Patent ID: 12240522

DETAILED DESCRIPTION

Detailed embodiments and drawings are combined hereinafter to further elaborate the technical solution of the present invention. These embodiments are implemented based on the technical solution of the present invention. Though a detailed implementation manner and a specific operation process are described, the scope of the present invention is not limited to the following embodiments.

The dynamic properties of vehicles are obviously non-linear in working conditions such as an ice and snow environment, and therefore, the planning for an executable path while taking into account dynamic properties of the vehicles is a guarantee for the safe driving of intelligent vehicles under the extreme operating conditions. The present invention provides a real-time path planning method for intelligent driving taking into account dynamic properties of vehicles. The method of the present invention comprises a calculation of a reachable set of the vehicle without connecting to internet and an online path planning. As shown inFIG.1, the method of the present invention comprising the steps of

Step 1: taking into account the dynamic properties of a vehicle, and calculating a reachable set of the vehicle based on the vehicle state and the wheel lateral force without connecting to internet;

In step 1, specifically, taking into account the implementation mode of the non-linear dynamic constraint of the vehicle, and converting a calculation with connecting to internet into a calculation without connecting to internet and query with connecting to internet to reduce the calculation amount in the online planning process, comprising the steps of

Step 1.1: obtaining an initial state of the vehicle, and estimating the wheel lateral force of the vehicle offline based on a two-degree-of-freedom vehicle model and a non-linear wheel model without connecting to internet; wherein the initial state of the vehicle comprises a transverse vehicle speed, a longitudinal vehicle speed and a yaw velocity; wherein in this embodiment, specifically, a calculation formula for estimating the wheel lateral force is the following:

α1=δf-arctan⁡(vy+α⁢φ.vx)⁢α2=arctan⁡(-vy+b⁢φ.vx)⁢Fyf=μ*m*g*b/(a+b)*(21+e-35⁢α1-1)⁢Fyr=μ*m*g*a/(a+b)*(21+e-35⁢α2-1)

wherein a and b respectively represent the front and rear wheelbase distances of the vehicle, wherein m represents the mass of the vehicle, wherein vxrepresents the transverse vehicle speed, wherein vyrepresents the longitudinal vehicle speed, wherein φ represents the yaw velocity of the vehicle, wherein g represents a gravitational acceleration, wherein μ represents a ground adhesion coefficient, wherein δfrepresents a front wheel rotation angle in the current state, wherein α1and α2respectively represent front and rear wheel side deflection angles of the current vehicle, and wherein Fyfand Fyrrespectively represent the front and rear wheel lateral forces of the vehicle;

In this step, more complex or simpler models may be selected according to the precision requirements for expressing the nonlinearity of vehicle dynamics, which are not limited to the method proposed in this embodiment;

Step 1.2: determining an input amount according to the wheel lateral force of the vehicle, and predicting a vehicle state at a next moment based on a discretized three-degree-of-freedom vehicle model without connecting to internet, wherein in this embodiment, a three-degree-of-freedom vehicle model is used to express the dynamic properties of the vehicle by using a whole state equation as follows:

{v.x=vy⁢φ.+1m⁢Fxf⁢cos⁡(δf)-1m⁢Fyf⁢sin⁡(δf)+1m⁢Fxrv.y=-vx⁢φ.+1m⁢Fxf⁢sin⁡(δf)+1m⁢Fyf⁢cos⁡(δf)+1m⁢Fyrφ¨=LfIz⁢(Fxf⁢sin⁡(δf)+Fyf⁢cos⁡(δf))+LrIz⁢Fyr
wherein in the aforesaid whole state equation, Fyfrepresents a lateral force of a front wheel, wherein Fyrrepresents a lateral force of a rear wheel, wherein Fxfrepresents a longitudinal force of the front wheel, wherein Fxrrepresents a longitudinal force of the rear wheel, wherein vxrepresents the transverse vehicle speed, wherein vyrepresents the longitudinal vehicle speed,
wherein {dot over (φ)} represents the yaw velocity, wherein {umlaut over (φ)} represents a yaw angular acceleration, wherein m represents a mass of the vehicle, wherein δfrepresents a front wheel rotation angle, wherein Lfrepresents a distance from a mass center of the vehicle to a front axle, wherein Lrrepresents a distance from the mass center of the vehicle to a rear axle, and wherein Izrepresents a rotational inertia of the vehicle;

In this embodiment, the discretized three-degree-of-freedom vehicle model is obtained by discretizing the three-degree-of-freedom vehicle model using a third-order third-stage Runge-Kutta formula, the discretized three-degree-of-freedom vehicle model is used to predict the vehicle state at the next moment, wherein a input amount of the discretized three-degree-of-freedom vehicle model is defined as a input matrix u=[FyfFyrFxfFxrδf] and a state matrix x=[vxvy{dot over (φ)}], and the calculation formula of a recursive prediction process is as follows:

{k1=Tf⁡(x,u)k2=Tf⁡(x+12⁢k1,u)k3=Tf⁡(x-k1+2⁢k2,u)x*=x+16⁢(k1+4⁢k2+k3)
wherein T represents a predicted sample time, wherein f represents a replaced symbol of a differential equation of the three-degree-of-freedom vehicle model, wherein k1, k2and k3respectively represent the intermediate variables in the calculation process, and wherein x*=[vx*vy*{dot over (φ)}*] represents a predicted vehicle state matrix at the next moment;

Step 1.3: calculating a position of the vehicle at the next moment based on the initial state of the vehicle and the predicted vehicle state at the next moment by a calculation formula as follows:

{Δ⁢X=12⁢(vx+vx*)⁢TΔ⁢Y=12⁢(vy+vy*)⁢T
wherein ΔX and ΔY represent a range where the vehicle is capable of reaching relative to a current position of the vehicle at the next moment;

Step 1.4: determining a range of feasible input variables according to the wheel lateral force estimated in steps 1.1 and a maximum value of the wheel lateral force of the vehicle, traversing values in the range of feasible input variables as the input amount, and repeating steps 1.2 and 1.3 to obtain a single vehicle reachable set in the initial state;

Step 1.5: determining a range of safe vehicle states according to a maximum value of the transverse vehicle speed, a maximum value of the longitudinal vehicle speed, a maximum value of the yaw velocity, and the initial state of the vehicle; traversing values in the range of the safe vehicle states as the initial state of the vehicle, repeating steps 1.1-1.4 to obtain the reachable set of the vehicle, and storing the reachable set of the vehicle in a database without connecting to internet;

The reachable set of the vehicle is a dataset, and it includes discrete vehicle initial state data, vehicle wheel lateral force data, vehicle next moment state data and vehicle next moment position data; therefore, the calculation of the vehicle reachable set of the vehicle comprises the following three steps:1) Estimating an initial wheel force of the vehicle: estimating the lateral force of the front wheel Fyfand the lateral force of the rear wheel Fyrin the current state according to the initial state of the vehicle, namely, x=[vxvy{dot over (φ)}]; it is worth mentioning that, to cover the safe driving section of the vehicle, a reasonable initial state needs to be traversed, wherein the maximum value of vxis 120, and the traversal sample time is 5, wherein the maximum value of vyis 1, and the traversal sample time is 0.1, and wherein the maximum value of {dot over (φ)} is 3, and the traversal sample time is 0.1;2) Estimating the vehicle state at a next moment x*=[vx*vy*{dot over (φ)}*] by means of a given input state u=[FyfFyrFxfFxrδf] according to the vehicle state prediction; it should be noted that, the reachable set of the vehicle is a dataset, and a reasonable input state needs to be traversed, wherein Fyfand Fyrare calculated and obtained in step 1), wherein Fxfand Fxrdepend on the acceleration performance of the vehicle, the maximum value and the minimum value are respectively 1000 and −1000, and the traversal sample time is 10, wherein δfdepends on the design of the steering mechanism of the vehicle, the maximum value and the minimum value are respectively 20 and −20, and the traversal sample time is 1;3) Predicting a reachable set of the vehicle: calculating the range of position where the vehicle is capable of reaching at the next moment according to the vehicle state obtained in step 2);

Taking the input state x=[0 0 0] as an example, the schematic diagram of the obtained vehicle reachable set is shown inFIG.2. Please note that the X axis inFIG.2defines the “longitudinal transport distance” and the Y axis inFIG.2defines the “lateral transport distance”. As illustrated inFIG.2, a driving range may be divided into three sections in accordance to the wheel force saturation condition of the vehicle when reaching the current position:

a portion of a predicted wheel force at the next moment lower than 50% of a wheel force saturation value is defined as a normal driving section (201) (corresponding to the dark gray area in the very center ofFIG.2), a portion of the predicted wheel force at the next moment greater than 50% and lower than 75% of the wheel force saturation value is defined as an emergency driving section (202) (corresponding to the light gray area in the middle ofFIG.2), and a portion of the predicted wheel force at the next moment greater than 75% of the wheel force saturation value is defined as a dangerous driving section (203) (corresponding to the outermost black area inFIG.2), wherein calculation formulas of the wheel force at the next moment and the wheel force saturation value are:

Ff*=(Fxf2+Fyf*2)⁢Fmax=μ⁢mg⁢LfLf+Lr
wherein Ff* represents the predicted wheel force at the next moment, wherein Fxfrepresents a front wheel longitudinal force, wherein Fyf* represents
a predicted front wheel lateral force at the next moment, wherein Fmaxrepresents the wheel force saturation value, wherein μ represents a ground adhesion coefficient, wherein m represents a mass of the vehicle, wherein g represents a gravitational acceleration, wherein Lfrepresents a distance from a mass center of the vehicle to a front axle, and wherein Lfrepresents a distance from the mass center of the vehicle to a rear axle;

The accuracy of the reachable set of the vehicle is related to the capability of the computing platform, and the model precision may be improved or reduced according to the actual situation;

Step 2: constructing an artificial potential field, and obtaining an online path planning taking into account non-linear properties of the vehicle based on the artificial potential field and the reachable set of the vehicle;

Step 2 is performed in real time, and specifically, quickly planning the path while taking into account the non-linear properties of the vehicle based on the artificial potential field method and the reachable set of the vehicle calculated and stored in step 1 comprising the steps of:

Step 2.1: obtaining the initial state of the vehicle with connecting to internet, querying the reachable set of the vehicle, and taking the reachable set of the vehicle as a range of a end point of a path planned in a current step;

Step 2.1 ensures that the planned path in the next step conforms to the non-linear properties of the vehicle, thereby ensuring the executability of the path;

Step 2.2: constructing an artificial potential field distribution of a scene based on a driving environment;

Step 2.3: selecting a point with a minimum barrier coefficient obtained based on an artificial potential field method in the reachable set of the vehicle queried in step 2.1, and using the point as an end point of the path planned in the current step at the next moment;

Step 2.4: querying a vehicle state at the next moment corresponding to the reachable set of the vehicle according to the end point of the path planned in the current step at the next moment, and taking the vehicle state at the next moment as the initial vehicle state of a planned path in a next predicted sample time;

Step 2.5: repeating steps 2.1-2.4 until a valid duration of a required future path is equal to a sum of predicted sample times, and completing the online path planning; in this embodiment, the valid duration of the required future path is 3 seconds, and the predicted sample time is 0.1 second.

The preferred embodiments of the present invention are described in detail above. It should be understood that modifications and variations may be made by those skilled in the art according to the concept of the present invention without paying creative labor. Therefore, the technical solutions obtained by those skilled in the art according to the concept of the present invention by means of logical analysis, reasoning or limited experiments on the basis of the prior art should fall into the scope defined by the claims of the present invention.