CN114355868A

CN114355868A - Dynamic speed planning method and system for self-driving

Info

Publication number: CN114355868A
Application number: CN202011030846.1A
Authority: CN
Inventors: 张志豪; 许琮明; 林伯翰
Original assignee: Automotive Research and Testing Center
Current assignee: Automotive Research and Testing Center
Priority date: 2020-09-27
Filing date: 2020-09-27
Publication date: 2022-04-15
Anticipated expiration: 2040-09-27
Also published as: CN114355868B

Abstract

The invention provides a dynamic speed planning method and a dynamic speed planning system for self-driving, wherein the dynamic speed planning method for self-driving is used for planning an optimal speed curve for self-driving. The information storage step drives the memory to store obstacle information, road information, and vehicle information of the obstacle. The acceleration limit calculation step calculates vehicle information according to an arithmetic program to generate an acceleration limit value range. The acceleration combination generating step generates a plurality of acceleration combinations according to the obstacle information, the road information and the acceleration limit value range. And the acceleration screening step screens the acceleration combination according to the jerk threshold value to obtain the screened acceleration combination. The acceleration smoothing step executes a driving behavior program to adjust the screened acceleration combinations to generate an optimal speed curve. Therefore, the method can adapt to environmental changes by considering the vehicle operation limit, the vehicle dynamic and the human driving behavior.

Description

Dynamic speed planning method and system for self-driving

Technical Field

The present invention relates to a dynamic speed planning method and system for self-driving, and more particularly, to a dynamic speed planning method and system for self-driving with human driving behavior patterns.

Background

The development of autonomous vehicles has been vigorous in recent years, and many vehicle factories invest a lot of resources, which are prepared for the coming of the self-driving era, and have planned to use unmanned vehicles to operate transportation systems, and have allowed the experimental nature of autonomous vehicles.

At present, Advanced Driver Assistance Systems (ADAS) and Automatic Driving Systems (ADS) of an auto-driven vehicle lack adaptability to the external environment. For example, speed Control of ADAS is mainly based on Auto Cruise Control (ACC), but it lacks speed or acceleration planning, and cannot predict future interaction with a target vehicle. In addition, the ADS, although having a velocity schedule, only considers vehicle operating limits, but does not consider the current vehicle dynamic acceleration and jerk limitations.

Therefore, both ADAS and ADS in the market at present lack the capability of acceleration planning and strain, and how to develop a dynamic speed planning method and system based on the dynamic state of the vehicle and the external environment is really an invaluable desire of people, and is also the target and direction for related manufacturers to make efforts to research and develop breakthroughs.

Disclosure of Invention

Therefore, the present invention aims to provide a dynamic speed planning method and system for self-driving, which first obtains an acceleration limit value range through vehicle information and an operation program, then plans an acceleration combination by fusing barrier information and road information, and simultaneously considers vehicle operation limit, vehicle dynamic and human driving behavior by using the acceleration as a reference, thereby achieving the purpose of adapting to environmental changes, and solving the problems that in the prior art, only map information is considered in a target speed range, future barrier information and self-driving dynamic information are not considered, and even only upper and lower speed limits are manually configured.

According to one aspect of the present invention, a method for dynamic speed planning of self-driving is provided for planning an optimal speed curve of self-driving, the method comprising an information storage step, an acceleration limit calculation step, an acceleration combination generation step, an acceleration filtering step, and an acceleration smoothing step. The information storage step is to drive the memory to store the obstacle information of the obstacle, the road information and the vehicle information of the self-driving, and the vehicle information comprises a jerk threshold value and a jerk switching frequency threshold value. The acceleration limit calculation step is that the driving arithmetic processing unit receives the vehicle information from the memory and calculates the vehicle information according to the arithmetic program to generate the acceleration limit value range of the self-driving. In addition, the acceleration combination generating step is that the driving arithmetic processing unit receives the obstacle information and the road information from the memory, plans an acceleration interval of the self-driving according to the obstacle information, the road information and the acceleration limit value range, and then generates a plurality of acceleration combinations of the self-driving according to the acceleration interval. And the acceleration screening step is that the driving operation processing unit screens acceleration combinations according to the jerk threshold value and the jerk switching frequency threshold value to obtain screened acceleration combinations. The acceleration smoothing step is to drive the arithmetic processing unit to execute the acceleration combination after the driving behavior program is adjusted and screened so as to generate an optimal speed curve.

Therefore, the dynamic speed planning method of the self-driving obtains the range of the limit value of the acceleration through the vehicle information and the operation program, then combines the barrier information and the road information to plan the acceleration combination, and takes the acceleration as the reference and considers the vehicle operation limit and the vehicle dynamic state to achieve the purpose of adapting to the environmental change, thereby ensuring that the future behavior of the self-driving can be estimated.

Other examples of the foregoing embodiments are as follows: the aforementioned vehicle information may further include front wheel steering rigidity, rear wheel steering rigidity, front wheel wheelbase, rear wheel wheelbase, vehicle inertia, and vehicle mass.

Other examples of the foregoing embodiments are as follows: the acceleration limit calculating step may include a lateral acceleration calculating step, a longitudinal acceleration calculating step, and a longitudinal lateral velocity calculating step. The lateral acceleration operation step is that the driving operation processing unit calculates the vehicle information according to the dynamic operation model to generate the lateral acceleration of the self-driving. The longitudinal acceleration operation step is that the driving operation processing unit calculates the lateral acceleration according to the friction circle operation model to generate the longitudinal acceleration of the self-driving. The longitudinal and lateral speed calculation step is that the driving calculation processing unit calculates the lateral acceleration and the longitudinal acceleration respectively according to the kinematics calculation model to generate the longitudinal speed and the lateral speed of the self-driving.

Other examples of the foregoing embodiments are as follows: the acceleration combination generating step may include an acceleration section generating step, which is configured to be implemented by the arithmetic processing unit and includes an obstacle restricting step and a road restricting step. The obstacle limiting step is to limit the acceleration limit range of the self-driving vehicle according to the obstacle information to generate an initial acceleration interval. The road limiting step is used for limiting the initial acceleration interval according to the road information to generate an acceleration interval.

Other examples of the foregoing embodiments are as follows: the acceleration combination generating step may further include an acceleration discrete step implemented by the operation processing unit and including a discrete step and a target point combining step. The discrete step is to generate a plurality of acceleration groups according to a preset time interval and an acceleration interval, and to generate at least one acceleration target point by dispersing each acceleration group according to the preset acceleration interval. The target point combination step is to combine at least one acceleration target point of each acceleration group in sequence to generate acceleration combination.

Other examples of the foregoing embodiments are as follows: each acceleration combination may include a maximum jerk and a jerk switching frequency, and the jerk threshold value is represented by J_thresholdEach maximum jerk is represented by J_maxEach jerk switching frequency is expressed as Jfrequency, and a jerk switching frequency threshold is expressed as f_thresholdAnd conforms to the formula:

J_max≤J_threshold；

∑Jfrequency≤f_threshold。

other examples of the foregoing embodiments are as follows: the acceleration smoothing step may include an adjustment step and a fitting step. The adjusting step is to adjust the screened acceleration combination according to any one of the positive model, the normal model and the conservative model to generate an artificial acceleration combination, and the artificial acceleration combination has a plurality of optimal accelerations. The fitting step is to integrate and smooth the optimal acceleration of the artificial acceleration combination and fit the optimal acceleration into an optimal speed curve.

According to one aspect of the present invention, a self-driving dynamic speed planning system for planning an optimal speed curve of a self-driving vehicle is provided, which includes a memory and an arithmetic processing unit. The memory is used for accessing obstacle information of obstacles, road information, vehicle information of self-driving, an operation program and a driving behavior program, and the vehicle information comprises a jerk threshold value. The operation processing unit is electrically connected with the memory and is configured to implement the operation comprising the following steps: the method comprises an acceleration limit calculation step, an acceleration combination generation step, an acceleration screening step and an acceleration smoothing step. Wherein the acceleration limit calculating step calculates the vehicle information based on the arithmetic program to generate the acceleration limit range for the self-driving. The acceleration combination generating step is to plan an acceleration interval from driving according to the obstacle information, the road information and the acceleration limit value range, and then generate a plurality of acceleration combinations from driving according to the acceleration interval. And the acceleration screening step is to screen the acceleration combination according to the jerk threshold value to obtain the screened acceleration combination. The acceleration smoothing step is to execute the driving behavior program to adjust the screened acceleration combination to generate an optimal speed curve.

Therefore, the self-driving dynamic speed planning system plans the feasible acceleration combination from the driving through the obstacle information, the road information and the vehicle information, and then screens out the artificial acceleration combination which accords with the driving behavior of the human through the sudden jump degree limiting condition and the driving behavior program, so that the purposes of adapting to the future environment change and improving the comfort level of passengers are achieved.

Other examples of the foregoing embodiments are as follows: the memory may include a dynamic operation model, a friction circle operation model, and a kinematic operation model, and the acceleration limit calculation step may include a lateral acceleration operation step, a longitudinal acceleration operation step, and a longitudinal lateral velocity operation step. The lateral acceleration operation step is to calculate the vehicle information according to the dynamic operation model to generate the lateral acceleration of the self-driving. The longitudinal acceleration operation step is to calculate the lateral acceleration according to the friction circle operation model to generate the longitudinal acceleration of the self-driving. The longitudinal and lateral speed calculation step is to calculate the lateral acceleration and the longitudinal acceleration respectively according to the kinematics calculation model to generate the longitudinal speed and the lateral speed of the self-driving.

Other examples of the foregoing embodiments are as follows: the arithmetic processing unit is configured to perform an acceleration section generating step including an obstacle restricting step and a road restricting step. The obstacle limiting step is to limit the acceleration limit range of the self-driving vehicle according to the obstacle information to generate an initial acceleration interval. The road limiting step is used for limiting the initial acceleration interval according to the road information to generate an acceleration interval.

Other examples of the foregoing embodiments are as follows: the arithmetic processing unit is configured to perform an acceleration discretization step including a discretization step and a target point combining step. The discrete step is to generate a plurality of acceleration groups according to a preset time interval and an acceleration interval, and to generate at least one acceleration target point by dispersing each acceleration group according to the preset acceleration interval. The target point combination step is to combine at least one acceleration target point of each acceleration group in sequence to generate acceleration combination.

J_max≤J_threshold；

∑Jfrequency≤f_threshold。

other examples of the foregoing embodiments are as follows: the arithmetic processing unit is configured to perform an acceleration smoothing step, which includes an adjustment step and a fitting step. The adjusting step is to adjust the screened acceleration combination according to any one of the positive model, the normal model and the conservative model to generate an artificial acceleration combination, and the artificial acceleration combination has a plurality of optimal accelerations. The fitting step is to integrate and smooth the optimal acceleration of the artificial acceleration combination and fit the optimal acceleration into an optimal speed curve.

Drawings

FIG. 1 is a flow chart illustrating a dynamic speed planning method for self-driving according to a first embodiment of the present invention;

FIG. 2 is a flow chart illustrating a dynamic speed planning method for self-driving according to a second embodiment of the present invention;

FIG. 3 is a schematic diagram illustrating the information storage steps of the dynamic speed planning method for self-driving of FIG. 2;

FIG. 4 is a schematic diagram showing the acceleration limit calculation step of the self-driving dynamic velocity planning method of FIG. 2;

FIG. 5 is a schematic diagram showing the acceleration interval generation step of the dynamic speed planning method for self-driving of FIG. 2;

FIG. 6 is a schematic diagram illustrating the application of the self-driving dynamic speed planning method of FIG. 2 to lane obstacle avoidance;

FIG. 7 is a schematic diagram illustrating the acceleration discrete steps of the self-driving dynamic velocity planning method of FIG. 2;

FIG. 8 is a schematic diagram showing an acceleration screening step of the dynamic speed planning method of self-driving of FIG. 2;

FIG. 9 is a schematic diagram showing an aggressive model, a normal model and a conservative model of the dynamic speed planning method of self-driving of FIG. 2;

FIG. 10 is a schematic diagram showing the adjustment steps of the dynamic speed planning method for self-driving of FIG. 2;

FIG. 11 is a schematic diagram showing the fitting steps of the self-driving dynamic speed planning method of FIG. 2; and

fig. 12 is a block diagram illustrating a third embodiment of a dynamic speed planning system for self-driving according to the present invention.

[ notation ] to show

100,100a dynamic speed planning method for self-driving

102 vehicle information

103 range of acceleration of obstacle

104 acceleration limit Range

104a initial acceleration interval

105 acceleration interval

106,1061,1062 acceleration combination

108 post-screening acceleration combinations

108a artificial acceleration simulating combination

108b speed combination

110 optimum speed Curve

200 dynamic speed planning system for self-driving

300 sensing module

400 memory

500 arithmetic processing unit

HV self-driving

V_HVSelf-driving speed

a_HVAcceleration of self-driving

Obj obstacle

V_ObjSpeed of obstacle

a_ObjAcceleration of an obstacle

V_maxRoad speed ceiling

V_minLower limit of road speed

u longitudinal velocity

v lateral velocity

a_xLongitudinal acceleration

a_yLateral acceleration

X is the longitudinal direction

Y is in the lateral direction

G₁,G₂,G₃,G₄Acceleration group

a_T,a_T1,a_T2,a_T3,a_T4Acceleration target point

a_f1,a_f2,a_f3,a_f4Optimum acceleration

M1 active model

M2 Normal model

M3 conservative model

Trend curve C

a acceleration

Velocity V

Angle of rotation of steering wheel

S02, S12 information storage step

S04, S14 acceleration limit calculation step

S142, calculating lateral acceleration

S144, longitudinal acceleration operation step

S146, longitudinal and lateral velocity calculating step

S06, S16 acceleration combination generating step

S162 acceleration interval generation step

S1622 obstacle limiting step

S1624 road limiting step

S164 acceleration dispersing step

S1642 discrete step

S1644 target point combination step

S08, S18 acceleration screening step

S10, S20 acceleration smoothing step

S202, an adjusting step

S204, fitting step

S22 control step

Detailed Description

Various embodiments of the present invention will be described below with reference to the accompanying drawings. For the purpose of clarity, numerous implementation details are set forth in the following description. It should be understood, however, that these implementation details are not to be interpreted as limiting the invention. That is, in some embodiments of the invention, these implementation details are not necessary. In addition, for the sake of simplicity, some conventional structures and elements are shown in the drawings in a simple schematic manner; and repeated elements will likely be referred to using the same reference numerals.

In addition, when an element (or a unit or a module, etc.) is "connected" to another element, it can mean that the element is directly connected to the other element or that the element is indirectly connected to the other element, i.e., that there is another element between the element and the other element. When an element is explicitly described as being "directly connected" to another element, it is not intended that another element be interposed between the element and the other element. The terms first, second, third and the like are used for describing different elements only, and the elements themselves are not limited, so that the first element can be also called the second element. And the combination of elements/units/circuits herein is not a commonly known, conventional or known combination in the art, and cannot be readily determined by a person of ordinary skill in the art whether the combination is readily accomplished by knowing whether the elements/units/circuits themselves are known.

Referring to fig. 1, fig. 1 is a flow chart illustrating a dynamic speed planning method 100 for self-driving according to a first embodiment of the invention. The method 100 for planning the dynamic speed of the self-driving vehicle is used for planning the optimal speed curve 110 of the self-driving vehicle, and the method 100 for planning the dynamic speed of the self-driving vehicle comprises an information storage step S02, an acceleration limit calculation step S04, an acceleration combination generation step S06, an acceleration filtering step S08 and an acceleration smoothing step S10.

In the information storage step S02, the driving memory stores obstacle information of obstacles, road information, and vehicle information 102 of the self-driving vehicle, and the vehicle information 102 includes a jerk threshold and a jerk switching frequency threshold. The acceleration limit calculating step S04 is a driving calculation processing unit receiving the vehicle information 102 from the memory and calculating the vehicle information 102 according to a calculation program to generate the self-driving acceleration limit value range 104. In addition, the acceleration combination generating step S06 is that the driving arithmetic processing unit receives the obstacle information and the road information from the memory, and plans the acceleration section from the driving according to the obstacle information, the road information and the acceleration limit value range 104, and then generates a plurality of acceleration combinations 106 from the driving according to the acceleration section. In the acceleration screening step S08, the driving arithmetic processing unit screens the acceleration combination 106 according to the jerk threshold and the jerk switching frequency threshold to obtain a screened acceleration combination 108. The acceleration smoothing step S10 is to drive the arithmetic processing unit to execute a driving behavior program to adjust the filtered acceleration combination 108 to generate the optimal speed curve 110. Therefore, the dynamic speed planning method 100 of the self-driving of the present invention obtains the acceleration limit range 104 through the vehicle information 102 and the calculation program, then combines the obstacle information and the road information to plan the acceleration combination 106, and uses the acceleration as a reference, and considers the vehicle operation limit, the vehicle dynamics and the human driving behaviors at the same time, so as to achieve the purpose of adapting to the environmental change, thereby enabling the future behavior of the self-driving to be predictable. The details of the above steps will be described below by way of more detailed examples.

Referring to fig. 2 to 11, fig. 2 is a flow chart illustrating a dynamic speed planning method 100a for self-driving according to a second embodiment of the present invention; FIG. 3 is a diagram illustrating the information storing step S12 of the method 100a for dynamic speed planning for self-driving of FIG. 2; FIG. 4 is a schematic diagram illustrating the acceleration limit calculating step S14 of the self-driving dynamic velocity planning method 100a of FIG. 2; FIG. 5 is a schematic diagram illustrating the acceleration interval generating step S162 of the dynamic speed planning method 100a for self-driving of FIG. 2; FIG. 6 is a schematic diagram illustrating the application of the self-driving dynamic speed planning method 100a of FIG. 2 to lane obstacle avoidance; FIG. 7 is a schematic diagram illustrating the acceleration discretization step S164 of the self-driving dynamic velocity planning method 100a of FIG. 2; FIG. 8 is a schematic diagram illustrating the acceleration filtering step S18 of the dynamic speed planning method 100a for self-driving of FIG. 2; FIG. 9 is a schematic diagram showing the aggressive model M1, the normal model M2, and the conservative model M3 of the dynamic speed planning method 100a of self-driving of FIG. 2; FIG. 10 is a schematic diagram illustrating the adjusting step S202 of the dynamic speed planning method 100a for self-driving of FIG. 2; and FIG. 11 is a schematic diagram showing the fitting step S204 of the self-driving dynamic speed planning method 100a of FIG. 2. As shown, the self-driving dynamic speed planning method 100a is used to plan the optimal speed curve 110 of the self-driving HV, and the self-driving dynamic speed planning method 100a includes an information storage step S12, an acceleration limit calculation step S14, an acceleration combination generation step S16, an acceleration filtering step S18, an acceleration smoothing step S20, and a control step S22.

In the information storage step S12, the drive memory stores obstacle information of the obstacle Obj, road information, and vehicle information 102 of the self-driving HV. In detail, the self-driving HV includes a sensing module for sensing obstacle information including an obstacle speed V of an obstacle Obj, road information, and vehicle information 102, and storing the same in a memory_ObjWith obstacle acceleration a_ObjAnd an obstacle acceleration range 103. The road information includes an upper limit V of the road speed_maxWith lower limit of road speed V_min. The vehicle information 102 may include jerk threshold values of the self-driving HV, front wheel steering stiffness, rear wheel steering stiffness, front wheel base, rear wheel base, vehicle inertia, and vehicle mass.

The acceleration limit calculation step S14 is a drive arithmetic processing unit that receives the vehicle information 102 from the memory and calculates the vehicle information 102 according to an arithmetic program to generate the acceleration limit value range 104 of the self-driving HV. Specifically, the acceleration limit calculation step S14 may include a lateral acceleration calculation step S142, a longitudinal acceleration calculation step S144, and a longitudinal lateral velocity calculation step S146. The lateral acceleration operation step S142 is a step in which the driving operation processing unit estimates the lateral acceleration a of the self-driving HV by estimating the vehicle information 102 according to a Dynamics (Dynamics) operation model_y. In the longitudinal acceleration calculation step S144, the driving calculation processing unit calculates the lateral acceleration a according to a Friction circle (Friction circle) calculation model_yTo generate a longitudinal acceleration a of the self-driving HV_x. In the longitudinal and lateral velocity calculating step S146, the driving calculation processing unit respectively calculates the lateral acceleration a according to a kinematics calculation model_yWith longitudinal acceleration a_xResulting in a longitudinal velocity u and a lateral velocity v of the self-driving HV.

More specifically, the calculation program includes a dynamic calculation model, a friction circle calculation model, and a kinematic calculation model. First, the dynamic operation model includes a lateral force F_yMass m, acceleration of vehicle

Longitudinal velocity u, yaw rate r, yaw acceleration

Front wheel side force F_yfRear wheel side force F_yrAnd vehicle inertia I_ZAnd satisfies the following formula (1):

the arithmetic processing unit steers the front wheel steering rigidity C of the vehicle information 102 in accordance with the dynamic operation model_αfRear wheel steering rigidity C_αrFront wheel base a, rear wheel base b and vehicle inertia I_ZAnd substituting the vehicle mass m into the equation (1) and deriving the following equation (2):

where v is the lateral velocity, δ_fIs the front wheel angle, and t is the time. The arithmetic processing unit carries out matrix multiplication and expansion on the formula (2) and obtains the lateral acceleration a after the matrix multiplication and the expansion are finished_yWhich complies with the following formula (3):

then, the friction circle operation model includes the available maximum longitudinal force F_x，maxLongitudinal force F_xMaximum lateral force F available_y，maxLateral force F_yMaximum longitudinal acceleration a_x，maxMaximum lateral acceleration a_y，maxLongitudinal acceleration a_xAnd lateral acceleration a_yAnd conforms to the following equation (4). The arithmetic processing unit generates a longitudinal acceleration a by shifting and eliminating the equation (4)_xWhich complies with the following formula (5):

finally, the kinematic operation model comprises a velocity V and an initial velocity V₀Acceleration of the vehicle

And time t, and according to the following equation (6), the arithmetic processing unit generates the longitudinal speed u and the lateral speed v according to the vehicle kinematics model operation, which is according to the following equation (7):

wherein S is a distance, u₀Is the initial longitudinal velocity, v₀Is the initial lateral velocity. Therefore, the dynamic speed planning method 100a for self-driving generates the lateral acceleration a through the vehicle information 102 and the dynamic operation model_yThen, the longitudinal acceleration a is generated through the friction circle calculation model_xFinally, the longitudinal velocity u and the lateral velocity v are generated through a kinematic calculation model. It is worth noting that the acceleration a of the self-propelled vehicle in the longitudinal direction X and in the lateral direction Y on the future path_HVRespectively the above-mentioned longitudinal acceleration a_xWith lateral acceleration a_ySelf-driving speed V of longitudinal X and lateral Y_HVThe longitudinal velocity u and the lateral velocity v, respectively, and the self-driving acceleration a_HVThe upper and lower ranges of (d) are acceleration limit value ranges 104 of the self-driving HV.

The acceleration combination generating step S16 may include an acceleration interval generating step S162 and an acceleration adding stepThe speed discrete step S164, wherein the acceleration interval generating step S162 may include an obstacle limiting step S1622 and a road limiting step S1624. The obstacle limiting step S1622 is to limit the acceleration limit value range 104 of the self-driving HV according to the obstacle information to generate the initial acceleration section 104 a. The road limiting step S1624 is to limit the initial acceleration segment 104a according to the road information to generate the acceleration segment 105. Specifically, the arithmetic processing unit calculates the obstacle acceleration range 103 (i.e., the obstacle acceleration a) based on the obstacle information_ObjUpper and lower limit ranges) limits the acceleration limit value range 104 to generate an initial acceleration section 104 a. Then, the arithmetic processing unit bases on the upper limit V of the road speed_maxWith lower limit of road speed V_minAn acceleration limit 104 is captured to generate an acceleration interval 105. Thus, in the general lane, the self-driving dynamic speed planning method 100a of the present invention further limits the acceleration limit value range 104 of the self-driving HV using the road information and the obstacle information to estimate the acceleration range (i.e., the acceleration section 105) to which the self-driving HV can be applied.

In addition, the acceleration discretization step S164 can include a discretization step S1642 and a target point combining step S1644, wherein the discretization step S1642 generates a plurality of acceleration groups G according to the predetermined time interval and the acceleration interval 105₁、G₂、G₃、G₄And the respective accelerations are grouped into G₁、G₂、G₃、G₄Generating at least one acceleration target point a according to the preset acceleration interval dispersion_T. The target point combination step S1644 is to sequentially combine each acceleration group G₁、G₂、G₃、G₄At least one acceleration target point a_TCombine with each other to produce a plurality of acceleration combinations 106. For example, when the predetermined acceleration interval is 1m/s²The acceleration interval 105 of the first track point of the self-driving HV is [1,1 ] when the preset time interval is 0.1 second as the reference]m/s²Then there is only one acceleration value (i.e. acceleration target point a)_T) Then, according to the first trace point, calculating the acceleration value of the next trace point, and the acceleration interval 105 of the next time is [ -3,5]m/s²Can cut-out-3, -2, -1, 0,1, 2, 3, 4 and 5m/s²9 acceleration values are calculated, and the acceleration value of the next track point is calculated according to the current track point, and the acceleration interval 105 of the next time is [ -4,8 [ -4]m/s²And so on (as shown in fig. 7), but the invention is not limited thereto.

In the acceleration filtering step S18, the driving arithmetic processing unit filters the acceleration combination 106 according to the jerk threshold to obtain a filtered acceleration combination 108. Specifically, each acceleration combination 106 includes a maximum jerk, each maximum jerk is less than or equal to a jerk threshold, and each maximum jerk is represented by J_maxThe jerk threshold value is represented by J_thresholdAnd satisfies the following formula (8):

J_max≤J_threshold (8)。

in detail, the acceleration combination 106 includes an acceleration combination 1061 and an acceleration combination 1062. Jerk threshold value J_thresholdCan be 20m/s³. In the

acceleration combination

1061, 1m/s from the beginning²Jump to 2m/s²Generate 10m/s³The jerk of (3 m/s) last²Jump to 1m/s²Generate 10m/s³Is the maximum jerk J of the acceleration combination 1061_max). In the

acceleration pattern

1062, 1m/s from the beginning²Jump to-3 m/s²Generate 40m/s³Is the maximum jerk J of the acceleration combination 1062_max) Last 1m/s²Jump to-1 m/s²Generate 20m/s³The degree of jerk of. Therefore, the arithmetic processing unit depends on the jerk threshold J_thresholdThe acceleration combination 1062 is eliminated.

In addition, the vehicle information 102 may further include a jerk switching frequency threshold of the self-driving HV, the jerk switching frequency threshold is stored in the memory, and each acceleration combination 106 may further include a jerk switching frequency. Specifically, each jerk switching frequency is less than or equal to a jerk switching frequency threshold, each jerk switching frequency is represented by Jfrequency, and the jerk switching frequency threshold is represented by f_thresholdAnd conforms to the following formula (9):

∑Jfrequency≤f_threshold (9)。

in detail, the jerk switching frequency threshold f_thresholdMay be 2. When the jerk has positive and negative switching, the jerk switching frequency Jfrequency is accumulated for 1 time. In the

acceleration combination

1061, 1m/s from the beginning²Jump to 2m/s²From 2m/s again²Jump to 3m/s²And again from 3m/s²Jump to 1m/s²The jerk switching frequency Jfrequency of the acceleration combination 1061 is 0. In the

acceleration pattern

1062, 1m/s from the beginning²Jump to-3 m/s²From-3 m/s²Jump to 1m/s²Again from 1m/s²Jump to-1 m/s²The jerk switching frequency Jfrequency of the acceleration combination 1062 is 3. Therefore, the arithmetic processing unit switches the frequency threshold f according to the jerk_thresholdThe acceleration combination 1062 is eliminated. Therefore, the method 100a for dynamic speed planning of self-driving according to the present invention dynamically screens the acceleration combination 106 for the vehicle through the above-mentioned limitation conditions, and further generates the screened acceleration combination 108 (i.e. the acceleration combination 1061).

In the acceleration smoothing step S20, the driving arithmetic processing unit executes the driving behavior program to adjust the filtered acceleration combination 108 and generate the optimal speed curve 110. The driving behavior program is classified into an aggressive model M1, a normal model M2, and a conservative model M3 according to the acceleration a, the speed V, and the steering wheel angle θ.

In addition, the acceleration smoothing step S20 may include an adjusting step S202 and a fitting step S204, wherein the adjusting step S202 adjusts the filtered acceleration combination 108 according to any one of the positive model M1, the normal model M2 and the conservative model M3 to generate the artificial acceleration combination 108a, and the artificial acceleration combination 108a has a plurality of optimal accelerations a_f1、a_f2、a_f3、a_f4. The fitting step S204 is to combine each optimal acceleration a of the simulated artificial accelerations 108a_f1、a_f2、a_f3、a_f4Integrated and smoothed and fitted to the optimal velocity curve 110.

In detail, the aggressive model M1, the normal model M2 and the conservative model M3 include corresponding trend curves C. The arithmetic processing unit respectively adjusts the acceleration target points a in the acceleration combination 108 after screening according to the trend curve C which is closer to the acceleration combination 108 after screening_T1、a_T2、a_T3、a_T4To an optimum acceleration a_f1、a_f2、a_f3、a_f4. For example, acceleration target point a_T2(-3m/s²) Adjusted to the optimum acceleration a_f2(-2m/s²). Finally, the artificial acceleration combination 108a is converted into a velocity combination 108b by an integral method, and the velocity combination 108b is curve-fitted, so that the velocity combination 108b is smoothed and an optimal velocity curve 110 is generated. The control step S22 is based on the optimal speed curve 110 as an automatic driving parameter for controlling the self-driving HV, and the details thereof are known in the art and thus will not be described again.

Therefore, the dynamic speed planning method 100a of the self-driving of the invention changes the acceleration rate through the driving behavior program, further obtains the artificial acceleration combination 108a conforming to the human driving behavior, then fits the speed combination 108b into a smooth curve, achieves the purpose of solving the control shock caused by the problem of discontinuous speed, and can further estimate the collision time of the self-driving HV and the obstacle Obj according to the optimal speed curve 110, further predict the interaction relationship of the self-driving HV and the obstacle Obj.

Referring to fig. 2 to 12 together, fig. 12 is a block diagram illustrating a dynamic speed planning system 200 for self-driving according to a third embodiment of the present invention. The self-driving dynamic speed planning system 200 is used for planning an optimal speed curve 110 of a self-driving HV, and the self-driving dynamic speed planning system 200 includes a sensing module 300, a memory 400 and an arithmetic processing unit 500.

The sensing module 300 is used for sensing the obstacle information, the road information and the vehicle information 102, and storing the information in the memory 400, wherein the obstacle information includes the obstacle speed V of the obstacle Obj_ObjWith obstacle acceleration a_Obj. The road information includes an upper limit V of the road speed_maxWith lower limit of road speed V_min. The vehicle information 102 may include jerk threshold values of the self-driving HV, front wheel steering stiffness, rear wheel steering stiffness, front wheel base, rear wheel base, vehicle inertia, and vehicle mass. The sensing module 300 may include a GPS, a Gyroscope (gyro), an odometer (odometer), a speedometer (Speed Meter), an Inertial Measurement Unit (IMU), a Radar (Lidar), a Radar (Radar), and a camera, and the sensing module 300 is known in the art and thus will not be described herein.

The memory 400 is used for accessing the obstacle information of the obstacle Obj, the road information, the vehicle information 102 of the self-driving HV, the calculation program and the driving behavior program, wherein the driving behavior program is classified into an aggressive model M1, a normal model M2 and a conservative model M3 according to the acceleration a, the speed V and the steering wheel angle theta, and the vehicle information 102 comprises a jerk threshold J of the self-driving HV_thresholdWith a jerk switching frequency threshold f_threshold。

The operation processing Unit 500 is electrically connected to the memory 400 and the sensing module 300, and the operation processing Unit 500 is configured to implement the dynamic speed planning method 100,100a for self-driving, which may be a microprocessor, an Electronic Control Unit (ECU), a computer, a mobile device or other operation processor.

Therefore, the self-driving dynamic speed planning system 200 plans the feasible acceleration combination 106 from the driving HV through the obstacle information, the road information and the vehicle information 102, and then screens out the artificial acceleration combination 108a which accords with the human driving behavior through the jerk limiting condition and the driving behavior program, so as to adapt to the future environmental change and improve the comfort level of passengers.

As can be seen from the above embodiments, the present invention has the following advantages: firstly, an acceleration limit value range is obtained through vehicle information and an operation program, then an acceleration combination is planned by combining obstacle information and road information, and the acceleration is used as a reference, and meanwhile, the vehicle operation limit, the vehicle dynamic state and the human driving behavior are considered, so that the environment change can be adapted to. Secondly, a feasible acceleration combination is dynamically screened out through the jerk threshold value and the jerk switching frequency threshold value, the change rate of the acceleration is reduced, and the comfort level of passengers is improved. Thirdly, the speed planning of the environmental change can be dealt with, and the situation that the general market-sold system can not deal with can be dealt with, for example: the automatic driving speed planning system is more robust and safer to environment sensing changes due to barrier intrusion, speed matching of lane change and the like.

Although the present invention has been described with reference to the above embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention.

Claims

1. A dynamic speed planning method for self-driving is used for planning an optimal speed curve of the self-driving, and is characterized in that the dynamic speed planning method for the self-driving comprises the following steps:

an information storage step, driving a memory to store obstacle information of an obstacle, road information and vehicle information of the self-driving vehicle, wherein the vehicle information comprises a jerk threshold value and a jerk switching frequency threshold value;

an acceleration limit calculation step of driving an arithmetic processing unit to receive the vehicle information from the memory and calculating the vehicle information according to an arithmetic program to generate an acceleration limit range of the self-driving;

an acceleration combination generating step of driving the arithmetic processing unit to receive the obstacle information and the road information from the memory, and planning an acceleration section of the self-driving according to the obstacle information, the road information and the acceleration limit value range, and then generating a plurality of acceleration combinations of the self-driving according to the acceleration section;

an acceleration screening step, which is to drive the arithmetic processing unit to screen the acceleration combinations according to the jerk threshold value and the jerk switching frequency threshold value to obtain a screened acceleration combination; and

and an acceleration smoothing step, namely driving the operation processing unit to execute a driving behavior program to adjust the screened acceleration combination to generate the optimal speed curve.

2. The method of claim 1, wherein the vehicle information further comprises a front wheel steering stiffness, a rear wheel steering stiffness, a front wheel base, a rear wheel base, a vehicle inertia, and a vehicle mass.

3. The method of claim 1, wherein the step of calculating the acceleration limit comprises:

a lateral acceleration calculation step, in which the calculation processing unit is driven to calculate the vehicle information according to a dynamic calculation model so as to generate a lateral acceleration of the self-driving;

a longitudinal acceleration operation step, driving the operation processing unit to calculate the lateral acceleration according to a friction circle operation model to generate a longitudinal acceleration of the self-driving; and

and a longitudinal and lateral speed calculation step, in which the calculation processing unit is driven to calculate the lateral acceleration and the longitudinal acceleration respectively according to a kinematic calculation model to generate a longitudinal speed and a lateral speed of the self-propelled vehicle.

4. The method of claim 1, wherein the step of generating the acceleration combination comprises:

an acceleration interval generating step, configured to be performed by the arithmetic processing unit, the acceleration interval generating step comprising:

an obstacle limiting step for limiting the acceleration limit range of the self-driving vehicle according to the obstacle information to generate an initial acceleration interval; and

a road limiting step, which is used for limiting the initial acceleration interval according to the road information to generate the acceleration interval.

5. The method of claim 4, wherein the step of generating the acceleration combination further comprises:

an acceleration discretization step, configured and implemented by the arithmetic processing unit, the acceleration discretization step comprising:

a dispersion step, generating a plurality of acceleration groups according to a preset time interval and the acceleration interval, and dispersing each acceleration group according to a preset acceleration interval to generate at least one acceleration target point; and

a target point combination step, which is to combine the at least one acceleration target point of each acceleration group in sequence to generate a plurality of acceleration combinations.

6. The method of claim 1, wherein each acceleration combination comprises a maximum jerk and a jerk switching frequency, and the jerk threshold is represented by J_thresholdEach of said maximum jerks is represented by J_maxEach of the jerk switching frequencies is represented as Jfrequency, and the jerk switching frequency threshold is represented as f_thresholdAnd conforms to the formula:

J_max≤J_threshold；

∑Jfrequency≤f_threshold。

7. the dynamic speed planning method for self-driving according to claim 1, wherein the acceleration smoothing step comprises:

an adjustment step of adjusting the screened acceleration combination according to any one of an active model, a normal model and a conservation model to generate an artificial acceleration combination, wherein the artificial acceleration combination has a plurality of optimal accelerations; and

and a fitting step, namely integrating and smoothing the optimal accelerations of the artificial acceleration combination and fitting the optimal accelerations into the optimal speed curve.

8. A dynamic speed planning system for self-driving, for planning an optimal speed profile of self-driving, the dynamic speed planning system comprising:

a memory for accessing an obstacle information, a road information, a vehicle information of the self-driving, an operation program and a driving behavior program of an obstacle, wherein the vehicle information includes a jerk threshold value; and

an arithmetic processing unit electrically connected to the memory, the arithmetic processing unit configured to perform operations comprising:

an acceleration limit calculation step of calculating the vehicle information according to an operation program to generate an acceleration limit range of the self-driving;

an acceleration combination generating step, namely, an acceleration interval of the self-driving is planned according to the obstacle information, the road information and the acceleration limit value range, and then a plurality of acceleration combinations of the self-driving are generated according to the acceleration interval;

an acceleration screening step, which is to screen the acceleration combinations according to the jerk threshold value to obtain a screened acceleration combination; and

and an acceleration smoothing step, which executes a driving behavior program to adjust the screened acceleration combination to generate the optimal speed curve.

9. The system of claim 8, wherein the vehicle information further comprises a front wheel steering stiffness, a rear wheel steering stiffness, a front wheel wheelbase, a rear wheel wheelbase, a vehicle inertia, and a vehicle mass.

10. The system of claim 8, wherein the memory comprises a dynamic operational model, a friction circle operational model and a kinematic operational model, and the acceleration limit calculating step comprises:

a lateral acceleration calculation step, which calculates the vehicle information according to the dynamic calculation model to generate a lateral acceleration of the self-driving;

a longitudinal acceleration calculation step, which calculates the lateral acceleration according to the friction circle calculation model to generate a longitudinal acceleration of the self-driving; and

a longitudinal and lateral speed calculation step, which calculates the lateral acceleration and the longitudinal acceleration according to the kinematic calculation model to generate a longitudinal speed and a lateral speed of the self-driving.

11. The system of claim 8, wherein the computing unit is configured to perform an acceleration interval generation step, the acceleration interval generation step comprising:

12. The system of claim 11, wherein the computing unit is configured to perform an acceleration discretization step comprising:

13. The system of claim 8, wherein each acceleration combination comprises a maximum jerk and a jerk switching frequency, and the jerk threshold is represented by J_thresholdEach of saidMaximum jerk is denoted J_maxEach of the jerk switching frequencies is represented as Jfrequency, and the jerk switching frequency threshold is represented as f_thresholdAnd conforms to the formula:

J_max≤J_threshold；

∑Jfrequency≤f_threshold。

14. the system of claim 8, wherein the computing unit is configured to perform the acceleration smoothing step, the acceleration smoothing step comprising: