CN116720356A - Design method of active safety module of vehicle based on accident damage prediction of cyclist - Google Patents
Design method of active safety module of vehicle based on accident damage prediction of cyclist Download PDFInfo
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Abstract
The application belongs to the technical field of vehicle safety, and particularly relates to a vehicle active safety module design method based on accident damage prediction of a cyclist, which comprises the following steps: firstly, acquiring shape parameters of the front end of a vehicle, and importing the shape parameters into an information processing system; the vehicle detects and acquires scene data and real-time vehicle speed through a ranging system and an acquisition module, and the scene data and the real-time vehicle speed are imported into an information processing system; then the information processing system judges the braking of the vehicle according to the real-time vehicle speed, scene data and a preset vehicle braking algorithm, and if the vehicle cannot be braked, the information is sent to the damage prediction module; the damage prediction module processes the information processing result, the scene data and the shape parameters according to a preset decision tree model, generates an optimal collision strategy of a rider under the scene, and sends the optimal collision strategy to the vehicle control system for implementing control of the optimal collision strategy. The application can reduce the damage of the vehicle to the cyclist in the traffic accident to the greatest extent.
Description
Technical Field
The application belongs to the technical field of vehicle safety, and particularly relates to a design method of a vehicle active safety module based on accident damage prediction of a cyclist.
Background
The active safety system of the current vehicle mainly comprises an ABS system, an EBD system, an AEB system, a lane keeping system, a vehicle distance control system and the like; when facing unavoidable accidents, the only active safety measure that the vehicle can implement is the intervention of the AEB system, and the vehicle is braked emergently; the AEB system mainly comprises a driving environment information acquisition unit, an electronic control unit and an execution unit, and can actively brake when a vehicle encounters an emergency dangerous situation or the distance between the vehicle and a front vehicle or the distance between the vehicle and a pedestrian is smaller than a safe distance, so that collision accidents are avoided or reduced.
The current AEB system can reduce damage caused by accidents, but according to the current data and research, instead of the lower the speed of the vehicle, the lower the damage degree of the cyclist of the two-wheeled vehicle, the optimal collision speed range exists, and the front cover height of the vehicle in collision and the collision angle with the two-wheeled vehicle greatly influence the damage of the cyclist in the accidents, so that in the accidents, the vehicle has the potential to do more so as to reduce the damage to the cyclist.
Disclosure of Invention
The technical problem solved by the application is to provide a design method of a vehicle active safety module based on the accident damage prediction of a rider, so that the damage of the vehicle to the rider in a traffic accident can be reduced to the greatest extent.
The basic scheme provided by the application is as follows: the design method of the active safety module of the vehicle based on the accident damage prediction of the cyclist comprises the following steps:
s1: after the modeling structure of the vehicle is confirmed, the shape parameters of the front end of the vehicle are acquired and are imported into an information processing system;
s2: when the vehicle detects that a front rider is within a preset distance through a distance measuring system, scene data and real-time vehicle speed are acquired through an acquisition module, and the scene data and the real-time vehicle speed are led into an information processing system;
s3: the information processing system judges whether braking can be performed or not according to the real-time vehicle speed, the scene data and a preset vehicle braking algorithm, and generates an information processing result; if the information processing result is that braking can be performed, the information processing system sends information to a vehicle braking system to perform emergency braking; if the information processing result is that stopping cannot be performed, the information is sent to a damage prediction module;
s4: the damage prediction module is used for processing the information processing result, the scene data and the shape parameters according to the preset decision tree model, generating an optimal collision strategy of a rider in the scene, and sending the optimal collision strategy to the vehicle control system for implementing control of the optimal collision strategy.
Further, the shape parameters include ground clearance, bumper lower end depth, bumper center height, bumper height, hood front height, bumper upper end depth, hood angle, hood length, windshield angle.
Further, the scenario data includes rider height, two-wheel vehicle speed, two-wheel vehicle to vehicle angle, and rider distance.
Further, the step S3 includes:
s3-1: the information processing system receives real-time vehicle speed and scene data;
s3-2: performing braking judgment according to a preset vehicle braking algorithm and received real-time vehicle speed and scene data; the preset vehicle braking algorithm is as follows:
wherein g is gravity acceleration, mu is the friction coefficient of road and vehicle tyre, v is vehicle speed;
s3-3: if S>S Brake stop The information processing result is that braking can be performed, and the information processing result is sent to a vehicle braking system to perform emergency braking; if S<S Brake stop And if the information processing result is that braking is impossible, the scene data is sent to the damage prediction module, wherein S represents the distance of the rider.
Further, the S4 includes:
s4-1: retrieving accident data of collision between the vehicle and the two-wheel vehicle from a real accident investigation database;
s4-2: selecting data in which characteristics of the severity of the cyclist's injury in the accident can be affected, and generating a data set;
s4-3: cleaning the data in the data set, filling the missing values, removing error data, and finishing data coding;
s4-4: calculating the characteristics in the data set by an information gain method to obtain the importance degree of influence of each characteristic on the injury of the cyclist in the accident;
s4-5: generating a decision tree model according to the importance of each feature and the data in the data set;
s4-6: and preprocessing and pruning are carried out on the decision tree model, so that the generalization capability of the decision tree model is improved.
Further, the information gain method in S4-4 specifically comprises the following steps:
s4-4-1: the empirical entropy of the dataset is calculated, specifically:
wherein C is k Representing features in the data set, k features are arranged in total, D represents the data set, and H (D) represents experience entropy;
s4-4-2: the empirical conditional entropy H (d|a) of the feature a in the dataset D to the dataset D is calculated, specifically:
s4-4-3: calculating information gain:
g(D,A)＝H(D)-H(D│A)；
s4-4-4: the information gain is characterized as the feature importance.
Further, S5: the vehicle control system receives the optimal collision strategy sent by the damage prediction module, and controls the vehicle braking system to decelerate or accelerate according to the optimal collision strategy, the steering system to perform steering operation, and the air suspension control system to increase or decrease the height of the front cover;
the optimal collision strategy includes an optimal collision angle, an optimal collision velocity, and an optimal front cover height.
The vehicle active safety module design system based on the accident damage prediction of the cyclist comprises a vehicle control system, an information processing system, a ranging system, a vehicle braking system, a steering system, an air suspension control system and a damage prediction module, wherein the vehicle control system, the information processing system, the ranging system, the vehicle braking system, the steering system, the air suspension control system and the damage prediction module execute the above.
The principle and the advantages of the application are as follows: in the application, the step S1 represents data acquisition of the vehicle in a design stage, the step S2 represents data acquisition of the vehicle in a driving stage, and the data acquisition in the design stage and the data acquisition in the driving stage can be used for calculating that the vehicle can be braked before collision with the two-wheel vehicle through an algorithm on one hand, and can be used as an optimal collision strategy generation parameter source when the vehicle cannot be braked on the other hand; when the vehicle cannot be braked, the optimal collision strategy is generated through the damage prediction module, wherein the optimal collision strategy comprises an optimal collision angle, an optimal collision speed and an optimal front cover height, and the generated optimal collision strategy is sent to a steering system, a vehicle braking system and an air suspension control system by a vehicle control system to carry out braking control, steering control and vehicle front cover height control, so that the optimal collision strategy is realized, and the damage to a rider in an accident is reduced.
Drawings
FIG. 1 is a flow chart of an embodiment of the present application;
FIG. 2 is a flow chart of an embodiment of the present application;
FIG. 3 is a schematic view of the front end of the vehicle according to the embodiment of the present application;
FIG. 4 is a diagram of a decision tree model in an embodiment of the application;
fig. 5 is a view of a scene reconstruction model according to a real accident investigation database in an embodiment of the present application.
Detailed Description
The following is a further detailed description of the embodiments:
the embodiment is substantially as shown in figures 1 and 2: the design method of the active safety module of the vehicle based on the accident damage prediction of the cyclist comprises the following steps:
s1: after the modeling structure of the vehicle is confirmed, the shape parameters of the front end of the vehicle are acquired and are imported into an information processing system;
in this embodiment, the obtained shape parameters of the front end of the vehicle include a ground clearance, a bumper lower end depth, a bumper center height, a bumper height, a hood front edge height, a bumper upper end depth, a hood angle, a hood length, and a windshield angle, as shown in fig. 3, which are schematic diagrams of the shape parameters of the front end of the vehicle of a type of vehicle type, wherein a 0 Represents the ground clearance, a 1 Represents the depth of the lower end of the bumper, a 2 Represents the center height of the bumper, a 3 Representing the height of the bumper, a 4 Representing the height of the front edge of the engine cover, a 5 Represents the depth of the upper end of the bumper, a 6 Represents the hood angle, a 7 Indicating the length of the engine cover, a 8 Indicating the windshield angle.
S2: when the vehicle detects that a front rider is in a dangerous distance through a ranging system, scene data and real-time vehicle speed are acquired through an acquisition module, and the scene data and the real-time vehicle speed are led into an information processing system;
in this embodiment, when the ranging system of the vehicle detects that the rider in front of the vehicle is at the preset distance in the driving stage of the vehicle, the preset distance represents the dangerous distance between the rider and the front end of the vehicle, and the current scene data is collected through the collecting module of the vehicle, where the scene data includes the rider height, the speed of the two-wheel vehicle, the angle between the two-wheel vehicle and the front end of the vehicle, and other data may be further included, for example, as shown in the following table 1:
| sequence number | Scene data | Range |
| 1 | Vehicle type | Sedan,SUV,MPV,VAN |
| 2 | Two-wheel vehicle type | Bicycle,E-bike,Motorcycle |
| 3 | Rider height | 5 th ，50 th ，95 th Female percentile, 5 th ，50 th 、95 th Male in percentile |
| 4 | Collision velocity | 0-80km/h |
| 5 | Two-wheel vehicle speed | 0-30km/h |
| 6 | Angle of collision | 0°-90° |
TABLE 1
In this embodiment, the ranging system continuously detects an object in front of the vehicle by a reflected signal of a millimeter wave radar or a laser radar equipped for the vehicle and a video shot in real time by an equipped imaging system, and when detecting that the object, for example, a two-wheeled vehicle, enters a preset distance, transmits a corresponding scene to the information processing system.
S3: the information processing system judges whether braking can be performed or not according to the real-time vehicle speed, the scene data and a preset vehicle braking algorithm, and generates an information processing result; if the information processing result is that braking can be performed, the information processing system sends information to a vehicle braking system to perform emergency braking; if the information processing result is that stopping cannot be performed, the information is sent to a damage prediction module;
wherein S3 includes:
s3-1: the information processing system receives real-time vehicle speed and scene data;
s3-2: performing braking judgment according to a preset vehicle braking algorithm and received real-time vehicle speed and scene data; the preset vehicle braking algorithm is as follows:
wherein g is gravity acceleration, mu is the friction coefficient of road and vehicle tyre, v is vehicle speed;
s3-3: if S>S Brake stop Information processing junctionIf the vehicle can be braked, the information processing result is sent to a vehicle braking system to execute emergency braking; if S<S Brake stop And if the information processing result is that braking is impossible, the scene data is sent to the damage prediction module, wherein S represents the distance of the rider.
In this embodiment, the information processing system receives real-time vehicle speed and scene data, calculates whether the vehicle can be braked emergently when the front two-wheel vehicle appears at a preset distance, if so, the information processing system sends processing information to the vehicle braking module to brake emergently, if not, the processed information is transmitted to the damage prediction module, and the damage prediction module is used for generating an optimal collision strategy so as to reduce damage to a rider.
S4: the damage prediction module is used for processing the information processing result, the scene data and the shape parameters according to the preset decision tree model, generating an optimal collision strategy of a rider in the scene, and sending the optimal collision strategy to the vehicle control system for implementing control of the optimal collision strategy.
Wherein, the S4 includes:
s4-1: retrieving accident data of collision between the vehicle and the two-wheel vehicle from a real accident investigation database;
s4-2: selecting data in which characteristics of the severity of the cyclist's injury in the accident can be affected, and generating a data set;
s4-3: cleaning the data in the data set, filling the missing values, removing error data, and finishing data coding;
s4-4: calculating the characteristics in the data set by an information gain method to obtain the importance degree of influence of each characteristic on the injury of the cyclist in the accident; the information gain method in S4-4 is specifically as follows:
s4-4-1: the empirical entropy of the dataset is calculated, specifically:
wherein C is k Indicating numberK features are arranged in total according to the features in the data set, D represents the data set, and H (D) represents the experience entropy;
s4-4-2: the empirical conditional entropy H (d|a) of the feature a in the dataset D to the dataset D is calculated, specifically:
s4-4-3: calculating information gain:
g(D,A)＝H(D)-H(D│A)；
s4-4-4: characterizing the information gain as feature importance;
s4-5: generating a decision tree model according to the importance of each feature and the data in the data set;
s4-6: and preprocessing and pruning are carried out on the decision tree model, so that the generalization capability of the decision tree model is improved.
In this embodiment, the real accident investigation database is a database made by the current chinese automobile research for various accidents, including an automobile-to-automobile collision accident, an automobile-to-person collision accident, an automobile-to-two-wheel vehicle collision accident, and the like, where the stored automobile-to-two-wheel vehicle collision accident data is a decision parameter source acquired by the present application, for example, as shown in fig. 5, in the real accident investigation database, scene reconstruction is performed for a class of real accidents, including a real photograph and a scene model, the real photograph includes a rider-to-automobile collision photograph, a rider air movement photograph, and a rider-to-ground collision photograph, scene reconstruction according to the real photograph also includes a rider-to-automobile collision model, a rider air movement model, and a rider-to-ground collision model, and decision parameters are acquired according to the reconstructed scene model, where the decision parameters include a vehicle speed, a two-wheel vehicle speed, a rider height, a rider-to-engine cover-off-ground height, and the like, and the severity of the above decision parameters are classified into three classes as a judgment result according to the class of injury, respectively, in this embodiment, as three classes of the judgment result:
low (L): MAIS grade is less than 2;
in (M): MAIS grade 2;
high (H): the MAIS rating is 3 or above.
Generating a data set by using the characteristics capable of representing the severity of the injury of the cyclist according to the classified categories, determining the importance of the characteristics by using an information gain method, generating a decision tree model according to the ranking of the importance of the characteristics, wherein the more important the characteristics are, the more important the characteristics are placed in the front position in the decision tree; meanwhile, in order to avoid overfitting of the decision tree model and improve generalization capability of data outside the training data set, the decision number model needs to be preprocessed, including simplifying the decision tree and removing useless subtrees or leaf nodes.
FIG. 4 is a schematic diagram of decision nodes of a decision tree model under a certain class of scenarios. In this example we get the scenario where the cyclist injury probability is low (L) when the parameters of the collision are as in table 2:
TABLE 2
The decision is made based on the vehicle speed at that time. For example, when the speed of the vehicle is not high and the speed of the vehicle can be reduced to less than 45km/h before collision, the parameter (3) can be selected to improve a 4 (hood front height) to greater than 727cm; if the speed of the vehicle is high and the speed cannot be reduced to less than 45km before collision, the speed of the front two-wheeled vehicle is further confirmed, and if the speed is more than 18km/h, a can be reduced selectively 4 To 743cm to achieve L injury, otherwise only one strategy can be selected in the decision of injury M in fig. 4.
S5: the vehicle control system receives the optimal collision strategy sent by the damage prediction module, and controls the vehicle braking system to decelerate or accelerate according to the optimal collision strategy, the steering system to perform steering operation, and the air suspension control system to increase or decrease the height of the front cover;
the optimal collision strategy includes an optimal collision angle, an optimal collision velocity, and an optimal front cover height.
According to the application, the collision condition of the vehicle and the cyclist is analyzed and processed through the damage prediction module, the generated optimal collision strategy is fed back and processed in real time through the steering system, the vehicle braking system and the air suspension system of the vehicle, and parameters such as the angle, the speed, the height and the like of the vehicle and the cyclist before collision are adjusted to be in an optimal state, so that the damage of the vehicle to the cyclist is reduced to the greatest extent.
In another embodiment of the present embodiment, a vehicle active safety module design system based on rider accident injury prediction is further included, including a vehicle control system, an information processing system, a ranging system, a vehicle braking system, a steering system, an air suspension control system, and an injury prediction module, which perform the above.
The foregoing is merely exemplary of the present application, and specific structures and features well known in the art will not be described in detail herein, so that those skilled in the art will be aware of all the prior art to which the present application pertains, and will be able to ascertain the general knowledge of the technical field in the application or prior art, and will not be able to ascertain the general knowledge of the technical field in the prior art, without using the prior art, to practice the present application, with the aid of the present application, to ascertain the general knowledge of the same general knowledge of the technical field in general purpose. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present application, and these should also be considered as the scope of the present application, which does not affect the effect of the implementation of the present application and the utility of the patent. The protection scope of the present application is subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (8)
1. The design method of the active safety module of the vehicle based on the accident damage prediction of the cyclist is characterized by comprising the following steps of: comprising the following steps:
s1: after the modeling structure of the vehicle is confirmed, the shape parameters of the front end of the vehicle are acquired and are imported into an information processing system;
s2: when the vehicle detects that a front rider is within a preset distance through a distance measuring system, scene data and real-time vehicle speed are acquired through an acquisition module, and the scene data and the real-time vehicle speed are led into an information processing system;
s3: the information processing system judges whether braking can be performed or not according to the real-time vehicle speed, the scene data and a preset vehicle braking algorithm, and generates an information processing result; if the information processing result is that braking can be performed, the information processing system sends information to a vehicle braking system to perform emergency braking; if the information processing result is that stopping cannot be performed, the information is sent to a damage prediction module;
s4: the damage prediction module is used for processing the information processing result, the scene data and the shape parameters according to the preset decision tree model, generating an optimal collision strategy of a rider in the scene, and sending the optimal collision strategy to the vehicle control system for implementing control of the optimal collision strategy.
2. The method for designing a vehicle active safety module based on rider accident damage prediction as set forth in claim 1, wherein: the shape parameters include ground clearance, bumper lower end depth, bumper center height, bumper height, hood front height, bumper upper end depth, hood angle, hood length, windshield angle.
3. The method for designing a vehicle active safety module based on rider accident damage prediction as set forth in claim 2, wherein: the scene data includes rider height, two-wheel vehicle speed, two-wheel vehicle to vehicle angle, and rider distance.
4. A method of designing a vehicle active safety module based on rider accident damage prediction as set forth in claim 3, wherein: the step S3 comprises the following steps:
s3-1: the information processing system receives real-time vehicle speed and scene data;
s3-2: performing braking judgment according to a preset vehicle braking algorithm and received real-time vehicle speed and scene data; the preset vehicle braking algorithm is as follows:
wherein g is gravity acceleration, mu is the friction coefficient of road and vehicle tyre, v is vehicle speed;
s3-3: if S>S Brake stop The information processing result is that braking can be performed, and the information processing result is sent to a vehicle braking system to perform emergency braking; if S<S Brake stop And if the information processing result is that braking is impossible, the scene data is sent to the damage prediction module, wherein S represents the distance of the rider.
5. The method for designing a vehicle active safety module based on rider accident damage prediction of claim 4, wherein: the step S4 comprises the following steps:
s4-1: retrieving accident data of collision between the vehicle and the two-wheel vehicle from a real accident investigation database;
s4-2: selecting data in which characteristics of the severity of the cyclist's injury in the accident can be affected, and generating a data set;
s4-3: cleaning the data in the data set, filling the missing values, removing error data, and finishing data coding;
s4-4: calculating the characteristics in the data set by an information gain method to obtain the importance degree of influence of each characteristic on the injury of the cyclist in the accident;
s4-5: generating a decision tree model according to the importance of each feature and the data in the data set;
s4-6: and preprocessing and pruning are carried out on the decision tree model, so that the generalization capability of the decision tree model is improved.
6. The method for designing a vehicle active safety module based on rider accident damage prediction of claim 5, wherein: the information gain method in S4-4 specifically comprises the following steps:
s4-4-1: the empirical entropy of the dataset is calculated, specifically:
wherein C is k Representing features in the data set, k features are arranged in total, D represents the data set, and H (D) represents experience entropy;
s4-4-2: the empirical conditional entropy H (d|a) of the feature a in the dataset D to the dataset D is calculated, specifically:
s4-4-3: calculating information gain:
g(D,A)＝H(D)-H(D│A)；
s4-4-4: the information gain is characterized as the feature importance.
7. The method for designing a vehicle active safety module based on rider accident damage prediction of claim 6, wherein: further comprising S5: the vehicle control system receives the optimal collision strategy sent by the damage prediction module, and controls the vehicle braking system to decelerate or accelerate according to the optimal collision strategy, the steering system to perform steering operation, and the air suspension control system to increase or decrease the height of the front cover;
the optimal collision strategy includes an optimal collision angle, an optimal collision velocity, and an optimal front cover height.
8. Vehicle initiative safety module design system based on cyclist accident injury prediction, its characterized in that: comprising a vehicle control system, an information processing system, a distance measuring system, a vehicle brake system, a steering system, an air suspension control system and a damage prediction module, which vehicle control system, information processing system, distance measuring system, vehicle brake system, steering system, air suspension control system and damage prediction module perform the object according to any of claims 1-7.
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| CN115257614A (en) * | 2022-07-25 | 2022-11-01 | 重庆长安汽车股份有限公司 | Intelligent automobile's overall process collision safety control system and car |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117496696A (en) * | 2023-10-19 | 2024-02-02 | 深圳市新城市规划建筑设计股份有限公司 | Traffic management system based on big data |
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