CN117227713B - Vehicle safety obstacle avoidance method and system and electronic equipment - Google Patents
Vehicle safety obstacle avoidance method and system and electronic equipment Download PDFInfo
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Abstract
The invention discloses a vehicle safety obstacle avoidance method, a vehicle safety obstacle avoidance system and electronic equipment, and relates to the technical field of general vehicle control. According to the vehicle safety obstacle avoidance method provided by the invention, the vehicle-mounted sensor of the self-vehicle is utilized to obtain the information of the speed of the front vehicle and the relative distance between two vehicles with unknown measurement noise, and the information is reconstructed through a designed self-adaptive sequential inspection filtering method to obtain more accurate information of the speed of the front vehicle and the relative distance. And establishing a critical safety distance index considering the road adhesion coefficient according to the information, and activating a steering decision instruction according to the index. When a steering instruction is received, the self-vehicle generates an obstacle avoidance path and obtains an expected steering wheel angle according to a reverse dynamic model of the steering system so as to control the self-vehicle to complete active collision avoidance, so that the technical blank of information acquisition under the condition that the measurement noise of the current sensor is unknown can be filled, the adaptation degree of the self-vehicle to different road surfaces of the vehicle is improved, the measurement precision and the traffic efficiency are further improved, and the problem of rear-end collision accidents is avoided.
Description
Technical Field
The invention relates to the technical field of general vehicle control, in particular to a vehicle safety obstacle avoidance method, a system and electronic equipment for fusing road surface information under the condition that a vehicle-mounted sensor measures noise is unknown.
Background
Advanced driving assistance systems, in particular active collision avoidance systems, can be used to avoid obstacles in dangerous situations by planning the path, thus improving the safety of the vehicle. Active collision avoidance systems typically determine whether to perform path planning based on the relative distance and speed of the preceding vehicle, and these signals may be obtained by radar. In the past studies, it was assumed that the measurement noise of the sensor was known, and radar information was directly input to the active collision avoidance system through simple processing. However, uncertainty in measurement noise may reduce measurement accuracy, may cause a lane change to occur prematurely, thereby reducing traffic efficiency, or may cause a rear-end collision accident due to a lane change too late.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a vehicle safety obstacle avoidance method, a vehicle safety obstacle avoidance system and electronic equipment.
In order to achieve the above object, the present invention provides the following solutions:
a vehicle safety obstacle avoidance method, comprising:
acquiring the speed of a front vehicle with unknown measurement noise data and the relative distance between the front vehicle and the front vehicle by using a vehicle-mounted sensor of the self vehicle, and acquiring the speed of the self vehicle by using a local area network bus of the self vehicle;
constructing a state equation and a measurement equation of information reconstruction, and designing a self-adaptive sequential test filtering method;
measuring the speed and the relative distance of the front vehicle with unknown measurement noise based on the state equation and the measurement equation by adopting the designed self-adaptive posterior filtering method to obtain measurement data, carrying out on-line self-adaptive design of a noise matrix, and obtaining the final speed and the final relative distance of the front vehicle based on the measurement data by adopting a minimum mean square error filtering algorithm;
determining a critical safety distance based on the final front vehicle speed, the speed of the own vehicle, a vehicle response time, a braking force increase time, and a minimum holding distance of the own vehicle from the front vehicle, taking into consideration a road surface adhesion coefficient;
activating a steering decision command when the final relative distance is less than the critical safety distance;
constructing a high-order polynomial path planning algorithm, and generating a lane change path based on the high-order polynomial path planning algorithm;
obtaining a desired steering wheel angle according to the steering inverse dynamics model and the variable road diameter;
and controlling the self-vehicle to complete steering and obstacle avoidance based on the expected steering wheel angle.
Optionally, the designed adaptive posterior filtering method is adopted, measurement data is obtained by measuring the speed and the relative distance of the front vehicle with unknown measurement noise based on the state equation and the measurement equation, on-line adaptive design of a noise matrix is carried out, and the final speed and the final relative distance of the front vehicle are obtained by adopting a minimum mean square error filtering algorithm based on the measurement data, and the method specifically comprises the following steps:
determining prior information of the speed of the front vehicle and the relative distance according to the state equation; the prior information comprises a prior estimated value and an error covariance of the prior estimated value;
determining posterior information of the speed and the relative distance of the front vehicle according to the measurement equation; the posterior information comprises a posterior estimated value and an error covariance of the posterior estimated value;
and carrying out continuous iterative circulation based on the prior information and the posterior information, carrying out online self-adaptive design of a measurement noise matrix, and obtaining final front vehicle speed and final relative distance based on a minimum mean square error filtering algorithm.
Optionally, the a priori estimate and the error covariance of the a priori estimate are expressed as:
；
；
in the method, in the process of the invention,is a priori state->Weight for one-step prediction, +.>Status of sigma point calculated for using state equation, +.>Error covariance for a priori estimate, +.>Weights updated for covariance, +.>For the process noise covariance matrix,/>For the dimension of the state vector, +.>Dimension number for state vector, +.>，/>Is the sampling time.
Optionally, the posterior estimate and the error covariance of the posterior estimate are expressed as:
；
；
in the method, in the process of the invention,for posterior state, add->Error covariance for posterior estimate, +.>For filtering gain +.>For the estimated measurement output +.>Covariance of the measurement output for estimation, +.>For a measured variable at unknown noise, +.>Is a transpose of the matrix.
Optionally, the higher order polynomial path planning algorithm is a seventh order polynomial function.
Optionally, the variable road diameter generated by using the seventh-order polynomial function is expressed as:
；
in the method, in the process of the invention,for changing road diameter function->For varying the lateral distance of the track->For the distance in x-direction in the coordinate system established with the set origin in the lane-change path,/->Is the longitudinal distance of the lane change.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the vehicle safety obstacle avoidance method provided by the invention, the vehicle-mounted sensor of the self-vehicle is utilized to obtain the information of the speed of the front vehicle and the relative distance between two vehicles with unknown measurement noise, and the information is reconstructed through a designed self-adaptive sequential inspection filtering method to obtain more accurate information of the speed of the front vehicle and the relative distance. And establishing a critical safety distance index considering the road adhesion coefficient according to the information, and activating a steering decision instruction according to the index. When a steering instruction is received, the self-vehicle generates an obstacle avoidance path and obtains an expected steering wheel angle according to a reverse dynamic model of the steering system so as to control the self-vehicle to complete active collision avoidance, so that the technical blank of information acquisition under the condition that the measurement noise of the current sensor is unknown can be filled, the adaptation degree of the self-vehicle to different road surfaces of the vehicle is improved, the measurement precision and the traffic efficiency are further improved, and the problem of rear-end collision accidents is avoided.
Further, the invention provides a vehicle safety obstacle avoidance system, and the vehicle safety obstacle avoidance method is applied; the system comprises:
the data acquisition module is used for acquiring the speed of the front vehicle with unknown measurement noise data and the relative distance between the front vehicle and the front vehicle by using the vehicle-mounted sensor of the self vehicle, and acquiring the speed of the self vehicle by using the local area network bus of the self vehicle;
the equation and filtering construction module is used for constructing a state equation and a measurement equation of information reconstruction and designing a self-adaptive sequential verification filtering method;
the noise filtering module is used for measuring the speed and the relative distance of the front vehicle with unknown measurement noise based on the state equation and the measurement equation to obtain measurement data, performing online self-adaptive design of a noise matrix, and obtaining the final speed and the final relative distance of the front vehicle based on the measurement data by adopting a minimum mean square error filtering algorithm;
a critical safe distance determining module for determining a critical safe distance based on the final front vehicle speed, the speed of the own vehicle, a vehicle response time, a braking force increasing time, and a minimum holding distance between the own vehicle and the front vehicle, taking into consideration a road surface attachment coefficient;
the steering decision instruction activation module is used for activating a steering decision instruction when the final relative distance is smaller than the critical safety distance;
the variable road path generation module is used for constructing a high-order polynomial path planning algorithm and generating a variable road path based on the high-order polynomial path planning algorithm;
the steering wheel angle determining module is used for obtaining a desired steering wheel angle according to the steering inverse dynamics model and the variable road diameter;
and the steering obstacle avoidance module is used for controlling the self-vehicle to finish steering obstacle avoidance based on the expected steering wheel angle.
Still further, the present invention also provides an electronic device including:
a memory for storing a computer program;
and the processor is connected with the memory and used for calling and executing the computer program so as to implement the vehicle safety obstacle avoidance method.
The technical effects achieved by the two implementation structures provided by the invention are the same as those achieved by the vehicle safety obstacle avoidance method provided by the invention, so that the description is omitted here.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a vehicle safety obstacle avoidance method provided by the invention;
fig. 2 is a schematic diagram of an implementation of obstacle avoidance by using the vehicle safety obstacle avoidance method provided by 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.
The invention aims to provide a vehicle safety obstacle avoidance method, a system and electronic equipment,
in order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the vehicle safety obstacle avoidance method provided by the invention includes:
step 100: the speed of the front vehicle with unknown measurement noise data and the relative distance between the front vehicle and the front vehicle are obtained by using the vehicle-mounted sensor of the self vehicle, and the speed of the self vehicle is obtained by using the local area network bus of the self vehicle. Wherein the vehicle-mounted sensor of the vehicle may be a radar device mounted on the vehicle.
Step 101: and constructing a state equation and a measurement equation of information reconstruction, and designing a self-adaptive posterior filtering method.
Step 102: the method comprises the steps of adopting a designed self-adaptive sequential test filtering method, measuring the speed and the relative distance of a front vehicle with unknown measurement noise based on a state equation and a measurement equation to obtain measurement data, carrying out on-line self-adaptive design of a noise matrix, and obtaining the final speed and the final relative distance of the front vehicle based on the measurement data by adopting a minimum mean square error filtering algorithm. Specifically, the implementation process of the step is as follows:
step 1021: and determining prior information of the speed and the relative distance of the front vehicle according to the state equation. The prior information includes a prior estimate and an error covariance of the prior estimate.
Step 1022: and determining posterior information of the speed and the relative distance of the front vehicle according to the measurement equation. The posterior information includes a posterior estimate and an error covariance of the posterior estimate.
Step 1023: and carrying out continuous iterative circulation based on the prior information and the posterior information, carrying out online self-adaptive design of the measurement noise matrix, and obtaining the final front vehicle speed and the final relative distance based on a minimum mean square error filtering algorithm.
Based on the above description, in the practical application process, the implementation process of the front vehicle speed and the relative distance reconstruction provided in the step 102 may be:
the following discrete state space equations are established:
(1)
wherein,，/>，/>state variable representing when the sampling time is k, < +.>Representing the measured variable in unknown noise at a sampling time k, <>For the state transition function +.>Representing the measurement output function>Representing the matrix transpose. />Input variable representing when the sampling time is k, < +.>Input variable representing when the sampling time is k-1, < >>Represents the process noise of the system at sample time k, the covariance of the process noise is +.>，/>Measurement noise of the system at a sampling time k is represented, and covariance of the measurement noise is +.>，/>Indicating the relative distance between two vehicles, < >>Indicating the speed of the lead vehicle.
Based on the discrete state space equation, the specific iterative steps of the front vehicle speed and the relative distance reconstruction are as follows:
1) Initializing:
initial state averageAnd corresponding covariance->The method comprises the following steps:
(2)
(3)
wherein,for the initial state mean->Is>Is a mathematical expectation.
2) And (5) updating time:
generating a state of sigma sampling pointsAnd corresponding weight->And->The method comprises the following steps:
(4)
(5)
wherein,for the dimension of the state vector, +.>,/>And->Are all undetermined parameters, are->For the time instant estimate of k-1,covariance of state when sampling time is k-1, +.>Is the state of the sigma sample point.
Calculating the state of sigma points using state equationsThe method comprises the following steps:
(6)
based on the updated sigma points, the prior stateAnd corresponding covariance matrix->The calculation formula is as follows:
(7)
(8)
in the method, in the process of the invention,to process noise coordinationA variance matrix.
3) Measurement update:
reconstructing the state of sigma points based on covariance of time updatesThe method comprises the following steps:
(9)
calculating the state of sigma points using measurement equationsThe method comprises the following steps:
(10)
in the method, in the process of the invention,an equation is estimated for the measurement.
Estimated measurement outputAnd its covariance matrix->The calculation formula of (2) is as follows:
(11)
(12)
wherein,is the covariance matrix of the measurement noise.
Further, cross covarianceThe calculation formula is as follows:
(13)
filtering gainAnd posterior state->And covariance matrix thereof->The calculation formula is as follows:
(14)
(15)
(16)
covariance matrix for measuring noiseThe dynamic update of (2) is as follows.
(17)
(18)
(19)
(20)
In the method, in the process of the invention,、/>、/>all are intermediate variables, and have no practical meaning for simplifying the formula. />For information variance +.>For measuring the noise matrix>For measuring the noise matrix>Is a number between 0 and 1.
Step 103: the critical safe distance is determined based on the final front vehicle speed, the speed of the own vehicle, the vehicle response time, the braking force increase time, and the minimum holding distance of the own vehicle from the front vehicle, taking the road surface adhesion coefficient into consideration. Specifically, the specific calculation step of the critical safety distance may be:
assume that the initial speed of the vehicle isWhich is at the response time +.>Distance travelled inside ∈ ->The method comprises the following steps:
(21)
braking force increase timeDistance travelled inside->The method comprises the following steps:
(22)
in the method, in the process of the invention,is the maximum deceleration.
The vehicle braking distance during this timeThe method comprises the following steps:
(23)
during the duration of brakingIn which the own vehicle is always at maximum deceleration +.>Driving at constant speed for a distance ∈>After stopping, there are:
(24)
thus, the total braking distance travelled during braking of the bicycle is:
(25)
based on this, critical safety distanceThe method comprises the following steps:
(26)
wherein,for road adhesion coefficient->Acceleration of gravity, ++>For minimum maintenance distance->Is the speed of the bicycle.
Step 104: when the final relative distance is less than the critical safe distance, a steering decision command is activated.
Step 105: and constructing a high-order polynomial path planning algorithm, and generating a lane change path based on the high-order polynomial path planning algorithm.
Step 106: and obtaining the expected steering wheel angle according to the steering inverse dynamics model and the variable road diameter.
In the practical application process, an ideal lane change path can be generated by adopting a seven-order polynomial function, and the method comprises the following steps:
(27)
wherein,for changing road diameter function->In a coordinate system established by taking a set starting point as an origin in a lane change pathDistance in x direction>For varying the lateral distance of the track->Is the longitudinal distance of the lane change. Assuming that the heading angle of the vehicle at lane change is small, i.e. the longitudinal speed is regarded as a constant value, the following is used +.>Replace->The following lane change trajectory function is obtained.
(28)
In the method, in the process of the invention,for the time variation during the channel change, < >>For the longitudinal speed of the vehicle>Is the total time of the lane changing process.
Obtaining a lateral acceleration calculation formula by solving the formula (28) twice, wherein the lateral acceleration calculation formula is as follows:
(29)
in the method, in the process of the invention,is->Lateral acceleration at time.
When the vehicle is operating in an unlimited situation, the curvature of the vehicle's trajectory may be reduced to be proportional to the steering wheel angle.
(30)
Wherein,for steering wheel angle>For the wheelbase of the vehicle>For steering gear ratio>Is the turning radius.
Multiplying both sides of equation (30) by lateral velocityThe method can obtain:
(31)
in the method, in the process of the invention,is the lateral acceleration.
Further considering the lateral turning characteristics of the vehicle, it is possible to obtain:
(32)
(33)
wherein,and->The distance between the center of gravity and the front and rear axles, respectively, < >>And->Lateral cornering stiffness, < >>For a desired steering wheel angle +.>For stability factor, < >>Is the mass of the vehicle.
The above formulas (30) - (33) are steering inverse dynamics models.
Step 107: and controlling the self-vehicle to complete steering and obstacle avoidance based on the expected steering wheel angle. I.e. the angle of rotation obtainedInputting the obstacle avoidance information to the own vehicle.
Based on the above description, in the practical application process, as shown in fig. 2, the implementation flow of the vehicle safety obstacle avoidance method provided by the invention is as follows: first, the speed of the front vehicle and the relative distance information of the two vehicles with unknown measurement noise are obtained by using the vehicle-mounted sensor of the own vehicle. Secondly, reconstructing the information through a designed filtering method (such as unscented Kalman filtering) combining the self-adaptive prior with the posterior to obtain more accurate information of the speed and the relative distance of the front vehicle. And then, establishing a critical safety distance index considering the road adhesion coefficient according to the information, and carrying out steering decision according to the index. And finally, generating an obstacle avoidance path by the self-vehicle after receiving the steering instruction, and controlling the self-vehicle to complete active collision avoidance according to the expected steering wheel angle obtained by the reverse dynamics model of the steering system.
Further, the invention provides a vehicle safety obstacle avoidance system, and the vehicle safety obstacle avoidance method is applied. The system comprises:
the data acquisition module is used for acquiring the speed of the front vehicle with unknown measurement noise data and the relative distance between the front vehicle and the front vehicle by using the vehicle-mounted sensor of the self vehicle, and acquiring the speed of the self vehicle by using the local area network bus of the self vehicle.
The equation and filtering construction module is used for constructing a state equation and a measurement equation of information reconstruction and designing a self-adaptive sequential test filtering method.
The noise filtering module is used for measuring the speed and the relative distance of the front vehicle with unknown measurement noise based on a state equation and a measurement equation to obtain measurement data, performing online self-adaptive design of a noise matrix, and obtaining the final speed and the final relative distance of the front vehicle based on the measurement data by adopting a minimum mean square error filtering algorithm.
The critical safety distance determining module is used for determining the critical safety distance based on the final front vehicle speed, the speed of the own vehicle, the vehicle response time, the braking force increasing time and the minimum keeping distance between the own vehicle and the front vehicle by taking the road adhesion coefficient into consideration.
And the steering decision instruction activation module is used for activating the steering decision instruction when the final relative distance is smaller than the critical safety distance.
The variable road path generation module is used for constructing a high-order polynomial path planning algorithm and generating a variable road path based on the high-order polynomial path planning algorithm.
And the steering wheel angle determining module is used for obtaining the expected steering wheel angle according to the steering inverse dynamics model and the variable road diameter.
And the steering obstacle avoidance module is used for controlling the self-vehicle to finish steering obstacle avoidance based on the expected steering wheel angle.
Still further, the present invention also provides an electronic device including:
and a memory for storing a computer program.
And the processor is connected with the memory and used for retrieving and executing the computer program so as to implement the vehicle safety obstacle avoidance method.
Furthermore, the computer program in the above-described memory may be stored in a computer-readable storage medium when it is implemented in the form of a software functional unit and sold or used as a separate product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (5)
1. A method for vehicle safety obstacle avoidance, comprising:
acquiring the speed of a front vehicle with unknown measurement noise data and the relative distance between the front vehicle and the front vehicle by using a vehicle-mounted sensor of the self vehicle, and acquiring the speed of the self vehicle by using a local area network bus of the self vehicle;
constructing a state equation and a measurement equation of information reconstruction, and designing a self-adaptive sequential test filtering method;
measuring the speed and the relative distance of the front vehicle with unknown measurement noise based on the state equation and the measurement equation by adopting the designed self-adaptive posterior filtering method to obtain measurement data, carrying out on-line self-adaptive design of a noise matrix, and obtaining the final speed and the final relative distance of the front vehicle based on the measurement data by adopting a minimum mean square error filtering algorithm;
determining a critical safety distance based on the final front vehicle speed, the speed of the own vehicle, a vehicle response time, a braking force increase time, and a minimum holding distance of the own vehicle from the front vehicle, taking into consideration a road surface adhesion coefficient;
activating a steering decision command when the final relative distance is less than the critical safety distance;
constructing a high-order polynomial path planning algorithm, and generating a lane change path based on the high-order polynomial path planning algorithm;
obtaining a desired steering wheel angle according to the steering inverse dynamics model and the variable road diameter;
controlling the self-vehicle to complete steering obstacle avoidance based on the expected steering wheel angle;
measuring the speed and the relative distance of the front vehicle with unknown measurement noise based on the state equation and the measurement equation to obtain measurement data, performing on-line self-adaptive design of a noise matrix, and obtaining the final speed and the final relative distance of the front vehicle based on the measurement data by adopting a minimum mean square error filtering algorithm, wherein the method specifically comprises the following steps:
determining prior information of the speed of the front vehicle and the relative distance according to the state equation; the prior information comprises a prior estimated value and an error covariance of the prior estimated value;
determining posterior information of the speed and the relative distance of the front vehicle according to the measurement equation; the posterior information comprises a posterior estimated value and an error covariance of the posterior estimated value;
performing continuous iterative loop based on the prior information and the posterior information, performing online self-adaptive design of a measurement noise matrix, and obtaining final front vehicle speed and final relative distance based on a minimum mean square error filtering algorithm;
the prior estimate and the error covariance of the prior estimate are expressed as:
；
；
in the method, in the process of the invention,is a priori state->Weight for one-step prediction, +.>Status of sigma point calculated for using state equation, +.>Error covariance for a priori estimate, +.>Weights updated for covariance, +.>For the process noise covariance matrix,/>For the dimension of the state vector, +.>Dimension number for state vector, +.>，/>Sampling time;
the posterior estimate and the error covariance of the posterior estimate are expressed as:
；
；
in the method, in the process of the invention,for posterior state, add->Error covariance for posterior estimate, +.>For filtering gain +.>For the estimated measurement output +.>Covariance of the measurement output for estimation, +.>For a measured variable at unknown noise, +.>Is a transpose of the matrix.
2. The vehicle safety obstacle avoidance method of claim 1 wherein the higher order polynomial path planning algorithm is a seventh order polynomial function.
3. The vehicle safety obstacle avoidance method of claim 2 wherein the varying road path generated using the seventh order polynomial function is represented as:
；
in the method, in the process of the invention,for changing road diameter function->For varying the lateral distance of the track->For the distance in x-direction in the coordinate system established with the set origin in the lane-change path,/->Is the longitudinal distance of the lane change.
4. A vehicle safety obstacle avoidance system, characterized by applying the vehicle safety obstacle avoidance method of any one of claims 1-3; the system comprises:
the data acquisition module is used for acquiring the speed of the front vehicle with unknown measurement noise data and the relative distance between the front vehicle and the front vehicle by using the vehicle-mounted sensor of the self vehicle, and acquiring the speed of the self vehicle by using the local area network bus of the self vehicle;
the equation and filtering construction module is used for constructing a state equation and a measurement equation of information reconstruction and designing a self-adaptive sequential verification filtering method;
the noise filtering module is used for measuring the speed and the relative distance of the front vehicle with unknown measurement noise based on the state equation and the measurement equation to obtain measurement data, performing online self-adaptive design of a noise matrix, and obtaining the final speed and the final relative distance of the front vehicle based on the measurement data by adopting a minimum mean square error filtering algorithm;
a critical safe distance determining module for determining a critical safe distance based on the final front vehicle speed, the speed of the own vehicle, a vehicle response time, a braking force increasing time, and a minimum holding distance between the own vehicle and the front vehicle, taking into consideration a road surface attachment coefficient;
the steering decision instruction activation module is used for activating a steering decision instruction when the final relative distance is smaller than the critical safety distance;
the variable road path generation module is used for constructing a high-order polynomial path planning algorithm and generating a variable road path based on the high-order polynomial path planning algorithm;
the steering wheel angle determining module is used for obtaining a desired steering wheel angle according to the steering inverse dynamics model and the variable road diameter;
and the steering obstacle avoidance module is used for controlling the self-vehicle to finish steering obstacle avoidance based on the expected steering wheel angle.
5. An electronic device, comprising:
a memory for storing a computer program;
a processor, coupled to the memory, for retrieving and executing the computer program to implement the vehicle safety obstacle avoidance method of any of claims 1-3.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011164126A (en) * | 2010-02-04 | 2011-08-25 | Nippon Telegr & Teleph Corp <Ntt> | Noise suppression filter calculation method, and device and program therefor |
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