus, the severity level S can be expressed as: s ═ f (Δ v (t)).
For example, according to the ISO/DIS26262 standard, the severity grade S may be divided into four grades S0, S1, S2 and S3, wherein the S0 grade: the damage can not be caused, and the relative speed delta v (t) of the target vehicle and the transverse adjacent vehicle along the driving direction or the longitudinal direction is less than 5 km/h; rating of S1: causing mild injury, and the relative speed delta v (t) is between 5 and 20 km/h; rating of S2: can cause serious injury, and the relative speed delta v (t) is between 20 and 40 km/h; rating of S3: can cause fatal damage, and the relative speed delta v (t) is more than or equal to 40 km/h.
The exposure level E is the probability that a person or property may be affected or disturbed when a vehicle is at risk, and is generally divided into 5 levels, E0, E1, E2, E3 and E4, wherein the E0 exposure probability is the lowest, or almost impossible, to be exposed to the risk; e4 has the highest probability of exposure, i.e., exposure to danger.
In the present application, the determination of the exposure level E is performed based on real-time scene information of the traveling of the target vehicle. In particular, the present application provides an estimation model for determining an exposure level E based on real-time scene information. Therefore, real-time scene information of the target vehicle running needs to be acquired firstly.
The collection of real-time scene information for a target vehicle is described below in conjunction with FIG. 1.
Fig. 1 shows an autonomous vehicle O 'traveling synchronously with a target vehicle O located in front of it in the same state in the direction of travel D, i.e. the autonomous vehicle O' and the target vehicle O have the same real-time speed and real-time acceleration. For convenience of description, the running direction D is also referred to as a longitudinal direction in the present application, and a direction perpendicular to the running direction D in the page of fig. 1 is a lateral direction, that is, a vehicle width direction. In addition, in the present application, the "target vehicle" may be a virtual vehicle.
The first nearest vehicle located on a first side (e.g., an upper side in the drawing) in the lateral direction of the target vehicle O as viewed in the traveling direction D is the vehicle O2, and the second nearest vehicle located on a second side (e.g., a lower side in the drawing) opposite to the first side in the lateral direction of the target vehicle O as viewed in the traveling direction D is the vehicle O3. Herein, the "closest vehicle" means the shortest or closest distance in the traveling direction D from the target vehicle O, and herein, the distance between the two vehicles means the straight-line distance between the same designated position points on the two vehicles (for example, the midpoint of the rear axle of the vehicle in the illustrated embodiment, although any other designated position point on the vehicle may be used).
Sensors that sense real-time scene information may be disposed on the autonomous vehicle O'. The real-time scene information may include road type information, such as an expressway, an urban interior road, or an off-road; road surface condition information, such as ice and snow road surfaces, wet road surfaces, or normal road surfaces; vehicle travel information; as well as any other desired information. The vehicle travel information is relative and absolute parameter information of the target vehicle O traveling in synchronization with the autonomous vehicle O' and the first and second closest vehicles O2 and O3 laterally adjacent thereto.
These parameter information may include:
the real-time speed of each vehicle, including the real-time speed of the target vehicle O (including the longitudinal real-time speed and the lateral real-time speed, which are equal to the real-time speed of the autonomous vehicle O 'in the present embodiment, as described above), the real-time speeds of the first and second closest vehicles O2 and O3, respectively, from which the real-time relative speeds of the first and second closest vehicles O2 and O3 with respect to the target vehicle O' can be obtained, although the real-time relative speeds can be directly measured;
the real-time acceleration of each vehicle, including the real-time acceleration of the target vehicle O (including the longitudinal real-time acceleration and the lateral real-time acceleration, which are equal to the real-time acceleration of the autonomous vehicle O' in the present embodiment, as described above), the real-time acceleration of the first and second closest vehicles O2 and O3, respectively, may be calculated as necessary relative to the real-time acceleration of the target vehicle O; and
position information of the laterally adjacent first and second closest vehicles O2 and O3 with respect to the target vehicle O, which position information can be represented by coordinates of the above-described specified position points of the first and second closest vehicles O2 and O3 in the vehicle coordinate system. The vehicle coordinate system may be "fixed" to the autonomous vehicle O', and alternatively, in the embodiment as shown in fig. 1, the vehicle coordinate system may be "fixed" to the target vehicle O with the above-mentioned designated location point as the origin, the vehicle traveling direction being the longitudinal direction as the vertical axis, and the width (lateral) direction of the vehicle as the horizontal axis, where the position abscissas of the vehicles O2 and O3 are the lateral distances between the vehicles O2 and O3 and the target vehicle O, and the position ordinates of the vehicles O2 and O3 are the longitudinal distances between the vehicles O2 and O3 and the target vehicle O.
In addition to the above-described varying parameter information measured in real time as the target vehicle travels, the parameter information may include some preset fixed parameter information, for example, assuming that: the width of the vehicle is 2m, and half of the width of the vehicle is 1 m; the length of the vehicle is 4 m; the (longitudinal) distance between the autonomous vehicle 10 and the target vehicle 20 is 20m, etc.
First, the travel trajectories, particularly the travel trajectories in a future period of time, of the target vehicle O and the vehicles O2 and O3 are calculated in conjunction with the above-described changed parameter information (including the real-time relative or absolute velocity, the real-time relative or absolute acceleration, the real-time position, and the like) of the target vehicle O and the vehicles O2 and O3, and it is determined with which side of the laterally adjacent nearest vehicle (O2 or O3) the target vehicle O will possibly collide in the lateral direction, while defining the vehicle O2 or O2 that collides with the target vehicle O as a "dangerous vehicle". Finally, the velocity V and the lateral acceleration a at the time of collision of the target vehicle O with the dangerous vehicle are determined.
The principle of the present application for assessing the level of risk of a lateral collision between the subject vehicle O and the dangerous vehicle is described below.
First, the severity level S may be determined based on the relative speed Δ v (t) of the target vehicle O and the dangerous vehicle at the time of collision therebetween, which is calculated as described above. In this context, "speed" refers to speed in the vehicle travel direction D, "longitudinal acceleration" refers to acceleration in the vehicle travel direction D, and "lateral acceleration" refers to acceleration in the lateral direction.
Then, an exposure level E is determined based on the real-time parameters related to the target vehicle 20 and the dangerous vehicle in the parameter information and the determined severity level S, and the exposure level E can be determined by using the following estimation model of the present application:
in the above estimation model, E denotes an exposure rate level Sx (x is 0, 1, 2, or 3) to be determined denotes a previously determined severity level. The numerator ∑ c (Sx) represents the number of measurement cycles corresponding to the previously determined severity level Sx of all measurement cycles; the denominator Σ c (sall) represents the number of all measurement cycles, i.e. all measurement cycles corresponding to all severity levels. Here, one "measurement cycle" is one measurement or update of all measurement parameters.
Alternatively, the exposure level E may be obtained by a table lookup from experimental data statistics. Table 1 shows the statistics for a particular embodiment, which may be used to query the exposure level E.
In the embodiment of table 1, it is assumed that the target vehicle O is unexpectedly steered, causing the target vehicle O to make an accelerating motion at the assumed lateral acceleration change rate "Day", and to make a uniform accelerating motion after the lateral acceleration reaches the maximum value "ay _ max". The second to fourth rows of the table list the distance of the target vehicle O, the total travel time, in particular the high speed travel time at speeds above 60kph (this is intended to remove some of the low urban speed cases by means of the speed limit, since in the low speed cases the road conditions are often complex and difficult to make statistics by means of models); the fifth row in the table is the percentage of high speed time to total time; the sixth row to the eighth row are respectively the calculated time length with the collision risk, the percentage of the time length to the total running time length of the target vehicle O, and the percentage of the time length to the high-speed running time length; the last four rows in the table are respectively obtained from the calculated time length with collision risk, and assuming that the target vehicle O has lateral deviation at any time, different collision events (TTC) can be obtained due to different distances between the laterally adjacent vehicles as dangerous vehicles and the target vehicle O, all TTC are counted and classified according to the time (less than 1.5 s; 1.5s-2 s; 2s-3 s; more than 3s) of the collision between the target vehicle O and the dangerous vehicle (PO) to obtain percentage data of the exposure rate grade E in the table.
TABLE 1
According to the per-person reaction time t of the driverreactThe corresponding lateral collision risk occurrence probability can be looked up from the above table. For example, assume driver's per-person reaction time treact2.5s, the probability of a lateral collision of the vehicle, i.e., the exposure level E, corresponding to a different lateral acceleration Day can be looked up from the second last row of table 1.
If different exposure levels are defined as E0: more than or equal to 10 percent; e1: more than or equal to 1 percent; e2: not less than 0.1 percent; e3: and 0.01%, table 2 lists the evaluation results of the vehicle lateral collision risk levels corresponding to the respective severity levels S and exposure levels E with the controllability level fixed at the level C3.
TABLE 2
For example, in the case of the controllability grade of C3, the grade of the lateral collision risk of the vehicle is ASIL B when the severity grade is S1 and the exposure grade E is more than or equal to 10 percent (namely E0); when the severity grade is S1 and the exposure grade E is more than or equal to 1 percent (namely E1), the grade of the lateral collision risk of the vehicle is ASIL A; when the severity grade is S1 and the exposure rate grade E is more than or equal to 0.1 percent (namely E2), the grade of the vehicle transverse collision risk is ASIL QM (QM represents that no special functional safety flow is needed, and only normal quality management is needed); the risk level of the vehicle lateral collision is ASIL C when the severity level is S3 and the exposure level E is more than or equal to 1%.
FIG. 2 presents a flow chart of a method of assessing the level of risk of a lateral collision of a target vehicle.
In step S1, real-time scene information of the target vehicle traveling is collected, the real-time scene information including at least parameter information of the target vehicle and the laterally adjacent vehicles. As can be seen from the above, in the case where there are vehicles on both lateral sides of the target vehicle, this step S1 includes collecting parameter information of the nearest vehicles on both lateral sides of the target vehicle.
In step S2, a future travel track of each vehicle is calculated based on the above-described collected real-time scene information and a dangerous vehicle PO that will laterally collide with the target vehicle is determined, and parameter information at the time of the lateral collision of the target vehicle and the dangerous vehicle, including at least each vehicle speed and lateral acceleration at the time of the lateral collision. In this step, if there are adjacent vehicles on both lateral sides of the target vehicle, it is determined that the adjacent vehicle that will have a lateral collision with the target vehicle is the dangerous vehicle PO based on the future travel trajectories of the target vehicle and the two adjacent vehicles. If there is an adjacent vehicle on only one of the lateral sides of the subject vehicle, the adjacent vehicle is the dangerous vehicle PO.
In step S3, a severity level S is determined based on the relative lateral velocity between the target vehicle and the dangerous vehicle at the time of the lateral collision therebetween.
In step S4, an exposure level E is determined by means of an estimation model or by means of a look-up table based on the determined severity level S.
In step S5, the level of the risk of lateral collision of the target vehicle is derived based on the preset specific controllability level C and the previously determined severity level S and exposure level E.
In addition, according to the principles of the present application, all the sensors for measurement are disposed on the autonomous vehicle O ', and the autonomous vehicle O' performing data acquisition and subsequent data manipulation is located at a certain distance behind the virtual target vehicle O, and both have the same driving state, i.e., all the real-time parameter information is the same.
This arrangement is advantageous in that: the measurement of real-time scene information from a position at a distance behind the target vehicle O enables all necessary information to be measured to both lateral sides of the target vehicle O and behind the target vehicle O. That is, using the autonomous vehicle O' as an "observer" or measuring vehicle, various potential crash scenarios can be detected comprehensively, such as: a vehicle parallel to the target vehicle O; a vehicle behind the target vehicle O but faster than the target vehicle; a vehicle located in front of the target vehicle O but having a slower vehicle speed than the target vehicle. This is advantageous over placing the measurement sensors directly on the target vehicle O because if the measurement sensors are placed directly on the target vehicle O, real-time scene information of vehicles parallel to the target vehicle O (i.e., adjacent vehicles on both lateral sides) and vehicles behind the target vehicle O but approaching quickly is likely to be undetectable, which may cause a deviation in statistical results, thereby affecting the accuracy of risk level assessment. The application utilizes the autonomous vehicle O' to carry out measurement and calculation, well overcomes the defects, and provides a novel statistical method.
At least some of the steps of the methods of the present invention may be implemented using hardware and software, as well as a combination of both. When the method of the invention is implemented or partly implemented in software, the software may be used for performing the steps of the method of the invention. Software and data used in representing various elements may be stored in memory and executed by a suitable instruction execution system (microprocessor). The software may include an ordered listing of executable instructions for implementing logical functions, which can be embodied in any "processor-readable medium" for use by an instruction execution system, apparatus, or device (e.g., a single-or multi-core processor or processor-containing system). These systems will typically access these instructions from an instruction execution system, apparatus, or device and execute the instructions. In summary, the present application encompasses a readable storage medium having stored thereon executable instructions that, when executed, cause a machine to perform a method as in fig. 2.
Some or all of the steps of the above-described method may also be performed by an evaluation module 50 as shown in fig. 3, which evaluation module 50 may be provided separately or may share some or all of the components with other functional modules.
The evaluation module 50 may include a processor 52 and a memory 54 storing executable instructions and algorithms for the various computational steps. The memory stores executable instructions that, when executed, cause the processor to perform the method of fig. 2. The application also relates to a readable storage medium having stored thereon executable instructions that, when executed, cause a machine to perform a method as in fig. 2.
The present invention has been described in detail with reference to the specific embodiments. It is to be understood that both the foregoing description and the embodiments shown in the drawings are to be considered exemplary and not restrictive of the invention. It will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit of the invention, and these changes and modifications do not depart from the scope of the invention.
Claims (11)
1. A method of assessing a level of risk of a lateral collision of a target vehicle, comprising:
acquiring real-time scene information of the target vehicle in a state of running along a longitudinal direction and transversely adjacent vehicles adjacent in a transverse direction perpendicular to the longitudinal direction, wherein the real-time scene information comprises vehicle running parameter information;
calculating a future travel track of each vehicle based on the vehicle travel parameter information, determining a dangerous vehicle to be in lateral collision with the target vehicle, and travel parameter information of each vehicle when the target vehicle and the dangerous vehicle are in lateral collision;
determining a severity level of a lateral collision of a target vehicle based on traveling parameter information of the target vehicle when the target vehicle and a dangerous vehicle have the lateral collision;
estimating or querying from a list an exposure level of the target vehicle for a lateral collision based on the severity level, based on an estimation model; and
deriving the level of the target vehicle lateral collision risk based on the severity level, the exposure rate level, and an assumed specific controllability level.
2. The method of claim 1, wherein the vehicle driving parameter information includes at least the following real-time variability parameter information: a real-time velocity, a real-time relative acceleration, and a real-time position between the laterally adjacent vehicle and the target vehicle.
3. The method according to claim 2, wherein the position information of the laterally adjacent vehicle is expressed as its abscissa and ordinate in a vehicle coordinate system having a specified position point of the target vehicle as an origin of coordinates and the lateral and longitudinal directions as X and Y axes.
4. The method of claim 3, wherein the designated location point of the target vehicle is a midpoint of a length of a rear axle of the target vehicle in the width direction.
5. The method according to any one of claims 1-4, wherein the vehicle driving parameter information further comprises non-variable fixed parameter information, and/or the real-time scenario information further comprises road type information and road surface status information.
6. The method according to any one of claims 1-5, wherein the severity level of the lateral collision of the target vehicle is determined based on the lateral relative speed of the target vehicle and the hazardous vehicle at the time of the lateral collision.
7. The method according to any of claims 1-6, wherein the assumed specific controllability level is the highest controllability level representing almost uncontrollable.
8. The method of any of claims 1-7, wherein a sensor that senses the real-time scene information is disposed on an autonomous vehicle that is traveling in synchronization with the target vehicle.
9. The method of any of claims 1-8, wherein, where the exposure level is evaluated based on an estimation model, the estimation model is represented as:
wherein E represents the exposure level to be determined; sx (x is 0, 1, 2, or 3) represents the previously determined severity level. The numerator ∑ c (Sx) represents the number of measurement cycles corresponding to the previously determined severity level Sx of all measurement cycles; the denominator ∑ c (sall) represents the number of all measurement cycles.
10. A module for assessing a level of a target vehicle's risk of lateral collision, comprising:
a processor; and
a memory storing executable instructions that, when executed, cause the processor to perform the method of any of claims 1 to 9.
11. A readable storage medium having stored thereon executable instructions that, when executed, cause a machine to perform the method of any of claims 1 to 9.
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