CN116176576A - Trailer collision intelligent early warning system and method based on artificial intelligence - Google Patents
Trailer collision intelligent early warning system and method based on artificial intelligence Download PDFInfo
- Publication number
- CN116176576A CN116176576A CN202310450267.XA CN202310450267A CN116176576A CN 116176576 A CN116176576 A CN 116176576A CN 202310450267 A CN202310450267 A CN 202310450267A CN 116176576 A CN116176576 A CN 116176576A
- Authority
- CN
- China
- Prior art keywords
- collision
- trailer
- steering
- early warning
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000001514 detection method Methods 0.000 claims abstract description 13
- 238000006243 chemical reaction Methods 0.000 claims abstract description 11
- 238000011156 evaluation Methods 0.000 claims description 17
- 230000008859 change Effects 0.000 claims description 10
- 238000003062 neural network model Methods 0.000 claims description 10
- 230000001133 acceleration Effects 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 abstract description 8
- 238000004364 calculation method Methods 0.000 abstract description 5
- 230000000694 effects Effects 0.000 abstract description 5
- 230000009471 action Effects 0.000 abstract description 4
- 238000005259 measurement Methods 0.000 abstract description 4
- 230000008569 process Effects 0.000 description 6
- 230000000007 visual effect Effects 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 239000000853 adhesive Substances 0.000 description 1
- 230000001070 adhesive effect Effects 0.000 description 1
- 239000010426 asphalt Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000003137 locomotive effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T7/00—Brake-action initiating means
- B60T7/12—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger
- B60T7/20—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger specially for trailers, e.g. in case of uncoupling of or overrunning by trailer
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of data processing, and discloses an intelligent trailer collision early warning system and method based on artificial intelligence, comprising the following steps: the system comprises an environment sensing module, a target recognition unit, a sensing detection module, a collision early warning module and an early warning reaction module, wherein the image information of targets such as vehicles, pedestrians, traffic facilities, roadside objects and the like around a trailer can be obtained in real time, the classification of specific targets is judged by utilizing the target recognition unit built based on AI technical training, then the sensing detection module carries out motion state parameter measurement and calculation on the corresponding obstacle objects, collision early warning is carried out, and when collision is judged, the early warning reaction module carries out braking action according to a corresponding collision processing strategy, so that the auxiliary driving effect is achieved to a certain extent, and the driving safety is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent early warning system for trailer collision based on artificial intelligence.
Background
A transport vehicle consisting of a tractor-drawn trailer is a major vehicle type in road transport, which has advantages of high efficiency, safety, etc., and thus is widely used in the field of cargo transportation; because the body of the trailer is relatively long, real-time safety detection needs to be carried out on the whole state of the trailer in the running process of the trailer so as to avoid the problem that hidden danger exists in the steering and running processes of the trailer to influence the transportation safety of the trailer.
The existing trailer transportation safety detection system mainly detects motion parameters, such as speed, acceleration and the like, in the trailer running process, and judges the safety state of the trailer in the trailer running and steering process by judging whether each motion parameter exceeds a standard range.
However, the trailer itself has a relatively large mass, and at relatively high speeds, it has a relatively long braking distance, and is relatively poor in view of the side, with serious consequences as soon as the danger arises. In addition, although the motion parameters of the vehicle state model can be accurately judged by analyzing the vehicle state model in the prior art, the process is mainly suitable for a design analysis stage, and the state of the real-time synchronous vehicle model has huge calculation amount of data and pressure in communication, and is easy to cause faults, so that the process is not suitable for practical application.
Disclosure of Invention
The invention aims to provide an intelligent early warning system for trailer collision based on artificial intelligence, which solves the following technical problems:
how to improve safety during the formation of the trailer.
The aim of the invention can be achieved by the following technical scheme:
an artificial intelligence based trailer collision intelligent early warning system, comprising:
the environment sensing module is used for acquiring road condition environment information around the trailer in real time;
the target recognition unit is connected with the environment sensing module and is used for distinguishing and classifying obstacle objects around the trailer according to the road condition environment information;
the sensing detection module is connected with the target identification unit and is used for monitoring and acquiring the motion state parameters of the corresponding obstacle according to the classification result;
the collision early warning module is connected with the sensing detection module and the environment sensing module and is used for carrying out collision early warning according to the self motion parameters of the trailer and the motion state parameters of the obstacle;
the early warning reaction module is connected with the collision early warning module and is used for executing a corresponding collision processing strategy according to the collision early warning;
the road condition environment information comprises forward shooting data, environment temperature and road surface parameters, and the target recognition unit is a trained neural network model.
Through the technical scheme, the image information of the targets such as vehicles, pedestrians, traffic facilities, roadside objects and the like around the trailer can be obtained in real time, the classification of the specific targets is judged by utilizing the target recognition unit built based on AI technical training, then the corresponding obstacle objects are subjected to motion state parameter measurement and calculation by the sensing detection module, collision early warning is carried out, and when the collision is judged, the early warning reaction module carries out braking action according to the corresponding collision processing strategy, so that the auxiliary driving effect is achieved to a certain extent, and the driving safety is improved.
As a further scheme of the invention: further comprises:
the steering sensing module is used for acquiring steering parameters of the trailer; the steering parameters include steering angle and steering angular acceleration;
the steering estimation module is connected with the steering sensing module and used for calculating a steering evaluation value according to the steering parameter;
and the auxiliary sensing module is connected with the steering estimation module and is used for acquiring the field video stream of the future steering direction when the steering estimation value is higher than a preset threshold value.
As a further scheme of the invention: the road condition environment information comprises steering direction camera shooting data;
the auxiliary sensing module comprises an automatic telescopic assembly and a shooting unit which are arranged in the middle of the trailer;
when the steering evaluation value is higher than a preset threshold value, the automatic telescopic assembly stretches out and drives the shooting unit to shoot, and the steering direction shooting data are obtained.
As a further scheme of the invention: the method for acquiring the steering evaluation value comprises the following steps:
wherein ,
to determine the time window length, +.>
Is at->
Actual angle of rotation change value of steering wheel in time, < >>
Is at->
Total steering angle change value of steering wheel in time, < >>
For the rotational acceleration of the steering wheel, +.>
For the actual steering angle at time t +.>
、/>
、/>
Respectively presetting coefficients for corresponding de-dimensionality values, < + >>
。
As a further scheme of the invention: the collision early warning module includes:
the sudden stop performance estimating unit is used for estimating the stopping distance according to the historical stopping information, the self motion parameters and the road condition environment information;
the collision early warning ranging unit is used for acquiring the actual distance between the obstacle and the trailer;
a collision judging unit for judging whether collision occurs or not according to the estimated stopping distance and the actual distance and the movement speed and movement direction of the obstacle;
the self-movement parameters comprise self-running speed, self-running direction and self-weight.
As a further scheme of the invention: the scram performance estimating unit includes:
the data generation module is used for loading the self-running speed, the self-running direction, the self-weight, the ambient temperature and the road surface parameters into the blank picture according to the corresponding conversion rule to obtain an analysis data graph;
the stopping distance estimation model is used for outputting corresponding estimated stopping distance according to the analysis data graph;
the stopping distance pre-estimation model is a trained neural network model.
As a further scheme of the invention: the scram performance estimating unit further includes:
the data supplementing module is used for detecting whether to start executing a corresponding collision processing strategy, and if so, loading the self-running speed change condition and the running distance in a preset time period into the analysis data graph;
the stopping distance estimation model is used for outputting corresponding new estimated stopping distance according to the new analysis data graph;
the starting point of the preset time period is the time for starting to execute the corresponding collision processing strategy, and the duration is a preset value;
the collision judging unit judges whether collision occurs or not according to the new estimated stopping distance and the actual distance and the movement speed and movement direction of the obstacle.
An intelligent early warning method for trailer collision based on artificial intelligence comprises the following steps:
acquiring road condition environment information around a trailer in real time;
the object recognition unit is used for distinguishing and classifying obstacle objects around the trailer according to the road condition environmental information;
according to the classification result, monitoring and acquiring the motion state parameters of the corresponding obstacle;
performing collision early warning according to the motion parameters of the trailer and the motion state parameters of the obstacle;
executing a corresponding collision processing strategy according to the collision early warning;
the road condition environment information comprises forward shooting data, environment temperature and road surface parameters, and the target recognition unit is a trained neural network model.
As a further scheme of the invention: the collision handling strategy includes:
continuously acquiring the movement track direction of the obstacle after judging that collision occurs;
if the obstacle is positioned in front of the trailer, limiting the average sliding rate of the rear wheels of the trailer to be larger than that of the front wheels;
if the obstacle moves from the left front to the right front of the trailer, setting and limiting the slip rate of the left rear wheel of the trailer to be larger than or equal to the amplitude of the right rear wheel according to a steering evaluation value;
and if the obstacle moves from the right front direction to the left front direction of the trailer, setting and limiting the slip rate of the left rear wheel of the trailer to be less than or equal to the amplitude of the right rear wheel according to the steering evaluation value.
As a further scheme of the invention: the slip ratio is limited to 15% -25%.
The invention has the beneficial effects that: the invention can acquire the image information of targets such as vehicles, pedestrians, traffic facilities, roadside objects and the like around the trailer in real time, utilizes the target recognition unit built based on AI technical training to judge the classification of the specific targets, then carries out motion state parameter measurement and calculation on the corresponding obstacle objects by the sensing detection module, carries out collision early warning, and carries out braking action by the early warning reaction module according to the corresponding collision processing strategy when the collision is judged to occur, thereby achieving the effect of assisting driving to a certain extent and improving driving safety.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic connection diagram of basic modules of the intelligent early warning system for collision of a trailer in the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention is an intelligent early warning system for collision of a trailer based on artificial intelligence, comprising:
the environment sensing module is used for acquiring road condition environment information around the trailer in real time;
the target recognition unit is connected with the environment sensing module and is used for distinguishing and classifying obstacle objects around the trailer according to the road condition environment information;
the sensing detection module is connected with the target identification unit and is used for monitoring and acquiring the motion state parameters of the corresponding obstacle according to the classification result;
the collision early warning module is connected with the sensing detection module and the environment sensing module and is used for carrying out collision early warning according to the self motion parameters of the trailer and the motion state parameters of the obstacle;
the early warning reaction module is connected with the collision early warning module and is used for executing a corresponding collision processing strategy according to the collision early warning;
the road condition environment information comprises forward shooting data, environment temperature and road surface parameters, and the target recognition unit is a trained neural network model.
Through the technical scheme, the image information of the targets such as vehicles, pedestrians, traffic facilities, roadside objects and the like around the trailer can be obtained in real time, the classification of the specific targets is judged by utilizing the target recognition unit built based on AI technical training, then the corresponding obstacle objects are subjected to motion state parameter measurement and calculation by the sensing detection module, collision early warning is carried out, and when the collision is judged, the early warning reaction module carries out braking action according to the corresponding collision processing strategy, so that the auxiliary driving effect is achieved to a certain extent, and the driving safety is improved.
As a further scheme of the invention: further comprises:
the steering sensing module is used for acquiring steering parameters of the trailer; the steering parameters include steering angle and steering angular acceleration;
the steering estimation module is connected with the steering sensing module and used for calculating a steering evaluation value according to the steering parameter;
and the auxiliary sensing module is connected with the steering estimation module and is used for acquiring the field video stream of the future steering direction when the steering estimation value is higher than a preset threshold value.
Because the whole volume of the trailer is larger, the general locomotive is wider, the semitrailer body is longer, and the difference of the inner wheels of the semitrailer body exists, the difference of the inner wheels of the semitrailer body refers to the difference between the inner front wheel turning radius and the inner rear wheel turning radius when the vehicle turns. Due to the existence of the inner wheel difference, when the vehicle turns, the movement tracks of the front wheel and the rear wheel are not coincident; if only the front wheel passes through the bicycle and forgets the difference of the inner wheel, the rear inner wheel can be driven out of the road or collide with other objects during the driving. The longer the vehicle body is, the larger the formed "wheel difference" is, and the range of the inner wheel difference is also enlarged. Particularly, the body of a semi-trailer or a heavy vehicle is long, and after the head of the vehicle is rotated, the long body is not rotated, so that a visual blind area of a driver of the large vehicle is easily formed. When a non-motor vehicle or a pedestrian walks into the 'visual blind area' of the inner wheel, the generated danger is increased, and once the vehicle body is clung to the vehicle body, the vehicle body is likely to be dragged into the vehicle by turning, and serious traffic accidents can be caused.
In the embodiment of the invention, the steering sensing module and the steering estimation module are adopted to judge whether the driver really steers, and when the driver is considered to be about to or is steering, the auxiliary sensing module is started to acquire the video image of the vehicle side and collect the video image as road condition environment information, so that the driver can be assisted in judging potential collision, and the driving safety is improved.
As a further scheme of the invention: the road condition environment information comprises steering direction camera shooting data;
the auxiliary sensing module comprises an automatic telescopic assembly and a shooting unit which are arranged in the middle of the trailer;
when the steering evaluation value is higher than a preset threshold value, the automatic telescopic assembly stretches out and drives the shooting unit to shoot, and the steering direction shooting data are obtained.
As a further scheme of the invention: the method for acquiring the steering evaluation value comprises the following steps:
wherein ,
to determine the time window length, +.>
Is at->
Actual angle of rotation change value of steering wheel in time, < >>
Is at->
Total steering angle change value of steering wheel in time, < >>
For the rotational acceleration of the steering wheel, +.>
For the actual steering angle at time t +.>
、/>
、/>
Respectively presetting coefficients for corresponding de-dimensionality values, < + >>
。
As a further scheme of the invention: the collision early warning module includes:
the sudden stop performance estimating unit is used for estimating the stopping distance according to the historical stopping information, the self motion parameters and the road condition environment information;
the collision early warning ranging unit is used for acquiring the actual distance between the obstacle and the trailer;
a collision judging unit for judging whether collision occurs or not according to the estimated stopping distance and the actual distance and the movement speed and movement direction of the obstacle;
the self-movement parameters comprise self-running speed, self-running direction and self-weight.
In the invention, because the road condition environmental information is an important influence factor of whether collision is possible, whether the vehicle is actually steering is required to be evaluated through the steering evaluation value, so that the auxiliary sensing module is started timely to acquire the road condition environmental information, otherwise, the situation that the driver overtakes the vehicle when the vehicle is overtaking or is overtaken is possibly misjudged to have collision risk, and normal driving is influenced.
As a further scheme of the invention: the scram performance estimating unit includes:
the data generation module is used for loading the self-running speed, the self-running direction, the self-weight, the ambient temperature and the road surface parameters into the blank picture according to the corresponding conversion rule to obtain an analysis data graph;
the stopping distance estimation model is used for outputting corresponding estimated stopping distance according to the analysis data graph;
the stopping distance pre-estimation model is a trained neural network model.
Therefore, the fact that the self-running speed, the self-running direction, the self-weight, the ambient temperature and the road surface parameters can influence the braking distance is considered, and generally, the larger the self-weight is, the larger the inertia is, the more difficult the braking is; the higher the ambient temperature is, the higher the temperature of the braking system is, the corresponding braking effect is poor, and the situation that the braking distance is increased is caused; in addition, road surface parameters reflecting road type and wet skid, such as dry cement road or wet asphalt road, also affect the braking distance. Therefore, the neural network model can label the historical stopping distance of the trailer as a label during training, and output the estimated stopping distance after training is finished; in addition, the continuous supplementary training of training samples can be further continued, and the measuring and calculating precision of the estimated stopping distance can be continuously optimized.
As a further scheme of the invention: the scram performance estimating unit further includes:
the data supplementing module is used for detecting whether to start executing a corresponding collision processing strategy, and if so, loading the self-running speed change condition and the running distance in a preset time period into the analysis data graph;
the stopping distance estimation model is used for outputting corresponding new estimated stopping distance according to the new analysis data graph;
the starting point of the preset time period is the time for starting to execute the corresponding collision processing strategy, and the duration is a preset value;
the collision judging unit judges whether collision occurs or not according to the new estimated stopping distance and the actual distance and the movement speed and movement direction of the obstacle.
The above technical solution is equivalent to loading the motion state change condition in a preset time period after the trailer starts to brake into the analysis data graph, and in this embodiment, the duration of the preset time period may be set to 0.05 seconds.
An intelligent early warning method for trailer collision based on artificial intelligence comprises the following steps:
acquiring road condition environment information around a trailer in real time;
the object recognition unit is used for distinguishing and classifying obstacle objects around the trailer according to the road condition environmental information;
according to the classification result, monitoring and acquiring the motion state parameters of the corresponding obstacle;
performing collision early warning according to the motion parameters of the trailer and the motion state parameters of the obstacle;
executing a corresponding collision processing strategy according to the collision early warning;
the road condition environment information comprises forward shooting data, environment temperature and road surface parameters, and the target recognition unit is a trained neural network model.
As a further scheme of the invention: the collision handling strategy includes:
continuously acquiring the movement track direction of the obstacle after judging that collision occurs;
if the obstacle is positioned in front of the trailer, limiting the average sliding rate of the rear wheels of the trailer to be larger than that of the front wheels;
if the obstacle moves from the left front to the right front of the trailer, setting and limiting the slip rate of the left rear wheel of the trailer to be larger than or equal to the amplitude of the right rear wheel according to a steering evaluation value;
and if the obstacle moves from the right front direction to the left front direction of the trailer, setting and limiting the slip rate of the left rear wheel of the trailer to be less than or equal to the amplitude of the right rear wheel according to the steering evaluation value.
As a further scheme of the invention: the slip ratio is limited to 15% -25%.
When the vehicle is braked, the magnitude of the braking force of the brake can be automatically controlled, so that the wheels are not locked, the slip rate is generally about 20%, and the vehicle is in a rolling and sliding state so as to ensure that the adhesive force between the wheels and the ground is at the maximum. Because the trailer body is longer and the weight of the rear part of the trailer is larger, the tail flick condition is easy to occur in emergency braking, the average sliding rate of the rear wheels of the trailer is larger than that of the front wheels, and the possibility of the tail flick occurring when the steering wheel is jogged is reduced as much as possible;
if the obstacle moves from the left front direction to the right front direction of the trailer, the lower consciousness operation of the driver has a larger probability of steering wheel steering to the right, at the moment, the situation that the vehicle swings the tail to the left easily occurs in emergency braking, and the swing tail amplitude is related to the speed and the amplitude of steering wheel steering to the right, so that the slip rate of the left rear wheel and the right rear wheel can be controlled and set by combining the steering evaluation value, the probability of swing tail greatly is reduced, and the driving safety is improved.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (10)
1. An artificial intelligence based trailer collision intelligent early warning system, which is characterized by comprising:
the environment sensing module is used for acquiring road condition environment information around the trailer in real time;
the target recognition unit is connected with the environment sensing module and is used for distinguishing and classifying obstacle objects around the trailer according to the road condition environment information;
the sensing detection module is connected with the target identification unit and is used for monitoring and acquiring the motion state parameters of the corresponding obstacle according to the classification result;
the collision early warning module is connected with the sensing detection module and the environment sensing module and is used for carrying out collision early warning according to the self motion parameters of the trailer and the motion state parameters of the obstacle;
the early warning reaction module is connected with the collision early warning module and is used for executing a corresponding collision processing strategy according to the collision early warning;
the road condition environment information comprises forward shooting data, environment temperature and road surface parameters, and the target recognition unit is a trained neural network model.
2. The artificial intelligence based trailer collision intelligent pre-warning system of claim 1, further comprising:
the steering sensing module is used for acquiring steering parameters of the trailer; the steering parameters include steering angle and steering angular acceleration;
the steering estimation module is connected with the steering sensing module and used for calculating a steering evaluation value according to the steering parameter;
and the auxiliary sensing module is connected with the steering estimation module and is used for acquiring the field video stream of the future steering direction when the steering estimation value is higher than a preset threshold value.
3. The intelligent early warning system for collision of a trailer based on artificial intelligence according to claim 2, wherein the road condition environmental information includes steering direction camera data;
the auxiliary sensing module comprises an automatic telescopic assembly and a shooting unit which are arranged in the middle of the trailer;
when the steering evaluation value is higher than a preset threshold value, the automatic telescopic assembly stretches out and drives the shooting unit to shoot, and the steering direction shooting data are obtained.
4. The intelligent early warning system for collision of a trailer based on artificial intelligence according to claim 2, wherein the method for acquiring the steering evaluation value comprises:
wherein ,
to determine the time window length, +.>
Is at->
Actual angle of rotation change value of steering wheel in time, < >>
Is at->
Total steering angle change value of steering wheel in time, < >>
For the rotational acceleration of the steering wheel, +.>
For the actual steering angle at time t +.>
、/>
、/>
Respectively presetting coefficients for corresponding de-dimensionality values, < + >>
。
5. The artificial intelligence based trailer collision intelligent pre-warning system of claim 2, wherein the collision pre-warning module comprises:
the sudden stop performance estimating unit is used for estimating the stopping distance according to the historical stopping information, the self motion parameters and the road condition environment information;
the collision early warning ranging unit is used for acquiring the actual distance between the obstacle and the trailer;
a collision judging unit for judging whether collision occurs or not according to the estimated stopping distance and the actual distance and the movement speed and movement direction of the obstacle;
the self-movement parameters comprise self-running speed, self-running direction and self-weight.
6. The intelligent early warning system for collision of a trailer based on artificial intelligence according to claim 5, wherein the sudden stop performance estimating unit comprises:
the data generation module is used for loading the self-running speed, the self-running direction, the self-weight, the ambient temperature and the road surface parameters into the blank picture according to the corresponding conversion rule to obtain an analysis data graph;
the stopping distance estimation model is used for outputting corresponding estimated stopping distance according to the analysis data graph;
the stopping distance pre-estimation model is a trained neural network model.
7. The intelligent early warning system for a collision of a trailer based on artificial intelligence of claim 6, wherein the sudden stop performance estimating unit further comprises:
the data supplementing module is used for detecting whether to start executing a corresponding collision processing strategy, and if so, loading the self-running speed change condition and the running distance in a preset time period into the analysis data graph;
the stopping distance estimation model is used for outputting corresponding new estimated stopping distance according to the new analysis data graph;
the starting point of the preset time period is the time for starting to execute the corresponding collision processing strategy, and the duration is a preset value;
the collision judging unit judges whether collision occurs or not according to the new estimated stopping distance and the actual distance and the movement speed and movement direction of the obstacle.
8. An artificial intelligence based trailer collision intelligent early warning method, characterized in that the method is applied to the artificial intelligence based trailer collision intelligent early warning system as claimed in any one of claims 1 to 7, comprising:
acquiring road condition environment information around a trailer in real time;
the object recognition unit is used for distinguishing and classifying obstacle objects around the trailer according to the road condition environmental information;
according to the classification result, monitoring and acquiring the motion state parameters of the corresponding obstacle;
performing collision early warning according to the motion parameters of the trailer and the motion state parameters of the obstacle;
executing a corresponding collision processing strategy according to the collision early warning;
the road condition environment information comprises forward shooting data, environment temperature and road surface parameters, and the target recognition unit is a trained neural network model.
9. The artificial intelligence based trailer collision intelligent pre-warning method of claim 8, wherein the collision handling strategy comprises:
continuously acquiring the movement track direction of the obstacle after judging that collision occurs;
if the obstacle is positioned in front of the trailer, limiting the average sliding rate of the rear wheels of the trailer to be larger than that of the front wheels;
if the obstacle moves from the left front to the right front of the trailer, setting and limiting the slip rate of the left rear wheel of the trailer to be larger than or equal to the amplitude of the right rear wheel according to a steering evaluation value;
and if the obstacle moves from the right front direction to the left front direction of the trailer, setting and limiting the slip rate of the left rear wheel of the trailer to be less than or equal to the amplitude of the right rear wheel according to the steering evaluation value.
10. The intelligent early warning method for collision of a trailer based on artificial intelligence according to claim 9, wherein the slip rate is limited to 15% -25%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310450267.XA CN116176576B (en) | 2023-04-25 | 2023-04-25 | Trailer collision intelligent early warning system and method based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310450267.XA CN116176576B (en) | 2023-04-25 | 2023-04-25 | Trailer collision intelligent early warning system and method based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116176576A true CN116176576A (en) | 2023-05-30 |
| CN116176576B CN116176576B (en) | 2023-07-14 |
Family
ID=86452486
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310450267.XA Active CN116176576B (en) | 2023-04-25 | 2023-04-25 | Trailer collision intelligent early warning system and method based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116176576B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116946089A (en) * | 2023-09-20 | 2023-10-27 | 深圳市蓝鲸智联科技股份有限公司 | Intelligent brake auxiliary system |
| CN117710909A (en) * | 2024-02-02 | 2024-03-15 | 多彩贵州数字科技股份有限公司 | Rural road intelligent monitoring system based on target detection and instance segmentation |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010193016A (en) * | 2009-02-16 | 2010-09-02 | Ricoh Co Ltd | Method and device for support of calibration for onboard camera device, and onboard camera device |
| CN106157386A (en) * | 2015-04-23 | 2016-11-23 | 中国电信股份有限公司 | Vehicular video filming control method and device |
| KR20180075985A (en) * | 2016-12-27 | 2018-07-05 | 재단법인대구경북과학기술원 | Apparatus for autonomous steering prediction considering driving environment and method thereof |
| CN109094460A (en) * | 2017-06-20 | 2018-12-28 | 丰田自动车株式会社 | Drive assistance device |
| CN111284407A (en) * | 2018-12-06 | 2020-06-16 | 沈阳美行科技有限公司 | Display method, device and apparatus for auxiliary steering and related equipment |
| CN111361557A (en) * | 2020-02-13 | 2020-07-03 | 江苏大学 | Early warning method for collision accident during turning of heavy truck |
| CN111976726A (en) * | 2020-08-26 | 2020-11-24 | 中南大学 | Steering auxiliary system of intelligent rail vehicle and control method thereof |
-
2023
- 2023-04-25 CN CN202310450267.XA patent/CN116176576B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010193016A (en) * | 2009-02-16 | 2010-09-02 | Ricoh Co Ltd | Method and device for support of calibration for onboard camera device, and onboard camera device |
| CN106157386A (en) * | 2015-04-23 | 2016-11-23 | 中国电信股份有限公司 | Vehicular video filming control method and device |
| KR20180075985A (en) * | 2016-12-27 | 2018-07-05 | 재단법인대구경북과학기술원 | Apparatus for autonomous steering prediction considering driving environment and method thereof |
| CN109094460A (en) * | 2017-06-20 | 2018-12-28 | 丰田自动车株式会社 | Drive assistance device |
| CN111284407A (en) * | 2018-12-06 | 2020-06-16 | 沈阳美行科技有限公司 | Display method, device and apparatus for auxiliary steering and related equipment |
| CN111361557A (en) * | 2020-02-13 | 2020-07-03 | 江苏大学 | Early warning method for collision accident during turning of heavy truck |
| CN111976726A (en) * | 2020-08-26 | 2020-11-24 | 中南大学 | Steering auxiliary system of intelligent rail vehicle and control method thereof |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116946089A (en) * | 2023-09-20 | 2023-10-27 | 深圳市蓝鲸智联科技股份有限公司 | Intelligent brake auxiliary system |
| CN116946089B (en) * | 2023-09-20 | 2024-01-02 | 深圳市蓝鲸智联科技股份有限公司 | Intelligent brake auxiliary system |
| CN117710909A (en) * | 2024-02-02 | 2024-03-15 | 多彩贵州数字科技股份有限公司 | Rural road intelligent monitoring system based on target detection and instance segmentation |
| CN117710909B (en) * | 2024-02-02 | 2024-04-12 | 多彩贵州数字科技股份有限公司 | Rural road intelligent monitoring system based on target detection and instance segmentation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116176576B (en) | 2023-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116176576B (en) | Trailer collision intelligent early warning system and method based on artificial intelligence | |
| CN107253482B (en) | A kind of Driving safety assistant system based on pavement image analysis | |
| CN110281893B (en) | Emergency braking system and method and semitrailer | |
| CN107782727B (en) | Fusion-based wet pavement detection | |
| CN105984447B (en) | To anticollision automatic emergency brake system and method before vehicle based on machine vision | |
| CN109318893B (en) | Safe driving assistance method and system based on license plate pixel height change | |
| CN104290753B (en) | A kind of vehicle motion state tracking prediction device in front of the vehicle and its Forecasting Methodology | |
| CN110203202B (en) | Lane changing auxiliary early warning method and device based on driver intention recognition | |
| CN105263785B (en) | Vehicle control system | |
| CN106427998A (en) | Control method for avoiding collision during emergent lane changing of vehicle in high-speed state | |
| CN105644557A (en) | Braking and steering assisting system and method considering collision avoidance intention of driver | |
| CN110077398B (en) | Risk handling method for intelligent driving | |
| CN104260723B (en) | A kind of front vehicle motion state tracking prediction meanss and Forecasting Methodology | |
| CN106428002B (en) | A kind of anti-collision prewarning apparatus and method based on vehicle active safety | |
| CN110588623B (en) | Large automobile safe driving method and system based on neural network | |
| CN108045376B (en) | A kind of control method for vehicle based on road surface adhesive ability, system and automobile | |
| CN108189763A (en) | A kind of analysis method of driver's driving behavior and special intelligent vehicular rear mirror | |
| CN204432641U (en) | Based on the crashproof automatic emergency brake system of vehicle forward direction and this vehicle of machine vision | |
| US20180043793A1 (en) | Smart cruise control and adas for range extension | |
| CN105263768A (en) | Vehicle control system | |
| CN105438183B (en) | A kind of recognition methods of the radical driving condition of driver | |
| CN108032809B (en) | Reverse side auxiliary system and data fusion and control method thereof | |
| CN113147752B (en) | Unmanned method and system | |
| CN112298132A (en) | Vehicle autonomous emergency braking control system and control method | |
| CN109080608B (en) | Braking force control method for emergency braking of unmanned vehicle on rainy, snowy and slippery road surface |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |