CN115691157A - Vehicle-road cooperation based curve road section vehicle speed early warning method and system - Google Patents
Vehicle-road cooperation based curve road section vehicle speed early warning method and system Download PDFInfo
- Publication number
- CN115691157A CN115691157A CN202211137799.XA CN202211137799A CN115691157A CN 115691157 A CN115691157 A CN 115691157A CN 202211137799 A CN202211137799 A CN 202211137799A CN 115691157 A CN115691157 A CN 115691157A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- speed
- early warning
- curve
- time
- 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.)
- Pending
Links
Images
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention relates to a vehicle-road cooperation based curve road section vehicle speed early warning method, which comprises the following steps: the method comprises the following steps of firstly, obtaining the current running state of a vehicle, vehicle parameters and curve road information; step two, calculating the rollover critical speed and the sideslip critical speed of the vehicle according to the acquired information, and taking the smaller value V of the rollover critical speed and the sideslip critical speed th ＝min{V r ,V s Using the speed as the threshold value of the over-bending speed; step three, after the vehicle passes through a front weighing sensor of the curve, the vehicle is about to drive into the curve, and at the moment, if the vehicle speed exceeds a curve passing speed threshold V th Sending out early warning to the driver or taking active control measures; and step four, when the vehicle runs on a curve, the Kalman filter is used for predicting the running speed and the acceleration of the vehicle in real time. The vehicle-road cooperation technology and the 5G-V2X technology improve the early warning and active control of the systemThe system can effectively ensure the safety of the large-scale vehicle running on the curve.
Description
Technical Field
The invention belongs to the technical field of vehicle safety early warning, and particularly relates to a curve road section vehicle speed early warning method and system based on vehicle-road cooperation.
Background
Compared with a small automobile, a large-sized vehicle has the characteristics of high gravity center and large mass, and is easy to cause side-turning or side-slipping accidents under the action of lateral acceleration when running on a curve. The statistical data of the traffic administration of the ministry of public security shows that the casualties and property losses caused by the accident of the curve are more serious than those of other road sections. Therefore, the vehicle risk situation is accurately predicted, the prediction result is timely sent to a driver or a vehicle active control device, effective control measures are taken, and the method has important significance for avoiding accidents.
The existing curve road section vehicle speed early warning system generally selects indexes such as a roll angle, a transverse load transfer ratio and the like, and determines when to give early warning information to a driver by comparing a threshold value with a real-time vehicle state index value. But providing early warning when a risk is present is likely to cause an accident due to the driver's lack of time to operate. Therefore, accurately predicting the vehicle risk development situation and taking control measures in advance have important theoretical and practical significance for effectively avoiding accidents. The vehicle-road cooperation technology and the 5G-V2X technology can effectively improve the prediction precision, reduce the communication delay and effectively improve the accuracy and timeliness of the existing system.
Disclosure of Invention
The invention aims to solve the technical problems and further provides a curve road section vehicle speed early warning method and system based on vehicle-road cooperation.
The invention relates to a vehicle-road cooperation based curve road section vehicle speed early warning method, which comprises the following steps:
the method comprises the following steps of firstly, obtaining the current running state of a vehicle, vehicle parameters and curve road information;
step two, calculating the rollover critical speed and the sideslip critical speed of the vehicle according to the acquired information, and taking the smaller value V of the rollover critical speed and the sideslip critical speed th ＝min{V r ,V s Using the speed as the threshold value of the over-bending speed;
step three, after the vehicle passes through a front weighing sensor of the curve, the vehicle is about to drive into the curve, and at the moment, if the vehicle speed exceeds a curve passing speed threshold V th Sending out early warning to the driver or taking active control measures;
step four, when the vehicle runs on a curve, the driving speed and the acceleration of the vehicle are predicted in real time by using a Kalman filter;
and step five, judging whether the rollover or sideslip risk exists or not in real time according to the predicted speed and the over-bending speed threshold value, and giving out early warning or taking active control measures when the risk exists.
In the first step, the current state information of the vehicle comprises a vehicle position, and the vehicle parameters comprise a vehicle wheel track, a vehicle body height and a vehicle mass; the curve information includes a curve radius, a curve superelevation, and an attachment coefficient.
In the second step, the calculation formulas of the rollover critical speed and the sideslip critical speed of the vehicle are respectively as follows:
in the formula: b-track, m; h-height of center of gravity, m; i-bend superelevation,%; g-gravitational acceleration, is taken to be 9.8m/s 2 (ii) a R-bend radius, m;
-the coefficient of adhesion.
In the second step, according to the historical data, the three-dimensional characteristic vector x of the wheel track, the height of the vehicle body and the mass of the vehicle is concentrated h And its corresponding height H of center of gravity h Estimating the gravity center height H corresponding to the current three-dimensional characteristic vector x by adopting a KNN regression model;
adopting K characteristic vectors which are nearest to the Euclidean distance of the current characteristic vector in the historical data set and corresponding gravity center height, wherein the gravity center height estimation formula is as follows:
in the formula: omega (x, x) h ) -feature vectors x and x h Weight in between;
the Euclidean distance formula is as follows:
in the formula: x (i) -current feature vector ith attribute value; x is the number of h (i) -the h characteristic vector in the history data set is the i attribute value; m is the number of samples in the historical dataset; the weight calculation formula is as follows:
the adhesion coefficient of the dry pavement is 0.6-0.8, and the adhesion coefficient of the wet pavement is 0.45-0.7.
In the fourth step, the kalman filtering equation of state is:
the observation equation is:
P k+1|k ＝AP k|k A T +Q (7)
in the formula: z k+1 -a motion state vector at time k + 1; z k -a motion state vector at time k; a-state transition matrix; ω -process noise; s k -the time k measurement value; h is a conversion matrix, and is taken as a constant C; v-observed noise.
The Kalman filter is structurally divided into a time updating part and a state updating part; the Kalman filter specifically operates as follows:
(1) Defining the state variables:
in the formula: (X) k Y k ) -the position coordinates of the vehicle at time k;
-the lateral and longitudinal speed of the vehicle at time k;
-lateral, longitudinal acceleration of the vehicle at time k;
can be prepared from (X) k Y k ) Respectively solving the first derivative and the second derivative to obtain the final product.
(2) Kalman filter time update process:
a prediction step:
and (3) covariance prediction:
P k+1|k ＝AP k|k A T +Q (10)
(3) Kalman filter state update process:
kalman gain:
K k+1 ＝P k+1|k H T /[HP k+1|k H T +R] -1 (11)
an estimation step:
and (3) estimating covariance:
P k|k ＝[I-K k H]P k+1|k (13)
in the formula:
-a priori state estimate at time k + 1; a-state transition matrix; z k|k -a thick state estimate at time k; p is k+1|k -prior estimated covariance at time k + 1;P k|k -a posteriori estimate of the covariance at time k; q-process excitation noise covariance, taken as eye (6); k k+1 -a kalman gain; h is a conversion matrix, and is taken as a constant C; r is the covariance of the measured noise, and is taken as 1;
-a posterior state estimate at time k + 1; s k+1 -the measurement at time k + 1; p k+1|k+1 -a posteriori estimated covariance at time k + 1; i-identity matrix.
Initialization Z 0 ＝[0,0,0,0,0,0] T ，P 0 = eye (6); the state transition matrix a is:
in step four, the vehicle speed is predicted as follows:
in the formula:
the lateral and longitudinal speed of the vehicle at time k.
In the fourth step, the early warning and active control measures are as follows: when the predicted speed V (t) > V th And meanwhile, due to the information interoperability of the vehicle-road cooperative system, the roadside electronic display screen also displays the alarm of 'the speed of the vehicle is too fast' to the driver. If the driver does not take effective measures and the vehicle speed is still in a dangerous range, the vehicle electronic control unit sends an electric signal for reducing power to the engine and reduces the vehicle speed through vehicle active control; when the predicted speed V (t) < V th And in time, the vehicle is determined not to have risks, measures are not taken, and the vehicle state is continuously monitored.
The invention also relates to a system for implementing the curve road section vehicle speed early warning method based on vehicle-road cooperation, which comprises a vehicle information acquisition module, a cloud speed prediction module, a cloud early warning control module, a wireless communication module, an in-vehicle early warning module and a roadside early warning module.
The vehicle information acquisition module is used for acquiring vehicle running state information and vehicle parameters, wherein the vehicle position is acquired by a vehicle-mounted GPS, the vehicle mass is acquired by a weighing sensor arranged in front of a curve, the vehicle wheel track and the vehicle body height are acquired by road side laser radar measurement, the information is sent to a speed prediction module and an early warning control module of a road side unit through a wireless communication module, and the speed prediction module calculates a curve passing speed threshold value V through vehicle information and curve pre-storing information of a built-in RSU database th And predicting the vehicle speed V (t) in real time, wherein the cloud early warning control module is used for comparing V th And the size of V (t), when V (t) > V th And the cloud early warning control module sends an early warning instruction to the in-vehicle early warning module and the roadside early warning module.
Advantageous effects
The invention provides a curve road section vehicle speed early warning method and system based on a vehicle-road cooperation technology, which utilize the vehicle-road cooperation technology and a 5G-V2X technology to realize real-time information communication between road-side equipment and vehicle-mounted equipment in the aspects of vehicle running state, road environment, driver behavior and the like, improve the accuracy and timeliness of system early warning and active control, and can effectively ensure the safety of large vehicles running in a curve.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a Kalman filtering vehicle speed prediction schematic diagram in accordance with the present invention;
FIG. 3 is a schematic diagram of the system of the present invention.
Detailed Description
The present embodiment will be specifically described below with reference to fig. 1 to 3.
The invention relates to a vehicle speed early warning system for a curve road section based on vehicle-road cooperation, which comprises the following steps of:
the method comprises the following steps: acquiring the current running state of a vehicle, vehicle parameters and curve road information;
the current state information of the vehicle comprises the position of the vehicle, and the parameters of the vehicle comprise the wheel track of the vehicle, the height of the vehicle body and the mass of the vehicle; the curve information includes a curve radius, a curve superelevation, and an adhesion coefficient.
Step two: calculating the critical speed of vehicle rollover and the critical speed of vehicle sideslip according to the acquired vehicle information and the acquired curve road information, and taking the smaller value V of the critical speed of vehicle rollover and the critical speed of vehicle sideslip th ＝min{V r ,V s As an over-bending speed threshold;
the calculation formulas of the rollover critical speed and the sideslip critical speed of the vehicle are respectively as follows:
in the formula: b-track, m; h-height of center of gravity, m; i-bend superelevation,%; g-gravitational acceleration, is taken to be 9.8m/s 2 (ii) a R is the radius of the curve, m;
-the adhesion coefficient.
According to the historical data, the three-dimensional characteristic vector x of the wheel track, the height of the vehicle body and the mass of the vehicle is concentrated h And its corresponding height H of center of gravity h And estimating the gravity center height H corresponding to the current three-dimensional characteristic vector x by adopting a KNN regression model.
Adopting K characteristic vectors which are nearest to the Euclidean distance of the current characteristic vector in the historical data set and corresponding gravity center heights, wherein the gravity center height estimation formula is as follows:
in the formula: omega (x, x) h )—-eigenvectors x and x h Weight in between; the euclidean distance formula is as follows:
in the formula: x (i) -the current feature vector ith attribute value; x is the number of h (i) -the h-th feature vector in the history dataset is the i-th attribute value; m-number of samples in historical dataset.
The weight calculation formula is as follows:
the adhesion coefficient is 0.6-0.8 when the road surface is dry and 0.45-0.7 when the road surface is wet.
Step three: after the vehicle passes through the front weighing sensor of the curve, the vehicle is about to drive into the curve, and at the moment, if the vehicle speed exceeds a curve passing speed threshold V th And an early warning is sent to the driver or an active control measure is taken.
Step four: when the vehicle runs on a curve, the Kalman filter is used for predicting the running speed and the acceleration of the vehicle in real time.
In step four, the kalman filter state equation is:
Z k+1 ＝AZ k +ω (6)
the observation equation is:
S k ＝HZ k +v (7)
in the formula: z k+1 -a motion state vector at time k + 1; z k -a motion state vector at time k; a-state transition matrix; omega-process noise; s k -a measurement value at time k; h is a conversion matrix, and is taken as a constant C; v-observed noise.
The Kalman filter is structurally divided into a time updating part (prediction) and a state updating part (correction); the Kalman filter specifically operates as follows:
(2) Defining the state variables:
in the formula: (X) k Y k ) -the position coordinates of the vehicle at time k;
-the lateral and longitudinal speed of the vehicle at time k;
-lateral, longitudinal acceleration of the vehicle at time k;
can be prepared from (X) k Y k ) The first derivative and the second derivative are obtained respectively.
(2) Kalman filter time update process:
a prediction step:
and (3) covariance prediction:
P k+1|k ＝AP k|k A T +Q (10)
(3) Kalman filter state update process:
kalman gain:
K k+1 ＝P k+1|k H T /[HP k+1|k H T +R] -1 (11)
an estimation step:
and (3) estimating covariance:
P k|k ＝[I-K k H]P k+1|k (13)
in the formula：
-a priori state estimate at time k + 1; a-state transition matrix; z k|k -a thick state estimate at time k; p k+1|k -prior estimated covariance at time k + 1; p k|k -a posteriori estimate of covariance at time k; q-process excitation noise covariance, taken as eye (6); k k+1 -a kalman gain; h is a conversion matrix, and is taken as a constant C; r is the covariance of the measured noise, and is taken as 1;
-an estimate of the posterior state at time k + 1; s k+1 -the measurement at time k + 1; p is k+1|k+1 -a posteriori estimated covariance at time k + 1; i-identity matrix.
Initialization Z 0 ＝[0,0,0,0,0,0] T ，P 0 = eye (6); the state transition matrix a is:
step five: and judging whether rollover or sideslip risks exist or not in real time according to the predicted speed and the over-bending speed threshold, and giving out early warning or taking active control measures when the risks exist.
The predicted vehicle speed is as follows:
in the formula:
the lateral and longitudinal speed of the vehicle at time k.
The early warning and active control measures are as follows: when the predicted speed V (t) > V th When the driver is driving, the display in the vehicle gives an audible and visual alarm of 'too fast speed' to the driver, and simultaneously, the driver is driven by the vehicleIn cooperation with the information interoperability of the system, the roadside electronic display screen also displays an alarm of 'too fast speed' to the driver. If the driver does not take effective measures and the vehicle speed is still in a dangerous range, an Electronic Control Unit (ECU) of the vehicle sends an electric signal for reducing power to the engine, and the vehicle speed is reduced through active control of the vehicle. When the predicted speed V (t) < V th And in time, the vehicle is determined not to have risks, measures are not taken, and the vehicle state is continuously monitored.
The invention also relates to a vehicle-road cooperation based curve road section vehicle speed early warning system which comprises a vehicle information acquisition module, a cloud speed prediction module, a cloud early warning control module, a wireless communication module, an in-vehicle early warning module and a roadside early warning module.
The vehicle information acquisition module is used for acquiring vehicle running state information and vehicle parameters, wherein the vehicle position is acquired by a vehicle-mounted GPS, the vehicle mass is acquired by a weighing sensor arranged in front of a curve, the vehicle track and the vehicle body height are acquired by road side laser radar measurement, the information is transmitted to a speed prediction module and an early warning control module of a Road Side Unit (RSU) through a wireless communication module (DSRC technology), and the speed prediction module calculates a curve passing speed threshold value V through the vehicle information and curve information (curve radius, curve superelevation and adhesion coefficient) prestored in an RSU built-in database th And predicting the vehicle speed V (t) in real time, wherein the cloud early warning control module is used for comparing V th And V (t) is greater than V th And the cloud early warning control module sends an early warning instruction to the in-vehicle early warning module and the roadside early warning module.
Examples
In this embodiment, a typical large bus is selected as the test vehicle, and the structural parameters are as follows:
TABLE 1 typical bus structural parameters
When the vehicle runs at a speed of 50km/h on a dry road surface curve with the radius of 300m and the ultrahigh of 5 percent:
the optimal K value of the KNN regression model obtained by system parameter adjustment is 5, and the gravity center heights corresponding to 5 historical characteristic vectors which are closest to the current characteristic vector Euclidean distance are as follows:
TABLE 2 sample parameters
The weighted sum current vehicle center of gravity height is H =1.29m.
Critical speed of side turning is expressed by formula
Calculated V r The critical speed of sideslip is expressed by the formula of =51km/h
Calculated V s =49km/h, critical speed of bending over V th ＝49km/h。
The over-curve speed threshold value can be calculated after the vehicle passes through a weighing sensor in front of the curve, before the vehicle enters the curve, if the vehicle speed exceeds 49km/h, the in-vehicle display gives a sound and picture alarm of 'the vehicle speed is too fast' to the driver, and meanwhile, the roadside electronic display screen also gives an alarm of 'the vehicle speed is too fast' to the driver. If the driver does not take effective measures, the cloud early warning control module sends an active control instruction signal to the ECU, the ECU sends an electric signal for reducing power to the engine, and the vehicle speed is reduced through vehicle active control.
After the vehicle enters a curve, the Kalman filter predicts the vehicle speed in real time, judges when the predicted speed V (t) exceeds 49km/h, if the predicted vehicle speed reaches 49km/h at a certain moment, the early warning is carried out 1s in advance, and if the driver does not take effective measures after 0.5s, the power is reduced through the system to actively control the vehicle speed.
The above-mentioned disclosure is only a preferred embodiment of the present invention, and is not intended to limit the embodiments of the present invention, and those skilled in the art can easily make various changes and modifications according to the main concept and spirit of the present invention, so the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A vehicle-road cooperation based curve road section vehicle speed early warning method is characterized by comprising the following steps:
the method comprises the following steps of firstly, obtaining the current running state of a vehicle, vehicle parameters and curve road information;
step two, calculating the rollover critical speed and the sideslip critical speed of the vehicle according to the acquired information, and taking the smaller value V of the rollover critical speed and the sideslip critical speed th ＝min{V r ,V s As an over-bending speed threshold;
step three, after the vehicle passes through a front weighing sensor of the curve, the vehicle is about to drive into the curve, and at the moment, if the vehicle speed exceeds a curve passing speed threshold V th Sending out early warning to the driver or taking active control measures;
step four, when the vehicle runs on a curve, the driving speed and the acceleration of the vehicle are predicted in real time by using a Kalman filter;
and step five, judging whether the risk of side turning or sideslip exists or not in real time according to the predicted speed and the over-bending speed threshold, and giving out early warning or taking active control measures when the risk exists.
2. The curve road section vehicle speed early warning method based on vehicle-road cooperation as claimed in claim 1, wherein in the first step, the current state information of the vehicle comprises vehicle position, and the vehicle parameters comprise vehicle wheel distance, vehicle height and vehicle mass; the curve information includes a curve radius, a curve superelevation, and an adhesion coefficient.
3. The curve road section vehicle speed early warning method based on vehicle-road cooperation as claimed in claim 1, wherein in the second step, the calculation formulas of the vehicle rollover critical speed and the vehicle sideslip critical speed are respectively as follows:
in the formula: b-track, m; h-height of center of gravity, m; i-bend superelevation,%; g-gravitational acceleration, is taken to be 9.8m/s 2 (ii) a R-bend radius, m;
-the coefficient of adhesion.
4. The curve road section vehicle speed early warning method based on vehicle-road cooperation as claimed in claim 3, wherein in the second step, the vehicle wheel track, the vehicle height and the vehicle mass three-dimensional feature vector x are concentrated according to historical data h And its corresponding gravity center height H h Estimating the gravity center height H corresponding to the current three-dimensional characteristic vector x by adopting a KNN regression model;
adopting K characteristic vectors which are nearest to the Euclidean distance of the current characteristic vector in the historical data set and corresponding gravity center height, wherein the gravity center height estimation formula is as follows:
in the formula: omega (x, x) h ) -feature vectors x and x h Weight in between;
the euclidean distance formula is as follows:
in the formula: x (i) -current feature vector ith attribute value; x is a radical of a fluorine atom h (i) -the h-th feature vector in the history dataset is the i-th attribute value; m is the number of samples in the historical dataset; the weight calculation formula is as follows:
the adhesion coefficient of the dry pavement is 0.6-0.8, and the adhesion coefficient of the wet pavement is 0.45-0.7.
5. The curve road section vehicle speed early warning method based on vehicle-road cooperation according to claim 1, wherein in the fourth step, a Kalman filtering state equation is as follows:
Z k+1 ＝AZ k +ω (6)
the observation equation is:
S k ＝HZ k +v (7)
in the formula: z k+1 -a motion state vector at time k + 1; z is a linear or branched member k -a motion state vector at time k; a-state transition matrix; ω -process noise; s k -the time k measurement value; h is a conversion matrix, and is taken as a constant C; v-observed noise.
The Kalman filter is structurally divided into a time updating part and a state updating part; the Kalman filter specifically operates as follows:
(1) Defining the state variables:
in the formula: (X) k Y k ) -the position coordinates of the vehicle at time k;
-the lateral and longitudinal speed of the vehicle at time k;
-lateral, longitudinal acceleration of the vehicle at time k;
can be prepared from (X) k Y k ) Respectively solving first and second derivatives;
(2) Kalman filter time update process:
a prediction step:
and (3) covariance prediction:
P k+1|k ＝AP k|k A T +Q (10)
(3) Kalman filter state update process:
kalman gain:
K k+1 ＝P k+1|k H T /[HP k+1|k H T +R] -1 (11)
an estimation step:
and (3) estimating covariance:
P k|k ＝[I-K k H]P k+1|k (13)
in the formula:
-a priori state estimate at time k + 1; a-state transition matrix; z is a linear or branched member k|k -a thick state estimate at time k; p k+1|k -prior estimated covariance at time k + 1; p is k|k -a posteriori estimate of the covariance at time k; q-process excitation noise covariance, taken as eye (6); k k+1 -a kalman gain; h is a conversion matrix, and is taken as a constant C; r is the covariance of the measured noise, and is taken as 1;
-an estimate of the posterior state at time k + 1; s. the k+1 When k +1Engraving a measured value; p k+1|k+1 -a posteriori estimated covariance at time k + 1; i-identity matrix.
Initialization Z 0 ＝[0,0,0,0,0,0] T ，P 0 = eye (6); the state transition matrix a is:
6. a curve road section vehicle speed early warning method based on vehicle-road cooperation according to claim 1, characterized in that in step four, the vehicle speed is predicted as follows:
in the formula:
the lateral and longitudinal speed of the vehicle at time k.
7. The curve road section vehicle speed early warning method based on vehicle-road cooperation as claimed in claim 1, wherein in step four, the early warning and active control measures are as follows: when the predicted speed V (t) > V th And meanwhile, due to the information interoperability of the vehicle-road cooperative system, the roadside electronic display screen also displays the alarm of 'the speed of the vehicle is too fast' to the driver. If the driver does not take effective measures and the vehicle speed is still in a dangerous range, the vehicle electronic control unit sends an electric signal for reducing power to the engine and reduces the vehicle speed through vehicle active control; when the predicted speed V (t) < V th And in time, the vehicle is determined not to have risks, measures are not taken, and the vehicle state is continuously monitored.
8. A system for implementing the vehicle speed early warning method for the curved road section based on the vehicle-road cooperation as claimed in any one of claims 1 to 7 is characterized by comprising a vehicle information acquisition module, a cloud speed prediction module, a cloud early warning control module, a wireless communication module, an in-vehicle early warning module and a roadside early warning module.
9. The vehicle-road cooperation-based curve road section vehicle speed early warning system as claimed in claim 8, wherein the vehicle information collection module is used for collecting vehicle driving state information and vehicle parameters, wherein the vehicle position is obtained by an on-board GPS, the vehicle mass is obtained by a weighing sensor arranged in front of a curve, the vehicle wheel distance and the vehicle body height are obtained by road-side laser radar measurement, the information is sent to a speed prediction module and an early warning control module of a road-side unit through the wireless communication module, and the speed prediction module calculates the over-curve speed threshold value V through the vehicle information and curve information prestored in an RSU built-in database th And predicting the vehicle speed V (t) in real time, wherein the cloud early warning control module is used for comparing V th And V (t) is greater than V th And the cloud early warning control module sends an early warning instruction to the in-vehicle early warning module and the roadside early warning module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211137799.XA CN115691157A (en) | 2022-09-19 | 2022-09-19 | Vehicle-road cooperation based curve road section vehicle speed early warning method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211137799.XA CN115691157A (en) | 2022-09-19 | 2022-09-19 | Vehicle-road cooperation based curve road section vehicle speed early warning method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115691157A true CN115691157A (en) | 2023-02-03 |
Family
ID=85061956
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211137799.XA Pending CN115691157A (en) | 2022-09-19 | 2022-09-19 | Vehicle-road cooperation based curve road section vehicle speed early warning method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115691157A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116252708A (en) * | 2023-05-15 | 2023-06-13 | 西格玛智能装备(山东)有限公司 | Driving collision early warning system suitable for wind power special-purpose vehicle |
| CN117423236A (en) * | 2023-12-18 | 2024-01-19 | 暨南大学 | Narrow curve vehicle scheduling method based on temporary avoidance of parking space |
-
2022
- 2022-09-19 CN CN202211137799.XA patent/CN115691157A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116252708A (en) * | 2023-05-15 | 2023-06-13 | 西格玛智能装备(山东)有限公司 | Driving collision early warning system suitable for wind power special-purpose vehicle |
| CN117423236A (en) * | 2023-12-18 | 2024-01-19 | 暨南大学 | Narrow curve vehicle scheduling method based on temporary avoidance of parking space |
| CN117423236B (en) * | 2023-12-18 | 2024-03-19 | 暨南大学 | Narrow curve vehicle scheduling method based on temporary avoidance of parking space |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115691157A (en) | Vehicle-road cooperation based curve road section vehicle speed early warning method and system | |
| US9604648B2 (en) | Driver performance determination based on geolocation | |
| US11100801B2 (en) | Utilizing sensors to detect hazard from other vehicle while driving | |
| CN106240458B (en) | A kind of vehicular frontal impact method for early warning based on vehicle-mounted binocular camera | |
| EP3118079B1 (en) | Road surface submergence estimation device | |
| CN102390320B (en) | Vehicle anti-collision early warning system based on vehicle-mounted sensing network | |
| US9528842B2 (en) | Method and device for ascertaining a source of danger on a travel route | |
| EP2507587B1 (en) | Method and system for mapping road conditions by means of terminals | |
| CN111667705B (en) | Rollover prevention early warning system for heavy truck at bend | |
| CN111383465B (en) | Highway danger early warning and speed control system based on car networking | |
| US20110282627A1 (en) | Method and server of determining advisory safety speed based on road surface states and statistical traffic conditions | |
| CN104867356A (en) | Vehicle threat assessment system based on DSRC and Telematics | |
| US9812014B2 (en) | Driving assistance system for identifying stopping points | |
| CN105313769A (en) | Vehicle active forewarning method, system and device based on millimeter wave radar | |
| JP6892590B2 (en) | Driving support system, driving support device, and driving support method | |
| KR101102818B1 (en) | System for providing safety gap and schematic display using real-time safety distance bar considering relative velocity algorithm | |
| CN104040607B (en) | Synchronous driving assist system and synchronous travel assist system | |
| CN103568991A (en) | Auxiliary driving safety system and method | |
| CN104742845A (en) | Dynamic early warning system for automobile rear-end collision preventing | |
| CN110040146A (en) | A kind of vehicle rollover method for early warning and system considering road surface Parameters variation | |
| CN108482365A (en) | A kind of vehicle flat tire transient state vehicle body emergency flight control system and method | |
| CN108417065B (en) | Pothole road surface early warning method based on smart phone and navigation application | |
| CN113232643A (en) | Self-adaptive auxiliary driving system and method based on road surface environment | |
| CN109383371A (en) | A kind of ADAS product driveway deviation alarming system | |
| CN114241750A (en) | Vehicle-road cooperative information interaction system and method based on intelligent road traffic risk early warning method |
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 |