CN113536949B - Accident risk level assessment method, device and computer readable storage medium - Google Patents
Accident risk level assessment method, device and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses an accident risk level assessment method, an accident risk level assessment device and a computer readable storage medium. The invention quantifies the damage degree of the accident to the vehicle, and enables the damage degree of the accident to the vehicle to be expressed by visual and objective dangerous grade data.
Description
Technical Field
The present invention relates to the field of vehicle technologies, and in particular, to an accident level assessment method, an accident level assessment device, and a computer readable storage medium.
Background
With the rapid development of the automobile industry and the improvement of the living standard of people, the average automobile possession is rapidly increased, but at the same time, the traffic accident safety problem is also more prominent.
Currently, the judgment of the dangerous degree of the traffic accident is generally subjective judgment based on intuition and experience of individuals, and the judgment depends on the accumulation of knowledge and experience of a judging subject and comprehensive capability, and is easily influenced by the physiology, capability and knowledge reserve of the judging subject.
Disclosure of Invention
The main object of the present invention is to provide an accident risk level assessment method, apparatus and computer readable storage medium, aiming at quantifying the accident risk level so that the accident risk level is presented in an intuitive and objective risk level.
In order to achieve the above object, the present invention provides an accident risk level assessment method comprising the steps of:
acquiring the speed and surrounding environment information of the vehicle, and determining the accident occurrence stage and accident location around the vehicle according to the surrounding environment information;
determining a corresponding preset dangerous degree according to the accident site to obtain an initial dangerous coefficient;
according to the accident occurrence stage and the speed of the vehicle, adjusting the initial risk coefficient according to preset adjustment parameters to obtain an adjusted risk coefficient;
and inquiring a risk level grading table according to the adjusted risk coefficient to obtain an accident risk level.
Preferably, the step of determining a corresponding preset risk degree according to the accident site, and obtaining an initial risk coefficient includes:
dividing the peripheral range of the vehicle according to a preset azimuth and a preset distance by taking the vehicle as a center point, wherein each region has a corresponding preset risk degree coefficient;
judging the dividing area where the accident site is located, and determining a corresponding preset risk degree coefficient according to the dividing area where the accident site is located to obtain an accident initial risk coefficient.
Preferably, the step of dividing the peripheral range of the host vehicle by taking the host vehicle as a center point according to a preset azimuth and distance further comprises:
the method comprises the steps of taking a vehicle as a center point, taking a vehicle head direction as a first direction, taking a vertical vehicle body direction as a second direction, establishing an azimuth coordinate system, and dividing the peripheral range of the vehicle into a first preset number of azimuth areas;
and dividing each azimuth area into a second preset number of distance areas according to the distance from the vehicle.
Preferably, the accident occurrence phase comprises: the method comprises the steps of adjusting an initial risk coefficient according to the accident occurrence stage and the speed of the vehicle and preset adjustment parameters to obtain an adjusted risk coefficient, wherein the steps comprise:
If the accident stage is that no accident occurs, adjusting the initial risk coefficient according to a preset first adjustment parameter to obtain a pending risk coefficient;
if the accident stage is that the accident has occurred, adjusting the initial risk coefficient according to a preset second adjustment parameter to obtain a pending risk coefficient;
and obtaining a corresponding third adjustment parameter according to the speed of the vehicle, and adjusting the undetermined risk coefficient according to the third adjustment parameter to obtain an adjusted risk coefficient.
Preferably, if the accident stage is that no accident has occurred, the step of adjusting the initial risk coefficient according to a preset first adjustment parameter to obtain the pending risk coefficient further includes:
if the accident stage is that no accident occurs, predicting accident occurrence probability according to surrounding environment information, and taking the accident occurrence probability as a scale factor to obtain a first adjustment parameter;
and adjusting the initial risk coefficient according to the first adjustment parameter to obtain a pending risk coefficient.
Preferably, if the accident stage is an accident, the step of adjusting the initial risk coefficient according to the preset second adjustment parameter to obtain the pending risk coefficient further includes:
if the accident stage is that the accident has occurred, adjusting the initial risk coefficient according to a preset second adjustment parameter to obtain an undetermined risk coefficient;
Judging whether the accident main body has motion change according to surrounding environment information;
and if the accident main body has motion change, adjusting the undetermined risk coefficient according to a preset fourth adjustment parameter to obtain an undetermined risk coefficient.
Preferably, the step of obtaining a corresponding third adjustment parameter according to the vehicle speed of the host vehicle, adjusting the undetermined risk coefficient according to the third adjustment parameter, and obtaining the adjusted risk coefficient further includes:
judging the position of the accident site relative to the local vehicle area;
if the accident site is located in the front area of the vehicle, a corresponding fifth adjustment parameter is obtained according to the speed of the vehicle, and the undetermined risk coefficient is adjusted according to the fifth adjustment parameter, so that an adjusted risk coefficient is obtained;
and if the accident site is positioned in the area behind the vehicle, acquiring a corresponding sixth adjustment parameter according to the speed of the vehicle, and adjusting the undetermined risk coefficient according to the sixth adjustment parameter to acquire an adjusted risk coefficient.
Preferably, the larger the speed of the vehicle is, the larger the value of the fifth adjustment parameter is; the larger the speed of the vehicle is, the smaller the value of the sixth adjustment parameter is.
In addition, in order to achieve the above object, the present invention also provides an accident risk level assessment apparatus comprising: the system comprises a memory, a processor and an accident risk level assessment program stored on the memory and capable of running on the processor, wherein the accident risk level assessment program realizes the steps of the accident risk level assessment method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon an accident risk level assessment program which, when executed by a processor, implements the steps of the accident risk level method as described above.
According to the accident risk level assessment method, the accident risk level assessment device and the computer readable storage medium, the accident occurrence stage and the accident location around the host vehicle are determined according to the surrounding environment information by acquiring the vehicle speed and the surrounding environment information of the host vehicle, the corresponding preset risk degree is determined according to the accident location, the initial risk coefficient is obtained, the initial risk coefficient is adjusted according to the preset adjustment parameters according to the accident occurrence stage and the vehicle speed, the adjusted risk coefficient is obtained, and the risk level setting table is inquired according to the adjusted risk coefficient, so that the accident risk level is obtained. By the method, the azimuth and the distance of the accident site relative to the vehicle are used as judging factors of the accident risk degree, the accident initial risk coefficient is firstly determined according to the surrounding area of the vehicle where the accident site is located, then the influence of the accident occurrence stage and the vehicle speed of the vehicle is considered, the initial risk coefficient is adjusted according to the accident occurrence stage and the vehicle speed of the vehicle to obtain the adjusted risk coefficient, and then a risk level grading table is inquired to obtain the accident risk level corresponding to the adjusted risk coefficient. The invention takes the accident location, the stage of the accident and the speed of the vehicle as the influence factors of the accident on the hazard degree of the vehicle, adjusts the initial hazard coefficient by determining the initial hazard coefficient, and then inquires the hazard degree grading table of the adjusted hazard coefficient, thereby quantifying the hazard degree of the accident on the vehicle, and enabling the hazard degree of the accident on the vehicle to be presented in hazard degree data, and the expression is more visual and objective.
Drawings
FIG. 1 is a schematic diagram of a device architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of an accident risk level assessment method according to the present invention;
FIG. 3 is a detailed flowchart of the step of determining a corresponding preset risk level according to the accident location to obtain an initial risk coefficient in the embodiment of the accident risk level assessment method of the present invention;
FIG. 4 is a schematic diagram of a division of a peripheral area of the host vehicle into thirty-two areas according to azimuth and distance, with the host vehicle as a center point;
FIG. 5 is a detailed flowchart of the step of adjusting the initial risk coefficient according to the accident occurrence stage and the vehicle speed according to the preset adjustment parameters to obtain the adjusted risk coefficient in the embodiment of the accident risk level evaluation method of the present invention;
fig. 6 is a detailed flow chart of the step of obtaining a corresponding third adjustment parameter according to the vehicle speed, and adjusting the undetermined risk coefficient according to the third adjustment parameter to obtain an adjusted risk coefficient in the embodiment of the accident risk level assessment method of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Since the current judgment of the dangerous degree of the traffic accident is generally subjective judgment based on intuition and experience of individuals, the judgment depends on the accumulation of knowledge and experience of a judging subject and comprehensive capability, and is easily influenced by the physiology, capability and knowledge reserve of the judging subject.
According to the accident risk level assessment method, device and computer readable storage medium, the vehicle speed and surrounding environment information of the vehicle are obtained, the accident occurrence stage and the accident location around the vehicle are determined according to the surrounding environment information, the corresponding preset risk degree is determined according to the accident location, the initial risk coefficient is obtained, the initial risk coefficient is adjusted according to the accident occurrence stage and the vehicle speed and preset adjustment parameters, the adjusted risk coefficient is obtained, and the risk level table is queried according to the adjusted risk coefficient, so that the accident risk level is obtained. According to the method, the initial risk coefficient of the accident is determined according to the surrounding area of the vehicle where the accident site is located, the damage degree of the accident to the vehicle due to the fact that the position and the distance of the accident site relative to the vehicle influence the accident is considered, then the initial risk coefficient is adjusted and corrected according to the accident occurrence stage and the speed of the vehicle to obtain the adjusted risk coefficient, and then a risk level grading table is queried to obtain the corresponding accident risk level. The invention takes the accident location, the stage of the accident and the speed of the vehicle as the influence factors of the accident on the hazard degree of the vehicle, adjusts the initial hazard coefficient by determining the initial hazard coefficient, and then inquires the hazard degree grading table of the adjusted hazard coefficient, thereby quantifying the hazard degree of the accident on the vehicle, and enabling the hazard degree of the accident on the vehicle to be presented in hazard degree data, and the expression is more visual and objective.
As shown in fig. 1, fig. 1 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a PC, or can be a mobile terminal device with a display function, such as a smart phone, a tablet personal computer, an electronic book reader, an MP3 (Moving Picture Experts Group Audio Layer III, dynamic image expert compression standard audio layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, dynamic image expert compression standard audio layer 4) player, a portable computer and the like.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Preferably, the terminal may further include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. Among other sensors, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal moves to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile terminal is stationary, and the mobile terminal can be used for recognizing the gesture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, and the like, which are not described herein.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 1 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and an accident risk level assessment program may be included in a memory 1005 as one type of computer storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call the accident risk level assessment program stored in the memory 1005 and perform the following operations:
acquiring the speed and surrounding environment information of the vehicle, and determining the accident occurrence stage and accident location around the vehicle according to the surrounding environment information;
determining a corresponding preset dangerous degree according to the accident site to obtain an initial dangerous coefficient;
according to the accident occurrence stage and the speed of the vehicle, adjusting the initial risk coefficient according to preset adjustment parameters to obtain an adjusted risk coefficient;
and inquiring a risk level grading table according to the adjusted risk coefficient to obtain an accident risk level.
Further, the processor 1001 may call the accident risk level assessment program stored in the memory 1005, and further perform the following operations:
Dividing the peripheral range of the vehicle according to a preset azimuth and a preset distance by taking the vehicle as a center point, wherein each region has a corresponding preset risk degree coefficient;
judging the dividing area where the accident site is located, and determining a corresponding preset risk degree coefficient according to the dividing area where the accident site is located to obtain an accident initial risk coefficient.
Further, the processor 1001 may call the accident risk level assessment program stored in the memory 1005, and further perform the following operations:
the method comprises the steps of taking a vehicle as a center point, taking a vehicle head direction as a first direction, taking a vertical vehicle body direction as a second direction, establishing an azimuth coordinate system, and dividing the peripheral range of the vehicle into a first preset number of azimuth areas;
and dividing each azimuth area into a second preset number of distance areas according to the distance from the vehicle.
Further, the accident occurrence stage includes: the processor 1001 may call the accident risk level assessment program stored in the memory 1005, and also perform the following operations:
if the accident stage is that no accident occurs, adjusting the initial risk coefficient according to a preset first adjustment parameter to obtain a pending risk coefficient;
if the accident stage is that the accident has occurred, adjusting the initial risk coefficient according to a preset second adjustment parameter to obtain a pending risk coefficient;
And obtaining a corresponding third adjustment parameter according to the speed of the vehicle, and adjusting the undetermined risk coefficient according to the third adjustment parameter to obtain an adjusted risk coefficient.
Further, the processor 1001 may call the accident risk level assessment program stored in the memory 1005, and further perform the following operations:
if the accident stage is that no accident occurs, predicting accident occurrence probability according to surrounding environment information, and taking the accident occurrence probability as a scale factor to obtain a first adjustment parameter;
and adjusting the initial risk coefficient according to the first adjustment parameter to obtain a pending risk coefficient.
Further, the processor 1001 may call the accident risk level assessment program stored in the memory 1005, and further perform the following operations:
if the accident stage is that the accident has occurred, adjusting the initial risk coefficient according to a preset second adjustment parameter to obtain an undetermined risk coefficient;
judging whether the accident main body has motion change according to surrounding environment information;
and if the accident main body has motion change, adjusting the undetermined risk coefficient according to a preset fourth adjustment parameter to obtain an undetermined risk coefficient.
Further, the processor 1001 may call the accident risk level assessment program stored in the memory 1005, and further perform the following operations:
Judging the position of the accident site relative to the local vehicle area;
if the accident site is located in the front area of the vehicle, a corresponding fifth adjustment parameter is obtained according to the speed of the vehicle, and the undetermined risk coefficient is adjusted according to the fifth adjustment parameter, so that an adjusted risk coefficient is obtained;
and if the accident site is positioned in the area behind the vehicle, acquiring a corresponding sixth adjustment parameter according to the speed of the vehicle, and adjusting the undetermined risk coefficient according to the sixth adjustment parameter to acquire an adjusted risk coefficient.
Further, the processor 1001 may call the accident risk level assessment program stored in the memory 1005, and further perform the following operations:
the larger the speed of the vehicle is, the larger the value of the fifth adjusting parameter is;
the larger the speed of the vehicle is, the smaller the value of the sixth adjustment parameter is.
The specific embodiment of the accident risk level assessment device of the present invention is substantially the same as the embodiments of the accident risk level assessment method described below, and will not be described herein.
Referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of an accident risk level assessment method according to the present invention, where the accident risk level assessment method includes:
step S100, acquiring the speed of the vehicle and surrounding environment information, and determining the accident occurrence stage and the accident location around the vehicle according to the surrounding environment information;
The surrounding environment information includes: other vehicles, pedestrians and obstacles' locations, sizes, directions of movement, speeds, relative vehicle distances, etc. The mode of acquiring the surrounding environment information can be through an intelligent network traffic system or through one or more vehicle-mounted sensors, wherein the vehicle-mounted sensors can be one or more of millimeter wave radar, laser radar, ultrasonic radar, visual camera sensor, infrared sensor, panoramic 360 sensor and the like. The accident can be before, during and after the accident, or only the stage of the accident is considered. The accident site comprises: accident sites that have not yet occurred but have a potential risk of occurrence, accident sites that are occurring and have occurred, and accident sites that have occurred but have been predicted to occur secondary accidents, i.e., accident sites include actual accident sites and predicted accident sites. The method for determining the accident occurrence stage and the accident site around the host vehicle according to the surrounding environment information comprises the following steps: the method comprises the steps of acquiring surrounding vehicle, person and other obstacle movement object data and form information, and analyzing and predicting accident occurrence stages and accident places around the vehicle through an algorithm, wherein the algorithm can be a neural network algorithm, collecting a large number of real accident data to establish a database, judging current environment conditions and accident data in the database through the neural network algorithm, predicting the current accident information, training the algorithm based on the database, gradually enhancing the algorithm capability and optimizing the algorithm, and in a specific embodiment, adopting a Bayesian algorithm, a gray Markov chain prediction method or an SVM (Support Vector Machine ) as a prediction algorithm. Video and images of the surrounding environment can also be directly analyzed and acquired for incidents that have occurred and are occurring, thereby determining the location and current stage of the incident.
The hazard degree of different traffic accidents on the vehicle is obviously different, and the embodiment of the invention takes the important and easily obtained influence factors which are relatively convenient to calculate as the priority, and in the embodiment of the invention, the direction of the accident point relative to the vehicle, the distance between the accident point and the vehicle, the stage of the accident and the vehicle speed are taken as the reference basis for evaluating the accident hazard level, firstly, the hazard degree of the accident on the vehicle is evaluated through the region of the accident point, and then the accident hazard degree is corrected and adjusted according to the accident occurrence stage and the vehicle speed, thereby obtaining the accident hazard level. In a specific embodiment, a person skilled in the art may also select other reference factors to evaluate the accident risk level, for example, the accident risk level corresponding to the quality map of the accident subject may be mapped, where the accident subject is a heavy truck having a higher hazard level than the accident subject is a bicycle.
Step S200, determining a corresponding preset risk degree according to the accident site to obtain an initial risk coefficient;
the method for determining the corresponding preset dangerous degree according to the accident site and obtaining the initial dangerous coefficient can be as follows: setting a corresponding dangerous degree for the distance between the accident point and the vehicle, for example, when the distance between the accident point and the vehicle is smaller than 50 meters, the corresponding dangerous degree is 0.9, when the distance between the accident point and the vehicle is larger than 50 meters and smaller than 100 meters, the corresponding dangerous degree is 0.7, when the distance between the accident point and the vehicle is larger than 100 meters, the corresponding dangerous degree is 0.5, and the distance between the accident point and the vehicle is obtained according to the accident point, so that the corresponding preset dangerous degree is determined, and the initial dangerous coefficient is obtained. Considering that the accident has different distances and directions from the vehicle, the corresponding accident risk degrees are different, and generally, the closer the accident site is to the vehicle, the higher the hazard degree to the vehicle is, and the higher the accident risk level is; the accident site is located in front of the traveling direction of the host vehicle and has a higher hazard level than the accident site is located behind the traveling direction of the host vehicle. In this embodiment, the peripheral area of the host vehicle is divided according to the azimuth and the distance by using the host vehicle as a reference point, and corresponding dangerous degrees are preset in each divided area, accident occurrence point information is obtained in step S100, the divided area where the accident location is located relative to the host vehicle is determined, and the dangerous degree corresponding to the located divided area is determined, so that an initial dangerous degree coefficient of the accident to the host vehicle is obtained. The dividing area where the accident is located represents the position of the accident site relative to the area where the accident is located by taking the vehicle as a reference point.
Step S300, according to the accident occurrence stage and the speed of the vehicle, adjusting the initial risk coefficient according to preset adjustment parameters to obtain an adjusted risk coefficient;
the damage degree of the accident to the vehicle is different between the accident and the accident which does not occur but is at risk, and the speed of the vehicle also affects the damage degree of the accident to the vehicle. In the step, the accident occurrence stage and the vehicle speed of the vehicle are taken as influencing factors of the accident risk degree, and the initial risk coefficient is adjusted according to the accident occurrence stage and the vehicle speed of the vehicle. The accident occurrence stage comprises the accident not occurring and the accident occurring, each occurrence stage is preset with corresponding adjustment parameters, the initial risk coefficient obtained in the step S200 is adjusted according to the adjustment parameters corresponding to the occurrence stage, the speed of the vehicle is considered, and the vehicle is adjusted according to the adjustment parameters corresponding to different speeds, so that the risk coefficient adjusted by the accident occurrence stage and the speed adjustment parameters of the vehicle is obtained.
And step S400, inquiring a risk level grading table according to the adjusted risk coefficient to obtain an accident risk level.
In the embodiment of the invention, a risk level grading table is arranged, and accident risk levels corresponding to the risk coefficients are regulated in the risk level grading table. And searching the corresponding risk level in the risk level grading table according to the regulated risk coefficient calculated in the previous step to obtain the accident risk level. Specifically, the hazard classes include hazard class 0 (no hazard), hazard class 1, hazard class 2, hazard class 3, with a larger number indicating a higher hazard class and an accident indicating a higher hazard level to the host vehicle. Further, a risk factor of [0,0.25 ] is defined as a risk level 0, a risk factor of [0.25, 0.5) as a risk level 1, a risk factor of [0.5, 0.75) as a risk level 2, a risk factor of [0.75,1] as a risk level 3, for example, a risk factor of 0.6 is obtained after step S300, falling within the range of 0.5 to 0.175, indicating that the risk level is 2. In a specific embodiment, other risk level grading tables can be defined to reasonably conform to the actual situation, or the risk level grading tables can be directly expressed by risk coefficients without being defined.
According to the accident risk level assessment method, the accident site, the stage where the accident is located and the speed of the vehicle are taken as influencing factors of the accident on the hazard degree of the vehicle, the accident occurrence stage and the accident site around the vehicle are determined according to the surrounding environment information by acquiring the speed of the vehicle and the surrounding environment information, the accident initial risk coefficient is determined according to the surrounding area of the vehicle where the accident site is located, then the initial risk coefficient is adjusted and corrected according to the accident occurrence stage and the speed of the vehicle to obtain the adjusted risk coefficient, then a risk level table is queried to obtain the corresponding accident risk level, the hazard degree of the accident on the vehicle is quantized, and the hazard degree of the accident on the vehicle is expressed in visual and objective risk level data.
Further, referring to fig. 3, fig. 3 is a detailed flow chart of the step of determining a corresponding preset risk level according to the accident location to obtain an initial risk coefficient in the embodiment of the accident risk level assessment method of the present invention, based on the embodiment shown in fig. 2, step S200 may include:
step S210, dividing the peripheral range of the vehicle according to a preset azimuth and a preset distance by taking the vehicle as a center point, wherein each region has a corresponding preset risk degree coefficient;
The accident site area takes the host vehicle as a reference, and comprises the distance and azimuth characteristics relative to the host vehicle. In the step, the vehicle is used as a central origin, the surrounding area of the vehicle is divided according to the distance and the azimuth without considering the shape and the size of the vehicle or the surrounding topography and topography, and each divided area has a corresponding risk degree coefficient, so that the risk degree coefficient is obtained according to the divided area where the accident site is located. The area division mode can be square checkerboard format or irregular shape, so as to meet the actual needs and achieve reasonable and effective area division. In the embodiment of the invention, the surrounding area of the host vehicle is divided into a first preset number of azimuth areas by taking the host vehicle as a center point, taking the vehicle head direction as a first direction and taking the vehicle body direction as a second direction, and then dividing each azimuth area into a second preset number of distance areas according to the distance from the host vehicle. Further, as a specific embodiment, the vehicle is taken as a center point, the vehicle head direction is taken as a first direction, the vehicle body direction is taken as a second direction, an azimuth coordinate system is established, the peripheral range of the vehicle is divided into eight sector azimuth areas of front, rear, left, right, front left, rear right, front right and rear right according to azimuth, each sector azimuth area is divided into four parts according to three distances from the vehicle, the vehicle is taken as a center point, and the peripheral range of the vehicle is divided into thirty-two areas according to azimuth and distance. Referring specifically to fig. 4, fig. 4 is a schematic diagram of dividing the peripheral area of the host vehicle into thirty-two areas according to the azimuth and the distance, wherein the peripheral area of the host vehicle is divided into eight parts according to the azimuth, namely, the front area, the rear area, the left area, the right area, the front area, the left area, the front area and the right area, according to the azimuth, and then divided into three sections according to the distance between the front area and the near area, wherein the selected dividing distance is 10 meters, 25 meters and 60 meters from the host vehicle. In a specific embodiment, other partition orientations and partition distances can be selected for partitioning, and other forms of partitioning modes can be selected.
Step S220, judging a division area where the accident site is located, and determining a corresponding preset risk degree coefficient according to the division area where the accident site is located to obtain an accident initial risk coefficient.
In step S210, each divided area corresponds to only one risk level coefficient, and the risk level coefficients of different divided areas may be different or the same. Specifically, the greater the risk level coefficient of the area closer to the host vehicle, the smaller the risk level coefficient of the area farther from the host vehicle, and the greater the risk level coefficient of the area located in front of the host vehicle, and the smaller the risk level coefficient of the area located behind the host vehicle. As a specific embodiment, the preset risk level coefficient may be: the higher the hazard level coefficient is, the greater the hazard of the accident to the vehicle is represented by the class A (hazard coefficient 0.8), the class B (hazard coefficient 0.6), the class C (hazard coefficient 0.4) and the class D (hazard coefficient 0.2). Referring to fig. 4, for an embodiment of dividing the thirty-two areas in step S210, fig. 4 also shows a preset risk level coefficient corresponding thereto. Particularly, if the accident site is just on the boundary of the regional division or there are multiple accident sites, and the accident sites are distributed in multiple division regions, the region with the largest risk degree coefficient in the relevant region is selected as the accident region in the embodiment of the method.
According to the embodiment of the invention, the host vehicle is taken as an origin, the peripheral area of the host vehicle is divided, the damage degree of the accident to the host vehicle is evaluated in the distance and azimuth from the accident location to the host vehicle, and the initial accident risk coefficient is obtained according to the fact that the accident location is positioned in the divided area around the host vehicle.
Further, referring to fig. 5, fig. 5 is a detailed flowchart of a step of adjusting an initial risk coefficient according to a preset adjustment parameter according to the accident occurrence stage and the vehicle speed in the embodiment of the accident risk level evaluation method of the present invention, to obtain an adjusted risk coefficient, based on the embodiment shown in fig. 2, step S300 may include:
step S310, if the accident stage is that no accident has occurred, the initial risk coefficient is adjusted according to a preset first adjustment parameter, and a pending risk coefficient is obtained;
step S320, if the accident stage is the occurrence, the initial risk coefficient is adjusted according to the preset second adjustment parameter to obtain a pending risk coefficient;
step S330, obtaining a corresponding third adjustment parameter according to the speed of the vehicle, and adjusting the undetermined risk coefficient according to the third adjustment parameter to obtain an adjusted risk coefficient.
The degree of harm of different accidents in the accident occurrence stage to the vehicle is different, and parameters need to be discussed and adjusted according to different stage conditions. The speed of the vehicle is also one of the factors affecting the degree of damage of the accident to the vehicle. In this embodiment, the accident occurrence stage is divided into an accident that has not occurred and an accident that has occurred, and corresponding adjustment parameters are set for the accident that has not occurred and the accident that has occurred, respectively. For example, in a specific embodiment, the first adjustment parameter for an accident that has not occurred is 0.8, and the second adjustment parameter for an accident that has occurred is 1, and the adjustment is performed on the initial risk coefficient according to the accident occurrence stage in a scale factor manner, so as to obtain the pending risk coefficient. Further, for the accident stage which does not occur yet, the accident estimated occurrence probability can be judged according to the surrounding environment information, and the initial risk coefficient is adjusted by taking the accident occurrence probability as a scale factor. The accident prediction occurrence probability can be judged by acquiring surrounding vehicle, human and other obstacle moving object data and predicting through an algorithm, wherein the algorithm can be a traffic accident probability prediction method based on a Bayesian network, and the accident probability can be predicted by establishing big data and performing deep learning. In a specific embodiment, only high probability accidents are considered, for example, when the accident occurrence probability is higher than 80%, the initial risk coefficient is adjusted by taking the occurrence probability, and for the accidents with the predicted occurrence probability lower than 80%, the accidents are considered to be accidents, the accidents are not considered, the accident probability is 0, and the adjustment parameter is 0. Further, for an accident that has occurred, there is a possibility that a secondary accident may occur, and whether the accident subject has a motion change is determined according to surrounding environment information. If the accident main body does not move or move after the accident occurs, the position does not move or the speed does not change, and the possibility of the secondary accident is very small, the undetermined danger coefficient is not required to be continuously adjusted after being adjusted according to the second adjusting parameter. If the acquired surrounding environment information shows that the accident main body has motion change after the accident occurs, the undetermined risk coefficient is adjusted according to the second adjustment parameter, and then the undetermined risk coefficient is further obtained by continuing to adjust according to the fourth adjustment parameter. Typically, the fourth adjustment parameter here takes 1.2. In a specific embodiment, each adjustment parameter may also be other values. And then, according to preset adjustment parameters corresponding to the speed of the vehicle, the undetermined danger coefficient after the adjustment of the accident occurrence stage is adjusted. In a specific embodiment, the vehicle speed (unit km/h) of the host vehicle can be used as a preset adjustment parameter corresponding to the vehicle speed of the host vehicle, and the undetermined risk coefficient can be adjusted in a scale factor mode.
In the implementation, the accident occurrence stage and the speed of the vehicle are used as influence factors of the accident on the hazard degree of the vehicle, and the initial risk coefficient is adjusted by selecting proper adjustment parameters according to the difference of the accident occurrence stage and the speed of the vehicle, so that the adjusted risk coefficient is obtained.
Further, referring to fig. 6, fig. 6 is a detailed flow chart of the step of obtaining a corresponding third adjustment parameter according to the vehicle speed of the vehicle and adjusting the undetermined risk coefficient according to the third adjustment parameter to obtain an adjusted risk coefficient according to the embodiment of the accident risk level assessment method of the present invention, based on the embodiment shown in fig. 5, step S330 may include:
step S331, judging the position of an accident site relative to the area of the vehicle;
the speed of the own vehicle is one of factors influencing the damage degree of the accident to the own vehicle, but the larger the speed of the own vehicle is for the traffic accident occurring in front of the own vehicle, the larger the damage influence of the accident is, and the larger the speed of the own vehicle is for the traffic accident occurring behind the own vehicle, the smaller the damage influence of the accident is. In the embodiment of the invention, the undetermined danger coefficient is adjusted according to the speed of the vehicle, and the influence of the speed of the vehicle on the accident danger level is different in consideration of the fact that the speed of the vehicle is the same but the accident occurs at different positions, so that the influence of the speed of the vehicle on the degree of the damage of the vehicle is discussed according to conditions. The accident site is here the accident site after the accident phase judgment, that is to say the accident site comprises: accident sites and predicted accident sites are occurring. Further, for the predicted accident site, the accident site includes a predicted accident occurrence position and a predicted accident subject movement track within a prescribed time limit. The accident site can be obtained by directly analyzing the acquired video and image of the surrounding environment according to the surrounding environment information, or can be obtained by acquiring surrounding vehicle, person and other obstacle animal data and predicting the accident site through an algorithm, wherein the algorithm can be a linear regression prediction method, a nonlinear regression prediction method, a gray Markov chain prediction method, a traffic accident Bayesian prediction method based on the vehicle speed or a gray correlation analysis method of road traffic accidents, and the like.
Step S332, if the accident site is located in the front area of the host vehicle, obtaining a corresponding fifth adjustment parameter according to the speed of the host vehicle, and adjusting the undetermined risk coefficient according to the fifth adjustment parameter to obtain an adjusted risk coefficient;
the driving direction of the vehicle is forward, and the area of the accident site in front of the transverse central line of the vehicle is the front area of the vehicle. If the accident position is located in the front area of the vehicle, and if one of the predicted accident position and the motion track of the accident main body within the predicted specified time limit is located in the front area of the vehicle, the fifth adjusting parameter is taken as a scaling factor to adjust the to-be-determined risk coefficient.
The fifth adjustment parameters correspond to the vehicle speed of the vehicle, each vehicle speed is provided with the corresponding fifth adjustment parameters, and the larger the vehicle speed is, the larger the value of the fifth adjustment parameters is. Further, an upper and lower threshold may be provided to limit the range of values of the fifth adjustment parameter. Specifically, when the speed of the vehicle is less than 60km/h, defining a corresponding fifth adjustment parameter as 1; when the speed of the vehicle is 60-100 km/h, the corresponding fifth adjustment parameter is increased along with the increase of the speed, interpolation is carried out in 1-1.2, namely, the speed of the vehicle is 0.7+0.005; when the speed of the vehicle is greater than 100km/h, the corresponding fifth adjustment parameter is 1.2. In a specific embodiment, the fifth adjustment parameter may also be another value.
Step S333, if the accident site is located in the area behind the host vehicle, obtaining a corresponding sixth adjustment parameter according to the speed of the host vehicle, and adjusting the undetermined risk coefficient according to the sixth adjustment parameter to obtain an adjusted risk coefficient;
the driving direction of the vehicle is forward, and the area of the accident site behind the transverse central line of the vehicle is the area behind the vehicle. And if the accident position is positioned in the area behind the vehicle and the accident main body movement track is positioned in the area behind the vehicle within the predicted specified time limit for the predicted accident, the situation that the accident place is positioned in the area behind the vehicle is met, and the sixth adjustment parameter is taken as a scale factor to adjust the to-be-determined risk coefficient.
The sixth adjustment parameters correspond to the vehicle speeds of the vehicles, each vehicle speed is provided with the corresponding sixth adjustment parameter, and the larger the vehicle speed is, the smaller the value of the sixth adjustment parameter is. Further, an upper and lower threshold may be provided to limit the range of values of the sixth adjustment parameter. Specifically, when the speed of the vehicle is less than 60km/h, defining a corresponding sixth adjustment parameter as 1; when the speed of the vehicle is 60-100 km/h, the corresponding sixth adjustment parameter is reduced along with the increase of the speed, interpolation is carried out in 1-0.8, namely, the speed of the vehicle is 1.3-0.005; when the speed of the vehicle is greater than 100km/h, the corresponding sixth adjustment parameter is 0.8. In a specific embodiment, the sixth adjustment parameter may also be another value.
The speed of the vehicle influences the hazard level of the accident to the vehicle, but the influence of the speed of the vehicle is different according to the position of the accident. According to the embodiment of the invention, according to different accident occurrence positions, the undetermined dangerous coefficient is adjusted by selecting the adjusting parameters corresponding to the vehicle speed according to different conditions, and the adjusted dangerous coefficient is obtained.
In addition, the embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores an accident risk level assessment program, and the accident risk level assessment program realizes the steps of the accident risk level assessment method when being executed by a processor.
The specific embodiments of the computer readable storage medium of the present invention are substantially the same as the embodiments of the accident risk level assessment method described above, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (9)
1. An accident risk level assessment method, characterized in that the accident risk level assessment method comprises the following steps:
acquiring the speed and surrounding environment information of the vehicle, and determining the accident occurrence stage and accident location around the vehicle according to the surrounding environment information;
determining a corresponding preset dangerous degree according to the accident site to obtain an initial dangerous coefficient;
according to the accident occurrence stage and the speed of the vehicle, adjusting an initial risk coefficient according to preset adjustment parameters, wherein the accident occurrence stage comprises that the accident does not occur yet and the accident occurs already;
if the accident stage is that no accident occurs, adjusting the initial risk coefficient according to a preset first adjustment parameter to obtain a pending risk coefficient;
if the accident stage is that the accident has occurred, adjusting the initial risk coefficient according to a preset second adjustment parameter to obtain a pending risk coefficient;
obtaining a corresponding third adjustment parameter according to the speed of the vehicle, and adjusting the undetermined risk coefficient according to the third adjustment parameter to obtain an adjusted risk coefficient;
and inquiring a risk level grading table according to the adjusted risk coefficient to obtain an accident risk level.
2. The method for assessing the risk level of an accident according to claim 1, wherein said step of determining the corresponding preset risk level from said accident site to obtain an initial risk factor comprises:
dividing the peripheral range of the vehicle according to a preset azimuth and a preset distance by taking the vehicle as a center point, wherein each region has a corresponding preset risk degree coefficient;
judging the dividing area where the accident site is located, and determining a corresponding preset risk degree coefficient according to the dividing area where the accident site is located to obtain an accident initial risk coefficient.
3. The method for evaluating an accident risk level according to claim 2, wherein the step of dividing the peripheral area of the host vehicle by a predetermined azimuth and distance with the host vehicle as a center point further comprises:
the method comprises the steps of taking a vehicle as a center point, taking a vehicle head direction as a first direction, taking a vertical vehicle body direction as a second direction, establishing an azimuth coordinate system, and dividing the peripheral range of the vehicle into a first preset number of azimuth areas;
and dividing each azimuth area into a second preset number of distance areas according to the distance from the vehicle.
4. The method of claim 1, wherein if the accident phase is that no accident has occurred, the step of adjusting the initial risk factor according to a preset first adjustment parameter to obtain the pending risk factor further comprises:
If the accident stage is that no accident occurs, predicting accident occurrence probability according to surrounding environment information, and taking the accident occurrence probability as a scale factor to obtain a first adjustment parameter;
and adjusting the initial risk coefficient according to the first adjustment parameter to obtain a pending risk coefficient.
5. The method of claim 1, wherein if the accident phase is that an accident has occurred, the step of adjusting the initial risk factor according to a preset second adjustment parameter to obtain the pending risk factor further comprises:
if the accident stage is that the accident has occurred, adjusting the initial risk coefficient according to a preset second adjustment parameter to obtain an undetermined risk coefficient;
judging whether the accident main body has motion change according to surrounding environment information;
and if the accident main body has motion change, adjusting the undetermined risk coefficient according to a preset fourth adjustment parameter to obtain an undetermined risk coefficient.
6. The method for evaluating the accident risk level according to claim 1, wherein the step of obtaining the corresponding third adjustment parameter according to the vehicle speed, adjusting the risk coefficient to be determined according to the third adjustment parameter, and obtaining the risk coefficient after adjustment further comprises:
Judging the position of the accident site relative to the local vehicle area;
if the accident site is located in the front area of the vehicle, a corresponding fifth adjustment parameter is obtained according to the speed of the vehicle, and the undetermined risk coefficient is adjusted according to the fifth adjustment parameter, so that an adjusted risk coefficient is obtained;
and if the accident site is positioned in the area behind the vehicle, acquiring a corresponding sixth adjustment parameter according to the speed of the vehicle, and adjusting the undetermined risk coefficient according to the sixth adjustment parameter to acquire an adjusted risk coefficient.
7. The accident risk level assessment method according to claim 6, wherein:
the larger the speed of the vehicle is, the larger the value of the fifth adjusting parameter is;
the larger the speed of the vehicle is, the smaller the value of the sixth adjustment parameter is.
8. An accident risk level assessment apparatus, characterized in that the accident risk level assessment apparatus comprises: a memory, a processor and an accident risk level assessment program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the accident risk level assessment method according to any one of claims 1 to 7.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon an accident risk level assessment program which, when executed by a processor, implements the steps of the accident risk level assessment method according to any one of claims 1 to 7.
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| CN111815986A (en) * | 2020-09-02 | 2020-10-23 | 深圳市城市交通规划设计研究中心股份有限公司 | Traffic accident early warning method and device, terminal equipment and storage medium |
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| CN108154681B (en) * | 2016-12-06 | 2020-11-20 | 杭州海康威视数字技术股份有限公司 | Method, device and system for predicting risk of traffic accident |
| US20210403051A1 (en) * | 2019-06-05 | 2021-12-30 | Lg Electronics Inc. | Method for controlling autonomous vehicle |
| CN111873993A (en) * | 2020-07-07 | 2020-11-03 | 上海万位科技有限公司 | Vehicle driving risk early warning method based on big data |
| CN112937520B (en) * | 2021-03-15 | 2022-07-19 | 东风柳州汽车有限公司 | Emergency braking method and device for vehicle, commercial vehicle and storage medium |
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| CN109389824A (en) * | 2017-08-04 | 2019-02-26 | 华为技术有限公司 | A kind of appraisal procedure and device driving risk |
| CN111815986A (en) * | 2020-09-02 | 2020-10-23 | 深圳市城市交通规划设计研究中心股份有限公司 | Traffic accident early warning method and device, terminal equipment and storage medium |
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