CN115303265A - Vehicle obstacle avoidance control method and device and vehicle - Google Patents
Vehicle obstacle avoidance control method and device and vehicle Download PDFInfo
- Publication number
- CN115303265A CN115303265A CN202210979383.6A CN202210979383A CN115303265A CN 115303265 A CN115303265 A CN 115303265A CN 202210979383 A CN202210979383 A CN 202210979383A CN 115303265 A CN115303265 A CN 115303265A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- information
- field model
- road
- obstacle avoidance
- 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
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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
-
- 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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
-
- 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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/064—Degree of grip
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
Abstract
The vehicle obstacle avoidance control method, the vehicle obstacle avoidance control device and the vehicle comprise the steps of calculating a road adhesion coefficient in real time based on vehicle state information after acquiring vehicle state information and road information of vehicle driving in the current vehicle driving process, constructing a driving risk field model according to the road adhesion coefficient and the road information, calculating an optimal reference path of obstacle avoidance based on the driving risk field model, and controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving. The influence of the road surface adhesion coefficient on the vehicle obstacle avoidance control is fully considered, so that the driving risk field model about the road surface adhesion coefficient is established, the vehicle obstacle avoidance control can be carried out based on the driving risk field model, the obstacle avoidance accuracy can be greatly improved, and the driving risk of a driver is effectively reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of vehicle obstacle avoidance, in particular to a vehicle obstacle avoidance control method and device and a vehicle.
Background
According to research, about 90% of traffic accidents are related to a driver, the vehicle active obstacle avoidance technology can enable a vehicle to avoid obstacles autonomously according to an obstacle avoidance path, and driving safety is improved.
The driving environment of the vehicle is complex and changeable, the condition of sudden change of the road adhesion condition often appears in the driving process, most typically, the driving condition that the weather changes from clear to rain, the road adhesion condition changes from high to low, for the scene, if the influence of the road adhesion change on a risk field is not considered, the accuracy of obstacle avoidance control can be seriously influenced, and therefore the driving risk of a driver is greatly improved.
Disclosure of Invention
In view of this, the embodiment of the invention provides a vehicle obstacle avoidance control method, a vehicle obstacle avoidance control device and a vehicle, which can improve the accuracy of vehicle obstacle avoidance, further reduce the driving risk of a driver, and enable the driver to safely go out.
In a first aspect, an embodiment of the present invention provides a vehicle obstacle avoidance control method, where the method is applied to a vehicle-mounted controller; the method comprises the following steps:
acquiring vehicle state information and road surface information of vehicle running in the current vehicle running process;
calculating a road adhesion coefficient in real time based on the vehicle state information;
constructing a driving risk field model according to the road adhesion coefficient and the road information;
calculating an optimal reference path for avoiding obstacles based on the driving risk field model;
and controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving.
The step of calculating the road adhesion coefficient in real time based on the vehicle state information includes:
extracting vehicle tire information and vehicle running speed information in the vehicle state information in real time;
inputting vehicle tire information and vehicle running speed information into a tire model trained in advance, and outputting tire adhesion coefficients corresponding to various tires of a vehicle through the tire model;
and calculating the mean value of the adhesion coefficients of the plurality of tires to obtain the road adhesion coefficient.
The step of constructing the driving risk field model according to the road adhesion coefficient and the road information includes:
extracting road boundary information, first position information of a vehicle driving to a target position, current second position information of an obstacle, relative information of the obstacle and the vehicle, and current third position information of the vehicle in the road surface information;
constructing a road boundary risk field model based on the road boundary information and the third position information;
constructing a target gravitational field model based on the first position information and the third position information;
constructing an obstacle risk field model based on the road adhesion coefficient, the second position information, the relative information and the third position information;
and constructing a driving risk field model according to the road boundary risk field model, the target gravitational field model and the obstacle risk field model.
The step of constructing the driving risk field model according to the road boundary risk field model, the target gravitational field model and the obstacle risk field model includes:
and carrying out weighted calculation on the road boundary risk field model, the target gravitational field model and the barrier risk field model to obtain a driving risk field model.
The step of determining the optimal reference path for obstacle avoidance based on the driving risk field model includes:
carrying out negative gradient derivation on the driving risk field model to obtain a plurality of obstacle avoidance path points of the initial obstacle avoidance path;
and calculating an optimal reference path based on the plurality of obstacle avoidance path points.
The step of calculating the optimal reference path based on the plurality of obstacle avoidance path points includes:
and performing multiple times of multiple fitting calculation on the plurality of obstacle avoidance path points to obtain an optimal reference path.
In a second aspect, an embodiment of the present invention provides a vehicle obstacle avoidance control apparatus, where the apparatus is applied to a vehicle-mounted server; the above-mentioned device includes:
the acquisition module is used for acquiring vehicle state information and road surface information of vehicle running in the current vehicle running process;
the first calculation module is used for calculating the road adhesion coefficient in real time based on the vehicle state information;
the construction module is used for constructing a driving risk field model according to the road adhesion coefficient and the road information;
the second calculation module is used for calculating the optimal reference path for avoiding the obstacle based on the driving risk field model;
and the control module is used for controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving.
The first computing module is further configured to: extracting vehicle tire information and vehicle running speed information in the vehicle state information in real time;
inputting vehicle tire information and vehicle running speed information into a tire model trained in advance, and outputting tire adhesion coefficients corresponding to various tires of a vehicle through the tire model;
and carrying out average value calculation on the adhesion coefficients of the plurality of tires to obtain the road adhesion coefficient.
The above-mentioned building block is further configured to: extracting road boundary information, first position information of a target position where a vehicle drives, current second position information of an obstacle, relative information of the obstacle and the vehicle, and current third position information of the vehicle in the road surface information;
constructing a road boundary risk field model based on the road boundary information and the third position information;
constructing a target gravitational field model based on the first position information and the third position information;
constructing an obstacle risk field model based on the road adhesion coefficient, the second position information, the relative information and the third position information;
and constructing a driving risk field model according to the road boundary risk field model, the target gravitational field model and the obstacle risk field model.
In a third aspect, embodiments of the present invention provide a vehicle equipped with an onboard controller for executing the above-mentioned vehicle obstacle avoidance control method.
The embodiment of the invention brings the following beneficial effects:
the embodiment of the invention provides a vehicle obstacle avoidance control method, a vehicle obstacle avoidance control device and a vehicle, wherein after vehicle state information and road surface information of vehicle driving in the current vehicle driving process are obtained, a road surface adhesion coefficient is calculated in real time based on the vehicle state information, a driving risk field model is constructed according to the road surface adhesion coefficient and the road surface information, an optimal reference path of obstacle avoidance is calculated based on the driving risk field model, and the vehicle is controlled to track the optimal reference path so as to carry out obstacle avoidance driving. The influence of the road surface adhesion coefficient on the vehicle obstacle avoidance control is fully considered, so that the driving risk field model about the road surface adhesion coefficient is established, the vehicle obstacle avoidance control can be carried out based on the driving risk field model, the obstacle avoidance accuracy can be greatly improved, and the driving risk of a driver is effectively reduced.
Drawings
Fig. 1 is a flowchart of a vehicle obstacle avoidance control method provided in this embodiment;
fig. 2 is a flowchart of another vehicle obstacle avoidance control method provided in this embodiment;
fig. 3 is a three-dimensional schematic diagram of a road boundary risk field model provided in this embodiment;
FIG. 4 is a three-dimensional schematic diagram of a target gravitational field model according to the present embodiment;
FIG. 5 is a three-dimensional schematic of the modeling of an obstacle risk field provided by the present embodiment;
fig. 6 is a three-dimensional schematic diagram of a driving risk field model provided in this embodiment;
fig. 7 is a schematic structural diagram of a vehicle obstacle avoidance control device provided in this embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
For the convenience of understanding the embodiments of the present invention, the following detailed description will be given with reference to the accompanying drawings, which are not intended to limit the embodiments of the present invention.
The embodiment provides a vehicle obstacle avoidance control method, wherein the method is applied to a vehicle-mounted controller; referring to a flowchart of a vehicle obstacle avoidance control method shown in fig. 1, the method specifically includes the following steps:
s102, acquiring vehicle state information and road surface information of the vehicle in the current vehicle running process;
in practical use, the vehicle-mounted controller is in communication connection with a sensor group on the vehicle, and the sensor group is used for collecting vehicle external data, namely road surface information, and detecting dynamic data, namely vehicle state information, of the vehicle in the driving process. The sensor group includes, for example, but not limited to, at least one of a camera, a laser radar, a millimeter wave radar, a GPS (Global Positioning System), an IMU (Inertial Measurement Unit), a speed sensor, and an acceleration sensor.
The vehicle-mounted controller is used for acquiring data of the sensor group, all sensors in the sensor group transmit data at a high frequency in the driving process of a vehicle, and the vehicle-mounted controller is further used for being in wireless communication with the cloud server and interacting various information.
The vehicle-mounted controller is further used for calculating a road adhesion coefficient based on data of the sensor group, planning and making a decision based on the road adhesion coefficient, and generating a vehicle control command based on the planned optimal reference path, so that the vehicle is controlled to safely avoid obstacles.
S104, calculating a road adhesion coefficient in real time based on the vehicle state information;
the road adhesion coefficient is the ratio of the adhesive force to the normal (perpendicular to the road) pressure of the wheel, can be regarded as the static friction coefficient between the tire and the road, and is approximately equal to the friction coefficient, and different road adhesion states have great influence on the obstacle avoidance path and the obstacle avoidance effect of the vehicle; under the same vehicle speed, the lower the road adhesion coefficient is, the smaller the lateral acceleration is when avoiding the obstacle, the smaller the standard deviation of the lateral acceleration is, and the more stable the obstacle avoiding effect is.
S106, constructing a driving risk field model according to the road adhesion coefficient and the road information;
the driving risk field model is one of effective means for evaluating the driving safety of the vehicle, the current mainstream driving risk field model does not consider information that the road adhesion coefficient influences the driving safety, neglects the influence of the road adhesion coefficient on the driving risk, obviously does not accord with the actual condition, and can seriously influence the safety of obstacle avoidance control. Therefore, in the process of vehicle active safety control, the road adhesion coefficient needs to be calculated in real time, and the calculated road adhesion coefficient is introduced into the driving risk field model, so that the accuracy of the model is effectively improved, the vehicle is accurately controlled to safely avoid obstacles, and the driving risk of a driver is greatly reduced.
S108, calculating an optimal reference path for obstacle avoidance based on the driving risk field model;
the premise of vehicle obstacle avoidance is that the vehicle is controlled to track the optimal reference path calculated by the driving risk field model for safe driving, and the optimal reference path can be understood as an obstacle avoidance path with the lowest driving risk.
And S110, controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving.
In the embodiment, the optimal reference path can be tracked by using the prediction model to control the front wheel steering angle of the vehicle for obstacle avoidance driving.
In specific implementation, the construction process of the prediction model is as follows: firstly, considering transverse and longitudinal movement of a vehicle when the vehicle avoids an obstacle and yaw movement of the vehicle, neglecting suspension influence, vertical movement and the like, and establishing a three-degree-of-freedom vehicle model as a formula (1):
in the formula: s f 、s r : slip rates of front and rear tires; c lf 、C lr : longitudinal tire front and rear stiffness; c cf 、C cr : tire front and rear cornering stiffness;
the longitudinal speed of the vehicle is such that,
the lateral speed of the vehicle is set to be,
a yaw angle;
yaw angular velocity;
vehicle longitudinal acceleration;
vehicle lateral acceleration;
yaw angular acceleration; a: the distance of the vehicle's center of mass to the front axle; b: distance of vehicle center of mass to rear axle; i is z : the moment of inertia of the vehicle about the z-axis; m is 1 : a vehicle mass;
the speed of the vehicle along the X-axis in the geodetic coordinate system;
the velocity of the vehicle along the Y-axis in the geodetic coordinate system.
Then, based on the established three-degree-of-freedom model of the vehicle, selectingTaking longitudinal speed of vehicle
Transverse velocity
Yaw angle
Yaw rate
And the transverse and longitudinal positions of the vehicle are system state quantities, such as the formula (2):
the front wheel turning angle is a control quantity: u = δ f . First, for the non-linear dynamic model
Carrying out linearization treatment to obtain a linear state space equation as formula (3):
in the formula:
and meanwhile, discretizing the obtained linear state space equation to obtain a formula (4):
in the formula: a. The t ＝I m2 +TA(t)B t ＝TB(t)，m 2 And T is the dimension of the state quantity, and the system sampling time. Is provided with
Obtaining a model expression of the prediction model, as formula (5):
wherein, the first and the second end of the pipe are connected with each other,
in the present embodiment, the control output amount of the prediction model is set as the front wheel rotation angle of the vehicle, and the vehicle speed is assumed to be constant, so that it is considered that the state amount directly set as the target function of the control output amount may cause an increase in the control amount, which affects the control accuracy. Therefore, taking the increase in the control output quantity as the state quantity of the objective function, as in equation (6):
in the formula eta ref Is an optimal reference path; n is a radical of p : predicting a time domain; n is a radical of c : controlling a time domain; q: a tracking effect adjustment matrix; r: a control quantity change adjustment matrix; ρ: a weight coefficient; epsilon: preventing a relaxation factor in which the control quantity increment is not solved.
In the solution process, the objective function needs to satisfy the following constraints:
and (3) controlling output quantity constraint:
u min ≤u(t+i)≤u max i＝0,1,L N c -1 (7)
controlling output increment constraint:
Δu min ≤Δu(t+i)≤Δu max i＝0,1,L N c -1 (8)
output variable constraint:
y min ≤y(t+i)≤y max i＝0,1,L N c -1 (9)
considering that the road adhesion coefficient also restrains the dynamic property of the vehicle and directly influences the acceleration of the vehicle, the specific relationship is as follows:
in the formula a x 、a y Longitudinal acceleration and transverse acceleration of the vehicle, and mu is a road surface adhesion coefficient. Since it is assumed that the vehicle longitudinal speed is constant, the above equation can be simplified as:
|a y |≤μg
according to the target function and the constraint conditions thereof, the problem can be converted into a quadratic programming problem through corresponding matrix operation, the increment of the control output quantity in a control domain can be solved, meanwhile, the first increment is acted on the prediction model, the solving process is repeated until the solving process meets the constraint conditions set by the target function, and the prediction model outputs the corner of the front wheel so as to realize the tracking driving of the optimal obstacle avoidance path.
According to the vehicle obstacle avoidance control method provided by the embodiment of the invention, the influence of the road surface adhesion coefficient on the vehicle obstacle avoidance control is fully considered, so that a driving risk field model related to the road surface adhesion coefficient is constructed, and the vehicle obstacle avoidance control based on the driving risk field model can greatly improve the obstacle avoidance accuracy, so that the driving risk of a driver is effectively reduced.
The embodiment provides another vehicle obstacle avoidance control method, which is implemented on the basis of the above embodiment; the embodiment focuses on a specific implementation manner of calculating a road adhesion coefficient, constructing a driving risk field model and calculating an optimal reference path. As shown in fig. 2, another flow chart of a vehicle obstacle avoidance control method is shown, and the vehicle obstacle avoidance control method in this embodiment includes the following steps:
s200, acquiring vehicle state information and road surface information of the vehicle in the current vehicle running process;
s201, extracting vehicle tire information and vehicle running speed information in the vehicle state information in real time;
the vehicle tire information comprises information such as tire radius, tire angular velocity, tire cornering stiffness, tire longitudinal stiffness, tire cornering angle and the like, and can be acquired by a sensor which acquires the vehicle tire information in a sensor group.
The vehicle running speed information comprises vehicle speed information and acceleration information, and the information can be acquired by a speed sensor and an acceleration sensor in a sensor group.
S202, inputting vehicle tire information and vehicle running speed information into a tire model trained in advance, and outputting tire adhesion coefficients corresponding to various tires of a vehicle through the tire model;
in this embodiment, the building process of the tire model is as follows:
firstly, a three-degree-of-freedom vehicle dynamic model is constructed, wherein the three-degree-of-freedom vehicle dynamic model is as shown in a formula (10):
in the formula: delta is the front wheel corner; beta is the centroid slip angle;
the yaw angular velocity; a centroid to front axle distance; b, distance from the center of mass to the rear axle; v. of y A lateral velocity; v. of x A longitudinal speed; f represents before; r represents; l represents left; r represents the right; f x 、F y Longitudinal and transverse forces; t is the track width.
The model formula for building the tire model based on the three-degree-of-freedom vehicle dynamics model is shown as (11):
wherein the content of the first and second substances,
wherein, the first and the second end of the pipe are connected with each other,
wherein, the first and the second end of the pipe are connected with each other,
in the formula: r is the tire radius; omega tire angular velocity; c x Tire cornering stiffness; c y Tire longitudinal stiffness; α tire slip angle; an epsilon velocity influence factor; lambda is slip ratio; l is a non-linear parameter used to describe tire slip; the subscript i takes f as a front shaft, and r as a rear shaft; the subscript j takes l to represent the left wheel and r to represent the right wheel; for example, the subscript fl indicates the left wheel of the front axle of the vehicle, rr the right wheel of the rear axle of the vehicle.
In practical use, the estimation method of the road adhesion coefficient is mainly divided into two types, namely a Cause-Based algorithm and an Effect-Based algorithm. The method has the advantages that a professional sensor is required to be additionally arranged in the Cause-Based algorithm, the cost is high, the Cause-Based algorithm is easily influenced by the environment, the applicability is limited, the Effect-Based algorithm is used for estimating the road adhesion coefficient according to the vehicle motion parameter change caused by the road surface change, the method is low in cost and high in applicability, and therefore in the embodiment, the road adhesion coefficient is calculated in real time by the Effect-Based algorithm Based on the functional relation generated between the tire model and the road adhesion coefficient.
Specifically, a state equation and an observation equation of the Effect-Based algorithm are established, and a state variable is selected as a tire adhesion coefficient of four wheels: x (t) = [ mu ] fl μ fr μ rl μ rr ] T
Further assume the lateral longitudinal acceleration a y 、a x Yaw angular velocity
It can be directly acquired by the sensor, so it is taken as an observation variable, namely:
the control quantities are the front wheel steering angle δ and four normalized tire forces:
in summary, the state space equation Based on the Effect-Based algorithm is as shown in formula (12):
in the formula: ω (t): process noise; v (t): and observing noise. By linearizing equation (12), the state space expression after linearization can be obtained as follows:
in the formula:
in conclusion, based on the state quantity type, the observed quantity type and the controlled quantity type of the Kalman filtering, which are road adhesion coefficient estimation, and combining with the Kalman filtering algorithm principle, the tire adhesion coefficients estimated by 4 tires of the vehicle can be obtained.
S203, carrying out average value calculation on the adhesion coefficients of the tires to obtain a road adhesion coefficient;
s204, extracting road boundary information in the road surface information, first position information of a vehicle driving to a target position, current second position information of an obstacle, relative information of the obstacle and the vehicle, and current third position information of the vehicle;
the road boundary information can be extracted from a road image shot by a camera in a sensor group, the determination of the position information of the first position information, the second position information and the third position information can be realized by GPS positioning, the positioning precision of the GPS is in the order of tens of meters to centimeters, and the positioning precision is high; the positioning can also be realized by a positioning method combining a GPS and an Inertial Navigation System (Inertial Navigation System), and the positioning method is not limited herein.
The relative information of the obstacle and the vehicle comprises relative speed information and acceleration information, and can be acquired by a speed sensor and an acceleration sensor.
S205, constructing a road boundary risk field model based on the road boundary information and the third position information;
considering that most vehicles can run close to the center line of the road when running on the road, and considering that the vehicle transverse obstacle avoidance scene is different from the vehicle lane change scene, the influence of the lane line in the road boundary on the traffic risk field model is ignored, so that the piecewise function is selected based on the road boundary information to model the road boundary risk field model, as shown in formula (13):
in the formula: lambda is a road boundary driving risk field adjusting coefficient and is used for adjusting the size of a road boundary risk field; l is left And L right The left and right boundary positions of the road are road boundary information respectively.
Fig. 3 shows a three-dimensional schematic diagram of a road boundary risk field model, and it can be seen from the diagram that the driving risk of a vehicle is small when the vehicle normally runs in a road boundary, and the driving risk of the vehicle increases when the vehicle runs beyond the road boundary range, so that an exponential function with a fast increasing speed is selected when the vehicle exceeds the road boundary, and the value of the risk field in the road boundary range is zero.
S206, constructing a target gravitational field model based on the first position information and the third position information;
considering that the target gravity field model acts to steer the vehicle to the target position, the target gravity field model is as in equation (14):
U target ＝α*[(x-x target ) 2 +(y-y target ) 2 ] (14)
in the formula: alpha is a target gravitational field regulating coefficient; x is a radical of a fluorine atom target ，y target The first position information is the horizontal and vertical coordinates of the target position, and the third position information is the current horizontal and vertical coordinates of the vehicle.
FIG. 4 shows a three-dimensional representation of a target gravitational field model, and as can be seen from FIG. 4, the target gravitational field model requires a high risk at locations far from the target location and a low risk at locations close to the target location, so that the gravitational field may be tilted toward the target location to drive the vehicle toward the target location
S207, constructing an obstacle risk field model based on the road adhesion coefficient, the second position information, the relative information and the third position information;
considering the smoothness requirement of the obstacle avoidance path of the vehicle, in the embodiment, a two-dimensional normal distribution function with a shape similar to that of the vehicle is selected to model the obstacle risk field, and the model is as shown in formula (15):
in the formula: beta: the adjustment coefficient of the magnitude of the repulsive force field of the obstacle; v: the relative speed between the test vehicle and the obstacle vehicle; beta is a beta v : a relative speed adjustment factor; μ: road surface adhesion coefficient; beta is a μ : adjusting coefficient of road surface adhesion coefficient;β obs : regulating coefficient of the overall dimension of the obstacle vehicle; beta is a beta a : a relative acceleration adjustment coefficient; a: relative acceleration.
Wherein, the first and the second end of the pipe are connected with each other,
in the formula, x obs 、y obs The horizontal and vertical coordinates of the obstacle are second position information.
FIG. 5 shows a three-dimensional schematic of the modeling of an obstacle risk field, as can be seen from FIG. 5, the closer to the obstacle the greater the risk value; and the purpose of adjusting the transverse and longitudinal risk values of the barrier can be achieved by adjusting the long and short axes of the barrier car risk field model.
S208, constructing a driving risk field model according to the road boundary risk field model, the target gravitational field model and the obstacle risk field model;
specifically, a road boundary risk field model, a target gravitational field model and an obstacle risk field model are subjected to weighted calculation to obtain a driving risk field model.
In actual use, the weighting coefficients may be set according to actual needs, which is not limited herein, and in this embodiment, the weighting coefficients are all 1, so that the specific expression of the driving risk field model is as follows: u = U road +U target +U obs For ease of understanding, fig. 6 shows a three-dimensional representation of a driving risk field model.
S209, carrying out negative gradient derivation on the driving risk field model to obtain a plurality of obstacle avoidance path points of the initial obstacle avoidance path;
the main purpose of vehicle obstacle avoidance is for safety, so the vehicle should travel towards the direction that the risk value of the driving risk field is reduced fastest in the obstacle avoidance process, and the high mathematics related theory shows that the vehicle moves along the direction of the negative gradient, the function value is reduced fastest, and the vehicle risk field model shows that: when the vehicle runs along the direction of the negative gradient of the traffic risk field model, the risk value is reduced fastest, and the obstacle avoidance safety is highest, so that negative gradient derivation is performed on the traffic risk field model, and a plurality of obstacle avoidance path points of the initial obstacle avoidance path are obtained.
S210, calculating an optimal reference path based on the plurality of obstacle avoidance path points;
considering that the initial obstacle avoidance path directly planned in the negative gradient direction of the driving risk field model may be unsmooth and not conform to vehicle dynamics constraints, and the like, multiple times of polynomial fitting is performed on multiple obstacle avoidance path points to obtain an optimal reference path, and in this embodiment, a 5 th-order polynomial fitting optimization may be adopted to obtain the optimal reference path.
And S211, controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving.
Corresponding to the above method embodiment, the present embodiment provides a vehicle obstacle avoidance control device, where the device is applied to a vehicle-mounted server; referring to fig. 7, a schematic structural diagram of a vehicle obstacle avoidance control device is shown, where the device includes:
the acquiring module 71 is used for acquiring vehicle state information and road surface information of vehicle running in the current vehicle running process;
a first calculation module 72 for calculating a road adhesion coefficient in real time based on vehicle state information;
the construction module 73 is used for constructing a driving risk field model according to the road adhesion coefficient and the road information;
a second calculation module 74, configured to calculate an optimal reference path for obstacle avoidance based on the driving risk field model;
and the control module 75 is used for controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving.
The vehicle obstacle avoidance control device provided by the embodiment of the invention is characterized in that after the vehicle state information and the road surface information of the vehicle running in the current vehicle running process are obtained, the road surface adhesion coefficient is calculated in real time based on the vehicle state information, a driving risk field model is constructed according to the road surface adhesion coefficient and the road surface information, the optimal reference path of obstacle avoidance is calculated based on the driving risk field model, and the vehicle is controlled to track the optimal reference path so as to carry out obstacle avoidance running. The influence of road surface adhesion coefficient on vehicle obstacle avoidance control is fully considered in the application, so that a driving risk field model about the road surface adhesion coefficient is established, the vehicle obstacle avoidance control can be performed based on the driving risk field model, the obstacle avoidance accuracy can be greatly improved, and the driving risk of a driver is effectively reduced.
The first calculating module 72 is further configured to: extracting vehicle tire information and vehicle running speed information in the vehicle state information in real time; inputting vehicle tire information and vehicle running speed information into a tire model trained in advance, and outputting tire adhesion coefficients corresponding to various tires of a vehicle through the tire model; and carrying out average value calculation on the adhesion coefficients of the plurality of tires to obtain the road adhesion coefficient.
The building block 73 is further configured to: extracting road boundary information, first position information of a vehicle driving to a target position, current second position information of an obstacle, relative information of the obstacle and the vehicle, and current third position information of the vehicle in the road surface information; constructing a road boundary risk field model based on the road boundary information and the third position information; constructing a target gravitational field model based on the first position information and the third position information; constructing an obstacle risk field model based on the road adhesion coefficient, the second position information, the relative information and the third position information; and constructing a driving risk field model according to the road boundary risk field model, the target gravitational field model and the obstacle risk field model.
The building block 73 is further configured to: and performing weighted calculation on the road boundary risk field model, the target gravitational field model and the obstacle risk field model to obtain a driving risk field model.
The second calculating module 74 is further configured to perform negative gradient derivation on the driving risk field model to obtain a plurality of obstacle avoidance path points of the initial obstacle avoidance path; and calculating an optimal reference path based on the plurality of obstacle avoidance path points.
The second calculating module 74 is further configured to perform multiple times of polynomial fitting calculation on the multiple obstacle avoidance path points to obtain an optimal reference path.
The embodiment of the invention provides a vehicle, which is provided with a vehicle-mounted controller, wherein the vehicle-mounted controller is used for executing the vehicle obstacle avoidance control method.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. The vehicle obstacle avoidance control method is characterized by being applied to a vehicle-mounted controller; the method comprises the following steps:
acquiring vehicle state information and road surface information of vehicle running in the current vehicle running process;
calculating a road adhesion coefficient in real time based on the vehicle state information;
constructing a driving risk field model according to the road adhesion coefficient and the road information;
calculating an optimal reference path for avoiding obstacles based on the driving risk field model;
and controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving.
2. The method of claim 1, wherein the step of calculating a road adhesion coefficient in real time based on the vehicle state information comprises:
extracting vehicle tire information and vehicle running speed information in the vehicle state information in real time;
inputting the vehicle tire information and the vehicle running speed information into a tire model trained in advance, and outputting tire adhesion coefficients corresponding to the tires of the vehicle through the tire model;
and calculating the mean value of the tire adhesion coefficients to obtain the road adhesion coefficient.
3. The method of claim 1, wherein the step of constructing a driving risk field model from the road adhesion coefficients and the road information comprises:
extracting road boundary information, first position information of a target position driven by the vehicle, current second position information of an obstacle, relative motion information of the obstacle and the vehicle, and current third position information of the vehicle in the road surface information;
constructing a road boundary risk field model based on the road boundary information and the third position information;
constructing a target gravitational field model based on the first position information and the third position information;
constructing an obstacle risk field model based on the road surface adhesion coefficient, the second position information, the relative movement information and the third position information;
and constructing a driving risk field model according to the road boundary risk field model, the target gravitational field model and the obstacle risk field model.
4. The method of claim 3, wherein the step of constructing a driving risk field model from the road boundary risk field model, the target gravity field model and the obstacle risk field model comprises:
and carrying out weighted calculation on the road boundary risk field model, the target gravitational field model and the obstacle risk field model to obtain a driving risk field model.
5. The method according to claim 1, wherein the step of determining the optimal reference path for obstacle avoidance based on the driving risk field model comprises:
carrying out negative gradient derivation on the driving risk field model to obtain a plurality of obstacle avoidance path points of an initial obstacle avoidance path;
and calculating an optimal reference path based on the obstacle avoidance path points.
6. The method of claim 5, wherein the step of calculating an optimal reference path based on the obstacle avoidance path points comprises:
and performing multiple times of multiple fitting calculation on the obstacle avoidance path points to obtain an optimal reference path.
7. The vehicle obstacle avoidance control device is characterized in that the device is applied to a vehicle-mounted server; the device comprises:
the acquisition module is used for acquiring vehicle state information and road surface information of vehicle running in the current vehicle running process;
the first calculation module is used for calculating the road adhesion coefficient in real time based on the vehicle state information;
the construction module is used for constructing a driving risk field model according to the road adhesion coefficient and the road information;
the second calculation module is used for calculating an optimal reference path for obstacle avoidance based on the driving risk field model;
and the control module is used for controlling the vehicle to track the optimal reference path so as to carry out obstacle avoidance driving.
8. The apparatus of claim 7, wherein the first computing module is further configured to: extracting vehicle tire information and vehicle running speed information in the vehicle state information in real time;
inputting the vehicle tire information and the vehicle running speed information into a tire model trained in advance, and outputting tire adhesion coefficients corresponding to the tires of the vehicle through the tire model;
and carrying out average value calculation on the tire adhesion coefficients to obtain the road adhesion coefficient.
9. The apparatus of claim 7, wherein the build module is further configured to:
extracting road boundary information, first position information of the vehicle driving to a target position, current second position information of an obstacle, relative information of the obstacle and the vehicle, and current third position information of the vehicle in the road surface information;
constructing a road boundary risk field model based on the road boundary information and the third position information;
constructing a target gravitational field model based on the first location information and the third location information;
constructing an obstacle risk field model based on the road surface adhesion coefficient, the second position information, the relative information, and the third position information;
and constructing a driving risk field model according to the road boundary risk field model, the target gravitational field model and the obstacle risk field model.
10. A vehicle, characterized in that the vehicle is provided with an on-board controller for executing the vehicle obstacle avoidance control method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210979383.6A CN115303265A (en) | 2022-08-15 | 2022-08-15 | Vehicle obstacle avoidance control method and device and vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210979383.6A CN115303265A (en) | 2022-08-15 | 2022-08-15 | Vehicle obstacle avoidance control method and device and vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115303265A true CN115303265A (en) | 2022-11-08 |
Family
ID=83862684
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210979383.6A Pending CN115303265A (en) | 2022-08-15 | 2022-08-15 | Vehicle obstacle avoidance control method and device and vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115303265A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117091618A (en) * | 2023-10-18 | 2023-11-21 | 理工雷科智途(北京)科技有限公司 | Unmanned vehicle path planning method and device and electronic equipment |
| CN117542003A (en) * | 2024-01-08 | 2024-02-09 | 大连天成电子有限公司 | Freight train model judging method based on image feature analysis |
-
2022
- 2022-08-15 CN CN202210979383.6A patent/CN115303265A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117091618A (en) * | 2023-10-18 | 2023-11-21 | 理工雷科智途(北京)科技有限公司 | Unmanned vehicle path planning method and device and electronic equipment |
| CN117091618B (en) * | 2023-10-18 | 2024-01-26 | 理工雷科智途(北京)科技有限公司 | Unmanned vehicle path planning method and device and electronic equipment |
| CN117542003A (en) * | 2024-01-08 | 2024-02-09 | 大连天成电子有限公司 | Freight train model judging method based on image feature analysis |
| CN117542003B (en) * | 2024-01-08 | 2024-04-02 | 大连天成电子有限公司 | Freight train model judging method based on image feature analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110036353B (en) | Method and system for controlling lateral movement of a ground vehicle | |
| CN107161207B (en) | Intelligent automobile track tracking control system and control method based on active safety | |
| CN112733270A (en) | System and method for predicting vehicle running track and evaluating risk degree of track deviation | |
| Bevly et al. | Integrating INS sensors with GPS velocity measurements for continuous estimation of vehicle sideslip and tire cornering stiffness | |
| CN115303265A (en) | Vehicle obstacle avoidance control method and device and vehicle | |
| JP7036080B2 (en) | Inertial navigation system | |
| US9963007B2 (en) | Method for compensating for an inclination | |
| US9618938B2 (en) | Field-based torque steering control | |
| CN104061899A (en) | Kalman filtering based method for estimating roll angle and pitching angle of vehicle | |
| CN107063305B (en) | Method for correcting downhill suspended rear wheel odometer error by using inertial navigation and pressure sensors | |
| CN111240187A (en) | Vehicle track tracking control algorithm based on vehicle error model | |
| CN111452786B (en) | Obstacle avoidance method and system for unmanned vehicle | |
| Park | Vehicle sideslip angle estimation based on interacting multiple model Kalman filter using low-cost sensor fusion | |
| CN114637292A (en) | Vehicle track tracking robust control method and system considering obstacle avoidance | |
| CN116552550A (en) | Vehicle track tracking control system based on parameter uncertainty and yaw stability | |
| CN113581201A (en) | Potential field model-based collision avoidance control method and system for unmanned automobile | |
| Liu et al. | Vehicle State estimation based on adaptive fading unscented Kalman filter | |
| Huang et al. | Estimation of sideslip angle based on extended Kalman filter | |
| CN114502450A (en) | Dead time compensation technique in transverse and longitudinal guidance of motor vehicles | |
| CN115525054B (en) | Method and system for controlling tracking of edge path of unmanned sweeper in large industrial park | |
| Katriniok | Optimal vehicle dynamics control and state estimation for a low-cost GNSS-based collision avoidance system | |
| JP2020098116A (en) | Yaw rate correction device | |
| Kapania et al. | An autonomous lanekeeping system for vehicle path tracking and stability at the limits of handling | |
| CN115432005A (en) | Virtual driving yard weight rolling optimization decision method in ice and snow environment | |
| CN115675481A (en) | GPS-enhanced friction estimation |
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 |