U.S. patent application number 17/605448 was filed with the patent office on 2022-06-23 for method for estimating and adjusting the speed and acceleration of a vehicle.
This patent application is currently assigned to RENAULT s.a.s.. The applicant listed for this patent is NISSAN MOTOR CO., LTD., RENAULT s.a.s.. Invention is credited to Joan DAVINS-VALLDAURA, Renaud DEBORNE, Paul LEMIERE, Denis MALLOL, Guillermo PITA-GIL.
Application Number | 20220194392 17/605448 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-23 |
United States Patent
Application |
20220194392 |
Kind Code |
A1 |
DAVINS-VALLDAURA; Joan ; et
al. |
June 23, 2022 |
METHOD FOR ESTIMATING AND ADJUSTING THE SPEED AND ACCELERATION OF A
VEHICLE
Abstract
A method for estimating the speed of a motor vehicle includes
defining a first speed threshold that corresponds to a minimum
speed value supplied by a vehicle wheel angular speed sensor,
defining a second speed threshold that is greater than the first,
estimating low speed values when the vehicle is running below the
first speed threshold by using an estimation method of adaptive
filtered type, measuring high speed values when the vehicle is
running above the second speed threshold by using vehicle speed
values supplied by the wheel angular speed sensor, and in the
intermediate zone between the first and second speed thresholds,
mixing high speed with low speed.
Inventors: |
DAVINS-VALLDAURA; Joan; (Le
Chesnay, FR) ; LEMIERE; Paul; (Gace, FR) ;
PITA-GIL; Guillermo; (Versailles, FR) ; MALLOL;
Denis; (Provins, FR) ; DEBORNE; Renaud; (Le
Chesnay, FR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
RENAULT s.a.s.
NISSAN MOTOR CO., LTD. |
Boulogne-Billancourt
Yokohama-Shi |
|
FR
JP |
|
|
Assignee: |
RENAULT s.a.s.
Boulogne-Billancourt
FR
NISSAN MOTOR CO., LTD.
Yokohama-Shi
JP
|
Appl. No.: |
17/605448 |
Filed: |
April 23, 2019 |
PCT Filed: |
April 23, 2019 |
PCT NO: |
PCT/EP2019/060287 |
371 Date: |
October 21, 2021 |
International
Class: |
B60W 40/105 20060101
B60W040/105; B60W 50/00 20060101 B60W050/00 |
Claims
1-6. (canceled)
7. A method for estimating the speed of a motor vehicle comprising:
defining a first speed threshold that corresponds to a minimum
speed value supplied by a vehicle wheel angular speed sensor;
defining a second speed threshold that is greater than the first
speed threshold; estimating low speed values when the vehicle is
running below the first speed threshold using an estimation method
of adaptive filtered type; measuring high speed values when the
vehicle is running above the second speed threshold by using
vehicle speed values supplied by the wheel angular speed sensor;
and mixing, in the intermediate zone between the first speed
threshold and the second speed threshold, high speed with low
speed.
8. The method according to claim 7, wherein the adaptive filter is
a Kalman filter.
9. The method according to claim 8, wherein, in the intermediate
zone between the first speed threshold and the second speed
threshold, the mixing is done periodically at successive instants
by using a linear mixing method according to the formula: Speed =
Speed .times. .times. low kalman .times. SV .times. .times. 2 -
Speed t - 1 SV .times. .times. 2 - SV .times. .times. 1 + Speed
.times. .times. high vehicule .times. Speed t - 1 - SV .times.
.times. 1 SV .times. .times. 2 - SV .times. .times. 1 ##EQU00008##
in which speed is the mixed speed at the current instant t,
speed.sub.t-1 is the mixed speed at the instant t-1 at the
preceding mixing instant t-1 (Speed.sub.Kalman.sup.Low) is the
speed value calculated by the Kalman method at the current instant
t, (Speed.sub.vehicle.sup.high) is the speed value measured by the
angular sensor at the current instant t, SV1 is first speed
threshold, and SV2 is the second speed threshold.
10. The method according to claim 7, wherein the first threshold is
1 km/h.
11. The method according to claim 7, wherein the second speed
threshold is 1.5 km/h.
12. The method according to claim 8, wherein the estimating
includes estimating a value of acceleration using the Kalman filter
and the mixing includes a mixing of the acceleration values between
the first speed threshold and the second speed threshold.
Description
TECHNICAL FIELD
[0001] The invention relates to the field of motor vehicles. It
relates more particularly to a strategy for estimating the speed
and the associated acceleration, ranging from high speeds to very
low speeds, dispensing with the current sensor limits.
STATE OF THE ART
[0002] In the context of the development of control laws, the
knowledge of a precise speed and of an associated acceleration are
very important. For example, the control laws used on the ADAS
(Advanced Driver Assistance Systems) systems and the driverless
vehicle still need to have speed and acceleration information.
[0003] On the current vehicles, the speed and the acceleration are
already calculated accurately above a certain threshold. If the
real speed is below this threshold, the information on the speed
and the acceleration is not available. This range of speeds is
commonly referred to as "low speed".
[0004] The main problem is that, due to the limitations of the
sensors used, the speed cannot be well estimated below said speed
threshold.
[0005] Consequently, the control laws used cannot robustly control
the different low speed systems; such as, for example: [0006] The
parking systems (known by the acronym HFPB, for Hands Free Parking
Brake, and also known as auto-park) [0007] The ACC (distance
regulator) systems for "Stop&Start" situations [0008] The
driverless car or TJP (Traffic Jam Pilot) system in traffic jam
situations.
[0009] A second problem is the use of the acceleration value from
the accelerometer. This value is not very accurate (it is subject
to offsets) because of: [0010] The position of the accelerometer
after factory installation [0011] Some external quantities such as
the slope and the camber of the road [0012] The roll and the pitch
of the body.
[0013] FIG. 1 represents a graph illustrating the problem
encountered. The currently estimated speed of the vehicle is
represented by the curve 1, the accelerometer value is represented
by the curve 2, the curve 3 represents the "peaks" of the coder
wheels (that is to say the peaks of signals that they send on the
passage of a tooth, such a wheel also being called "toothed wheel")
and, between the lines A and B, the low speed zone where the
vehicle is running below a threshold of 1 km/h. The peaks indicate
whether or not the wheels have turned and give an image of the
speed by their amplitude.
[0014] In the zone between the lines A and B, the speed is unknown.
For example, in the right hand part of the low speed zone, it can
be seen that the wheels are turning (presence of peaks from the
coder wheels) but no speed is detected below the threshold of 1
km/h.
[0015] Finally, the plot from the accelerometer (curve 2) shows an
offset in the low speed zone (zone without the presence of peaks
and an accelerometer constant at non-zero value).
[0016] Thus, it becomes necessary to develop a strategy for
estimating the speed and the acceleration in the low speed zone
(between A and B), complementing the speed value already present on
the car.
[0017] One example of such a strategy is known from the document
"Improving the Response of a Wheel Speed Sensor by Using a RLS
Lattice Algorithm" by W. Hernandez, published in Sensors in June
2006, pages 64-79. This document more particularly discloses the
use of adaptive filters to resolve the problem of inaccuracy at low
speed and notably of the Kalman filters.
[0018] The main advantage of this type of software solution based
on adaptive filtering lies in its low cost.
[0019] However, a greater problem remains beyond the estimation of
the speed, that is the discontinuity of the speed and acceleration
values estimated upon a transition from the high speed range,
situated above the threshold, to the low speed range situated below
the threshold.
[0020] The aim of the present invention is notably to resolve this
technical problem by proposing a method that makes it possible to
estimate the speed and/or the acceleration of a vehicle at low
speed while being suited to the accurate measurement of speed of
the vehicle at medium and high speeds, without presenting any
discontinuity of these values.
DESCRIPTION OF THE INVENTION
[0021] To this end, the subject of the invention is a method for
estimating the speed of a motor vehicle wherein: [0022] A first
speed threshold SV1 is defined that corresponds to a minimum speed
value supplied by a vehicle wheel angular speed sensor; [0023] A
second speed threshold SV2 is defined that is greater than SV1;
[0024] Low speed values when the vehicle is running below SV1 are
estimated by using an estimation method of adaptive filter type;
[0025] High speed values when the vehicle is running above SV2 are
measured by using vehicle speed values supplied by the wheel
angular speed sensors; [0026] In the intermediate zone between SV1
and SV2, there is a mixing of high speed with low speed.
[0027] According to the invention, three speed ranges are used: low
speed, high speed and an intermediate mixing zone. The use of a
mixing range makes it possible to avoid discontinuity on both speed
and the acceleration (essential for guaranteeing the stability of
the control laws).
[0028] Advantageously, the adaptive filter is a Kalman filter.
[0029] Advantageously, in the intermediate zone between SV1 and
SV2, the mixing is done periodically at successive instants by
using a linear mixing method according to the formula:
Speed = Speed .times. .times. low kalman .times. SV .times. .times.
2 - Speed t - 1 SV .times. .times. 2 - SV .times. .times. 1 + Speed
.times. .times. high vehicle .times. Speed t - 1 - SV .times.
.times. 1 SV .times. .times. 2 - SV .times. .times. 1
##EQU00001##
[0030] This linear mixing makes it possible to calculate the mixed
speed (speed) by using the speed values from the Kalman method
(Speed.sub.kalman.sup.Low) and the vehicle speed
(speed.sub.vehicle.sup.high)
[0031] The vehicle speed (Speed.sub.vehicle.sup.high) is the speed
measured using the angular speed of the wheels.
[0032] The speed is the mixed speed at the current instant t,
speed.sub.t-1 is the mixed speed at the preceding mixing instant
t-1, (Speed.sub.kalman.sup.Low) is the speed value calculated by
the Kalman method at the current instant t and
(Speed.sub.vehicle.sup.high) is the speed value measured by the
angular sensor at the current instant t.
[0033] According to a feature of the invention, the first threshold
SV1 can be 1 km/h.
[0034] According to another feature of the invention, the second
speed threshold SV2 can be 1.5 km/h.
[0035] Advantageously, in the step C), the value of the
acceleration is also estimated using the Kalman filter and, in the
step E), there is also a mixing of the acceleration values between
SV1 and SV2.
[0036] One advantage of the invention is that the speed is
estimated without discontinuity and that the associated
acceleration value can also be taken into account.
BRIEF DESCRIPTION OF THE FIGURES
[0037] The invention will be better understood on reading the
following description of an exemplary embodiment given as an
illustrative example, the description referring to the attached
drawings in which:
[0038] FIG. 1 represents a graph illustrating the problems
encountered at low speeds;
[0039] FIG. 2 schematically illustrates the principle of the
invention;
[0040] FIGS. 3 and 4 represent examples of results of the use of
the method of the invention in vehicle starting and stopping
phase.
DETAILED DESCRIPTION
[0041] FIG. 2 schematically represents the general principle of the
invention in which 3 speed zones are defined: [0042] a low speed
zone, below a first threshold SV1 below which the values of the
speed and of the acceleration are not available. [0043] Typically 1
km/h. [0044] In this zone, the speed is measured according to the
Kalman method. This method is known per se to the person skilled in
the art, but it is recalled below for greater clarity of the
explanation of the invention. [0045] A high speed zone above a
second threshold SV2 greater than the first threshold SV1, for
example 1.5 km/h. In this zone, the values of the speed and of the
acceleration are supplied by the vehicle sensors; and [0046] A
mixing zone situated between the two thresholds SV1 and SV2.
[0047] I. Estimation of the Speed with the Kalman Method
[0048] I.1 Conventional Kalman Filter
[0049] A Kalman filter takes into account three state variables
[x]: [0050] x(1) distance travelled from the first instant t;
[0051] x(2) speed information; [0052] x(3) last acceleration.
[0053] The two sensor measurements [z] used for the estimation of
the state variable are: [0054] z(1) the average of the peaks of the
coder wheels (WT). The signals of the peaks of the 4 wheels are
already present in the vehicle messaging system (CAN). This
information makes it possible to have an idea of the displacement
of each wheel by counting, at each sampling interval, how many
teeth of the coder have passed (typically 48 teeth). [0055] z(2)
the angular speeds of the wheels (WS). The signals of the angular
speeds of the four wheels are already present in the vehicle
messaging system (CAN). The average of the rear wheels will be used
in the Kalman (axis of non-drive wheels, that is to say, less slip
in the start-up phases).
[0056] The Kalman filter equation system is: [0057] 1)
Prediction
[0057] {circumflex over (x)}.sub.k|k-1=F.sub.k{circumflex over
(x)}.sub.k-1|k-1+B.sub.ku.sub.k-1
P.sub.k|k-1=F.sub.kP.sub.k-1|k-1F.sub.k.sup.T+Q.sub.k [0058] 2)
Correction
[0058] {tilde over (y)}.sub.k=z.sub.k-H.sub.k{circumflex over
(x)}.sub.k|k-1
S.sub.k=H.sub.kP.sub.k|k-1H.sub.k.sup.T+R.sub.k
K.sub.k=P.sub.k|k-1H.sub.k.sup.TS.sub.k.sup.-1
{umlaut over (x)}.sub.k|k={umlaut over (x)}.sub.k|k-1+K.sub.k{tilde
over (y)}.sub.k
P.sub.k|k=(I-K.sub.kH.sub.k)P.sub.k|k-1
[0059] The notation used is as follows: [0060] x: state of the
system (vector) [0061] z: sensor measurements (vector) [0062] P:
estimated covariance matrix [0063] F.sub.k: state transition matrix
[0064] U.sub.k: command input [0065] B.sub.k: command transition
matrix [0066] H: measurement transition matrix [0067] Q: model
noise covariance matrix (accuracy) [0068] R: measurement noise
covariance matrix (accuracy) [0069] I: identity matrix [0070]
{circumflex over (x)}: estimated value of the variable x [0071]
{tilde over (x)}: measured value of the variable x
[0072] Note: In the Kalman filter fitted, the vector u is zero,
which simplifies the first equation.
[0073] I.2 Estimation of the Speed
[0074] At the input of the system, there are the two sensor data
which correspond to the wheel speeds (WS) and the peaks of the
coder wheels (WT). These data are processed (DP: "Data processing")
then passed into the Kalman filter ("Estimation" block) from which
emerge a speed and an acceleration.
[0075] First Step--"Data Processing": [0076] Wheel pulse: the coder
sends the position of the last tooth seen. We will use this
increment in the number of teeth [WT] during a sampling interval of
the system [Te] (interval necessarily at the same rate as the
recording to the sensor). Then, the average value between the four
wheels will be used as measurement of [WT]. The value equivalent to
a linear speed and using the peaks of the wheels is
[0076] 2 .times. .pi. .times. R nb_pic * WT ##EQU00002## [0077]
with [R] the radius of the wheels assumed constant [0078] and known
(setting parameter) and [nb_pic] the number of teeth of the coder.
[0079] WS: The average of the angular speeds of the rear wheels
(axis of non-drive wheels, that is to say, less slip in the
start-up phases) will be used in the Kalman filter. This angular
speed [WS] will be converted into linear speed on the basis of:
[0079] 2 .times. .pi. .times. R 60 * WS ##EQU00003##
[0080] Second step: "Estimation":
[0081] The Kalman model used is as follows:
[0082] State equation:
x k = ( d k .nu. k a k ) = ( d k - 1 + Te * v k - 1 + Te 2 2 * a k
- 1 v k - 1 + Te * a k - 1 a k - 1 ) ##EQU00004##
[0083] d.sub.k, v.sub.k and a.sub.k are, respectively, the distance
travelled, the speed and the acceleration on the iteration k of the
filter.
[0084] Input Data Vector
z k = ( 2 .times. .pi. .times. .times. R nb_pic * WT 2 .times. .pi.
.times. .times. R nb_pic * Wheel_Top + 2 .times. .pi. .times.
.times. R 60 * WS 1 Te * 2 .times. .pi. .times. .times. R 60 * WS )
##EQU00005##
[0085] nb_pic=96, the increment number of the coder.
[0086] Te=0.01 s, the sampling period.
[0087] The state equation represents the first line of the
prediction step shown previously. The hypothesis made here is a
constant changing of the acceleration.
[0088] The input vector (z) corresponds to the insertion of the
sensor data into the Kalman filter. The datum [WT] corresponds to
the sum of the peaks of the coder wheels divided by four (the
number of wheels). The variable [WS] itself is equal to the sum of
the speed of the rear wheels of the vehicle divided by 2.
[0089] Since this last datum is not always available (falls to 0
below SV1), an adaptation of the matrix H (see the Kalman equation
system, correction phase) in the Kalman filter has been made.
[0090] II. Mixing of Speeds
[0091] In the zones of the speeds situated between the first
threshold SV1 and the second threshold SV2, between 1 km/h and 1.5
km/h in the example represented in FIG. 2, there is a mixing of
high speed with low speed.
[0092] More particularly, the mixing was done using a linear mixing
method according to the formula:
Speed = Speed .times. .times. low kalman .times. SV .times. .times.
2 - Speed t - 1 SV .times. .times. 2 - SV .times. .times. 1 + Speed
.times. .times. high vehicle .times. Speed t - 1 - SV .times.
.times. 1 SV .times. .times. 2 - SV .times. .times. 1
##EQU00006##
[0093] This linear mixing makes it possible to calculate the value
of the mixed speed (speed), by using the speed values of the Kalman
method (Speed.sub.kalman.sup.Low) and the vehicle speed
(Speed.sub.vehicle.sup.high)
[0094] The vehicle speed (speed.sub.vehicle.sup.high) is the speed
calculated by using the angular speed of the wheels. At low speed,
the value of the high speed of the vehicle is not available.
[0095] In order to guarantee a correct mixing, the value of the
reference speed used is the last speed value. This value is used to
define the weight of each speed (weight defined between the
relative distance in relation to the thresholds). For example, the
weight of the speed of the Kalman method is defined as
( SV .times. .times. 2 - Speed t - 1 SV .times. .times. 2 - SV
.times. .times. 1 ) ##EQU00007##
TABLE-US-00001 t - 1 (last value) t = 0 (current value) Mixed speed
Speed.sub.t-1 Speed Estimated speed Not used Speed low (with the
Kalman Kalman method) Vehicle speed (using Not used Speed high the
angular speeds) Vehicle
[0096] The choice of reference speed makes it possible to guarantee
a continuity during the mixing.
[0097] The use of the speed estimated with the Kalman method is not
possible because the initial value can be greater than SV2 (because
of the delay of the filter). The use of the vehicle speed is also
not possible because it shows a discontinuity at low speeds where
the angular speed is no longer available.
[0098] III. Examples of Results Obtained
[0099] III.1--Start-Up Phase
[0100] FIG. 3 represents results obtained in the start-up phase of
the vehicle. The green curve corresponds to the current system
speed, the curve 3 shows WT, the curves 4 and 5 respectively show
the speed and the acceleration calculated according to the method
of the invention.
[0101] Regarding the speed, it can be seen that the Kalman filter
proposes an increasing speed 4 which meets the vehicle speed 1 used
currently. The speed 4 calculated by the method of the invention
takes off at the first detected wheel peak, that is to say, first
peak of the curve 3.
[0102] Concerning the acceleration 5, the same observation can be
made. The new estimation starts at the first peak detected and
converges fairly well towards a value which corresponds to that
expected for the speed 4.
[0103] The grey region corresponds to the transition region between
low and high speed. It can be seen that there is no discontinuity
and that the estimated speed value shows a coherent transition
relative to the real speed dynamics of the vehicle.
[0104] III.2 in Braking Phase to Stop
[0105] FIG. 4 represents results obtained in the stopping
phase.
[0106] Looking at the speed, it can be seen that the curve 4
follows a speed profile that is more fairly in agreement with the
coder wheel peaks than the curve 1. The stopping of the vehicle is
also detected more cleanly with the method of the invention.
[0107] The acceleration 5 seems to correspond to the speed 4
proposed and stops at the same time as the speed 4.
[0108] The grey region corresponds to the transition region between
high and low speed. It can be seen that there is no discontinuity
and that the speed value 4 estimated by mixing shows a coherent
transition relative to the Kalman speed dynamics.
* * * * *