WO2011054363A1 - Classification de surface - Google Patents

Classification de surface Download PDF

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Publication number
WO2011054363A1
WO2011054363A1 PCT/EP2009/007912 EP2009007912W WO2011054363A1 WO 2011054363 A1 WO2011054363 A1 WO 2011054363A1 EP 2009007912 W EP2009007912 W EP 2009007912W WO 2011054363 A1 WO2011054363 A1 WO 2011054363A1
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WO
WIPO (PCT)
Prior art keywords
correlation signal
wheel
correlation
sensor signals
determining
Prior art date
Application number
PCT/EP2009/007912
Other languages
English (en)
Inventor
Fredrik Gustafsson
Marius RÖLLAND
Rickard Karlsson
Andreas Hall
Original Assignee
Nira Dynamics Ab
Priority date (The priority date 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 date listed.)
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Publication date
Application filed by Nira Dynamics Ab filed Critical Nira Dynamics Ab
Priority to PCT/EP2009/007912 priority Critical patent/WO2011054363A1/fr
Priority to DE112009005342.4T priority patent/DE112009005342B4/de
Publication of WO2011054363A1 publication Critical patent/WO2011054363A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
    • B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
    • B60G17/016—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input
    • B60G17/0165—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input to an external condition, e.g. rough road surface, side wind
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
    • B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
    • B60G17/018—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by the use of a specific signal treatment or control method
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G99/00—Subject matter not provided for in other groups of this subclass
    • B60G99/004—Other suspension arrangements with rubber springs
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
    • B60T8/17—Using electrical or electronic regulation means to control braking
    • B60T8/172—Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters
    • 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
    • B60W40/072—Curvature of the road
    • 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
    • B60W40/076—Slope angle of the road
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P3/00—Measuring linear or angular speed; Measuring differences of linear or angular speeds
    • G01P3/64—Devices characterised by the determination of the time taken to traverse a fixed distance
    • G01P3/80—Devices characterised by the determination of the time taken to traverse a fixed distance using auto-correlation or cross-correlation detection means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
    • B60G2400/10—Acceleration; Deceleration
    • B60G2400/102—Acceleration; Deceleration vertical
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
    • B60G2400/20—Speed
    • B60G2400/208—Speed of wheel rotation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
    • B60G2400/80—Exterior conditions
    • B60G2400/82—Ground surface
    • B60G2400/821—Uneven, rough road sensing affecting vehicle body vibration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/18—Automatic control means
    • B60G2600/188—Spectral analysis; Transformations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60G—VEHICLE SUSPENSION ARRANGEMENTS
    • B60G2600/00—Indexing codes relating to particular elements, systems or processes used on suspension systems or suspension control systems
    • B60G2600/60—Signal noise suppression; Electronic filtering means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T2210/00—Detection or estimation of road or environment conditions; Detection or estimation of road shapes
    • B60T2210/10—Detection or estimation of road conditions
    • B60T2210/12—Friction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T2210/00—Detection or estimation of road or environment conditions; Detection or estimation of road shapes
    • B60T2210/10—Detection or estimation of road conditions
    • B60T2210/14—Rough roads, bad roads, gravel roads
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/28—Wheel speed
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/35—Road bumpiness, e.g. potholes

Definitions

  • the present invention relates generally to the estimation of the road surface condition under a vehicle and, for example, to systems, methods, and computer program products for estimating the road surface condition under a vehicle.
  • Modern cars comprise electronic control systems like anti-lock-braking systems (ABS), dynamic stability systems, anti-spin systems and traction control systems.
  • ABS anti-lock-braking systems
  • dynamic stability systems like dynamic stability systems
  • anti-spin systems like dynamic stability systems
  • traction control systems like driver safety information systems
  • driver safety information systems such as road friction indicators and sensor-free tire pressure monitoring systems which present information about the driving condition to the driver.
  • the present invention relates to techniques for estimating the road condition which make use of the vibration signals obtained from sensors, e.g. wheel speed sensors or acceleration sensors.
  • sensors e.g. wheel speed sensors or acceleration sensors.
  • using the signals from wheel speed sensors of ABS systems (and/or from the vehicle's internal CAN-bus) provides an economical way to road surface condition measurements since these ABS systems belong to the standard equipment of the majority of the cars and trucks sold today.
  • Such a system which is based on the signals of wheel speed sensors is for example disclosed in US-patent 5,566,090 which is directed to a method for detecting segments of bad road directly from the raw data provided by an ABS sensor.
  • the method uses the fact that segments of bad road result in strong fluctuations of the wheel speeds of the car. Strong wheel speed fluctuations in turn result in large differences between successive segment times, where the segment time is the time the wheel needs to pass through associated angle segments.
  • the disclosed method determines a segment of bad road if the difference between successive segment times is greater than a pre-set limit value.
  • This simple decision algorithm operates directly on the raw signals of the wheel speed sensor.
  • the US 4,837,727 discloses a method which is based on a similar decision algorithm.
  • EP 0 795 448 A2 discloses a road surface condition detection system which comprises a wheel speed sensor for detecting a wheel speed of at least one wheel to generate a wheel speed signal and a control unit which integrates the wheel speed signal for a predetermined period of time.
  • the control unit determines a rough road surface condition when the integrated signal is above a predetermined threshold value and, otherwise, a normal road surface condition.
  • the wheel speed signal is band-pass filtered in the frequency range of 10-15 Hz.
  • a method for estimating the ground condition under a driving vehicle having at least one pair of a first and a second wheel, the method comprising determining first and second sensor signals indicative of time dependent vibrations at the first and the second wheel, respectively; correlating the first and second sensor signals in order to determine a correlation signal of the first and second sensor signals; and determining the ground condition based on characteristics of the correlation signal.
  • a system for estimating the ground condition under a driving vehicle having at least one pair of a first and a second wheel, the system comprising: first and second sensors determining first and second sensor signals indicative of time dependent vibrations at the first and the second wheel, respectively; a correlation unit for correlating the first and second sensor signals in order to determine a correlation signal of the first and second sensor signals; an evaluation unit for determining the ground condition based on characteristics of the correlation signal.
  • a computer program product adapted for estimating the ground condition under a driving vehicle having at least one pair of a first and a second wheel, the computer program being arranged for: determining first and second sensor signals indicative of time dependent vibrations at the first and the second wheel, respectively; correlating the first and second sensor signals in order to determine a correlation signal of the first and second sensor signals; determining the ground condition based on characteristics of the correlation signal.
  • Fig. 1 shows a car driving on a road with a surface disturbance ("bump") and the time dependent rear and front wheel noise signals resulting when the car passes this surface disturbance;
  • Fig. 2 is a schematic view of an embodiment of a wheel speed sensor
  • Fig. 3 shows an exemplary diagram of four wheel speed signals obtained from the four wheels of a driving vehicle as a function of time
  • Fig. 4 shows the correlation function of the signals in Fig. 3;
  • Fig. 5a schematically displays the correlation signal in the correlation function for a rough road condition; the dashed line indicates the envelope of the i o correlation signal;
  • Fig. 5b schematically displays the correlation signal in the correlation function for a smooth road condition; the dashed line indicates the envelope of the correlation signal;
  • Fig. 5c schematically displays how fluctuations in the correlation signal can result i s in side maxima being stronger than the main maximum of the correlation signal; the dashed line indicates the envelope of an ideal correlation signal which is not influenced by fluctuations;
  • Fig. 6 schematically describes the Full Width Half Maximum (FWHM) technique which is used in an embodiment to determine the width of the correlation 20 peak;
  • FWHM Full Width Half Maximum
  • Fig. 7 shows the correlation function of the correlation between data from the front and the rear wheel in the event domain. At the delay of 67 cogs the maximum peak value is located;
  • Fig. 8 shows a plot of the delay which corresponds to the maximum of the 25 correlation signal for a large number of batches of measurement data.
  • Each circle represents the maximum correlation value (correlation peak) from a sample of the monitored sensor signal;
  • Fig. 9 shows an exemplary embodiment of a method to determine the ground condition under a driving vehicle;
  • Fig. 10 shows a further exemplary embodiment of a method to determine the ground condition under a driving vehicle.
  • the presented method and system for determining the road surface condition under a vehicle is based on a correlation analysis of the time dependent behavior of vibration signals, e.g. vibrations in wheel speed signals, of a vehicle's pair of wheels which vary with road bumpiness or unevenness.
  • the cause for such variations may be any small or large road feature like asphalt texture, splits, small stones, bumps, etc. These features induce these vibrations via the tire-road contact.
  • the variations which are induced at a particular wheel result in a specific time dependent behavior of the respective sensor signals. If wheel speed sensors are used to measure the vibrations, then the wheel speed signals are preferably measured in a pair of a front and a rear wheel which are running in a lane so that they feel the same road features in a time delayed manner.
  • the instantaneous wheel speed signals measured by these sensors are influenced by the road features which were instantaneously passed by an individual wheel.
  • Fig. 1 schematically shows a car with four wheels which is driving on a road with a surface disturbance ("bump").
  • the two graphs below the schematic representation of the car display the time dependent rear and front vibration signals resulting when the car passes the surface disturbance. It can be seen in the two graphs that the signal resulting from the surface disturbance appears in the rear wheel vibration signal with a time delay ⁇ compared to the front wheel vibration signal.
  • the vehicle may be any wheeled vehicle, like cars, lorries, trucks, motorcycles, trains, etc. which have a front and a rear wheel in contact with ground.
  • the two wheels which are running in a lane are referred to as belonging to a particular pair of wheels.
  • the front and the rear wheel are mounted on different axles but the axles are not required to be the first and the last axle of the vehicle.
  • the front-left and the rear-left wheel of a four wheeled car may constitute an appropriate wheel pair.
  • two arbitrary axles of the entire set of axles may be chosen as the two wheels in a lane which are here denoted as front and rear wheel.
  • the front and the rear wheel run in a lane so that during straight driving road features which are passed by the front wheel are subsequently passed by the rear wheel.
  • Wheel speed variations at the two wheels of such a pair of wheels are the basis for the velocity determination as presented herein. More then one of the above defined wheel pairs may be included in a velocity analysis to enhance the performance of the system, but, in the following embodiments, the principles of the velocity determination method are presented with one pair of wheels only.
  • the wheel pair must not necessarily be a pair of front and rear wheels. In other embodiments, a pair of a left wheel and a right wheel are used.
  • the two axles respectively the two wheels are spaced by a distance which is in the following denoted as wheel spacing.
  • wheel spacing ⁇ is also denoted as the wheel base.
  • the sensors used to obtain the front and real wheel noise signals may be of any type which is responsive to vibrations resulting from the contact of the vehicles' front and rear wheels with the road surface.
  • the sensors may be any common wheel speed sensors.
  • any other sensor type may also be used, e.g. accelerometers in a suspension system of a car, ultrasound sensors, microphones, laser sensors, axel height sensors, any other analog distance sensors, geophones which convert displacements into voltage, or e.g. in-tire pressure/accelerometer sensors.
  • Fig. 2 shows a schematic diagram of a wheel speed sensor comprising a toothed wheel with seven identical teeth.
  • a sensor is located at the circumference of the toothed wheel.
  • the sensor is arranged to generate a sensor signal whenever a tooth (cog) of the toothed wheel passes the sensor.
  • the sensor may be an optical sensor, a magnetic sensor (e.g. a HALL sensor) or any other conceivable type of sensor.
  • the sensor produces electrical signals which are transported by wires or radio transmission to a subsequent unit for further processing.
  • the sensor of the wheel speed sensor may internally generate a signal with two possible states, high and low (e.g., high indicating a covered sensor and low indicating an uncovered sensor), which in turn triggers the output of a clock signal delivered from a timer unit (not shown), and outputs a data stream.
  • the data stream comprises data samples in form of, for instance, a real or integer number t(n) which is representative of the time instance of the occurrence of a corresponding internal signal.
  • the sequence t(n) can be transformed from the event domain to the time domain by known means to obtain a wheel speed signal as function of time co(f).
  • the time intervals between two sensor signals depend on the rotational velocity of the observed wheel.
  • Such type of data which is for example generated by sampling the output from a wheel speed sensor is generally referred to as event domain sampled (here angle domain sampled; in some cases called "cog" domain).
  • the embodiment which is shown in Fig. 1 applies to a four-wheeled car where the front- left wheel is numbered 1 , the front-right 2, the rear-left 3 and the rear-right 4.
  • Examples of wheel speed signals sampled by the wheel speed sensors are shown in Fig. 3.
  • the plot shows measured wheel speeds in rad/s of the four wheels of a car as function of time. The data was recorded over a time interval of 60 seconds.
  • the wheel speeds are shown in the interval from 41.9 rad/s to 42.7 rad/s which illustrates that the fluctuations of the wheel speeds around their median are in the range of some percent.
  • the embodiment which is shown in Fig. 1 applies to a four-wheeled car where the front- left wheel is numbered 1 , the front-right 2, the rear-left 3 and the rear-right 4.
  • 40 rad/s corresponds roughly to 40 km/h which is about 11 m/s.
  • One curve contains an offset that is due to different tire radii, wheel slip or cornering. Both curves contain a barely visible disturbance that is delayed by 0.3 seconds.
  • the sensor signals contain a road induced disturbance which appears in the rear axle signals fi3 ⁇ 4ff) and ⁇ ) ⁇ seconds later than in the front axle signals o (t) and c ⁇ i)-
  • a correlation analysis of the front and the rear sensor signals shows a specific correlation signal, like one or more peaks of the correlation function, which is indicative of this time delay ⁇ between the front wheel and rear wheel speed signals.
  • the cross correlation between the front and rear wheel speeds is defined as
  • the plot of Fig. 4 shows the correlation function of the wheel speed signals of the front-left and the rear-left wheel and the correlation function /3 ⁇ 44(*) of the wheel speed signals of the front-right and the rear-right wheel of a car.
  • the abscissa of the plot represents the time delay ⁇ in the interval from -1 to 1 seconds.
  • the ordinate denotes the corresponding values of the cross correlation function Ri 3 (i).
  • Each correlation function shows a correlation signal which is indicative of the time delay between the front and rear wheel vibrations, e.g. as here a peak of maximal oscillation for a particular time delay ⁇ . These peaks indicate that disturbances in the wheel speed signals are most similar at these particular time delay values.
  • the plot of Fig. 4 also shows that the correlation signal develops an oscillatory characteristic with a main lobe and a corresponding main maximum (and a main minimum) in the center of the correlation signal and several side lobes with corresponding side maxima (and side minima) in the vicinity of the main maximum.
  • the main maximum and the main minimum are considered as belonging to the main lobe of the correlation signal and the side minima and side maxima are considered as belonging to side lobes of the correlation signal.
  • the invention is based on the observation that different road surfaces will excite the cross correlation differently and thus produce a correlation signal which can be used for estimating the road surface condition under a driving vehicle, i.e. for road surface classification.
  • the main correlation peak (or main lobe) of the correlation signal is weaker developed when a vehicle is driving on a smooth surface.
  • the side lobes of the correlation signal are weaker developed when a vehicle is driving on a rough surface. The rougher the road is, the less pronounced are the side lobes.
  • h (t) h(-t) denotes the time reversed impulse response and r? Z
  • h x h resembles a non-causal band-pass filter which is characterized by one main lobe and several smaller side lobes.
  • ⁇ de notes the Dirac delta function
  • B the wheel base
  • v the vehicle velocity.
  • the convolution with ⁇ - ⁇ ) reflects a shift of h * h (i.e. of the main lobe and side lobes) on the time axis.
  • h * h i.e. of the main lobe and side lobes
  • R z ,ij ⁇ the expected correlation result
  • R 2 j ⁇ r has a band-pass character. This results in that the cross-correlation of the sensor signal 3 ⁇ 4 ,/ , ⁇ r) is a smoothed version of h * h . The side lobes are less pronounced on rough roads.
  • sensor signals of a front and a rear wheel are correlated with each other.
  • the principles may also be applied, for example, by correlating sensor signals associated with left and right wheels.
  • the sensors of a left and a right wheel of the same vehicle axis are correlated with each other.
  • sensor signals of front and rear wheels are correlated with each other.
  • Figs. 5a and 5b show two correlation signals for two different ground conditions, namely Fig. 5a the correlation signal of a vehicle driving on a rough road surface and Fig. 5b the correlation signal of a vehicle driving on a smooth road surface.
  • the correlation signal In the rough road case of Fig. 5a, the correlation signal has a pronounced main lobe maximum and significantly smaller side lobe maxima.
  • the correlation signal In the smooth road case shown in Fig. 5b, the correlation signal has side lobe maxima which are nearly as pronounced as the maximum of the main lobe.
  • This difference can also be seen in the envelope curves of the correlation function which are shown as dashed lines in Figs. 5a and 5b.
  • the envelope curve in Fig. 5a raises and falls steeper than the envelope curve in Fig.
  • the correlation signal in Fig. 5b is broader in shape than the correlation signal in Fig. 5a. This shows that characteristics of the correlation signal, e.g. the shape of the correlation signal which reflect the development of the side lobes depend on the ground condition under a driving vehicle and can thus be used for road surface classification.
  • the height of the main maximum is related to the heights of the side maxima.
  • the ratio between the height of the main maximum and the heights of the side maxima is larger for rough road surfaces than for smooth road surfaces.
  • the envelope of the correlation function as displayed by the dashed lines in Figs. 5a and 5b is determined for each correlation signal by conventional means, e.g. by applying a corresponding filter to the correlation function.
  • the width of each correlation signal is determined by computing the width of each envelope.
  • the width of the envelope can for example be determined by the conventional Full Width Half Maximum technique (FWHM), which is schematically described in Fig. 6, or any other suitable means for quantizing the width of the envelope.
  • FWHM Full Width Half Maximum technique
  • the width of the correlation signal can then be used to deduce the ground condition under the driving vehicle.
  • the envelope of the correlation signal is computed as in the previous embodiment; but instead of the width of the correlation signal, the surface under the envelope function is used as characteristic for determining the ground condition under the driving vehicle.
  • the surface under the envelope of the correlation signal can for example be calculated by integration.
  • the skilled person can of course use other ways to quantize characteristics of the correlation signal to determine the ground condition under the driving vehicle, e.g. by applying filters of various types, e.g. Kalman filters or Least Mean Squares autoregressive filters.
  • filters of various types e.g. Kalman filters or Least Mean Squares autoregressive filters.
  • any shape factors as disclosed in published international application WO 2008/113384 might be used to quantize the characteristics of the correlation signal.
  • the front and rear wheel sensor signals are collected during specific time intervals.
  • the sensor signals collected in a time interval form a batch of measurement signals.
  • Such a batch of measurement signals may e.g. have a length of 60 seconds as shown in the example of Fig. 3.
  • the length of a batch might as well be smaller, e.g. 1 second, or larger than the 60 seconds displayed in Fig. 3.
  • an individual correlation function is calculated for each of the measurement batches and a specific feature of the correlation signal, e.g. the time delay f which corresponds to the maximum of the correlation function is determined for each batch.
  • the main lobe is strong and the side lobes are weak.
  • the determined maximum of the correlation function is the central peak of the correlation signal, i.e. the peak which is associated with the main lobe of the correlation signal as displayed in Fig. 5a.
  • the global maximum of the correlation function determined according to equation (4) is situated in a side lobe of the correlation signal, approximately 10% off from the expected value B/v.
  • the likelihood that the global maximum of the correlation signal is a side lobe maximum is larger when the side lobes are pronounced, i.e. under smooth road conditions. Under rough road conditions, the side lobes are less pronounced and the likelihood that the global maximum of the correlation function is a side lobe maximum is smaller.
  • Fig. 8 shows a plot of the correlation results of a large number of measurement batches. The plot was obtained from an analysis performed in the cog domain.
  • Each circle represents the maximum correlation value (correlation peak) of one batch of the monitored sensor signal, e.g. the correlation peak of a 60 seconds batch of the monitored sensor signal as shown in Fig. 3.
  • the correlation delay ⁇ in the cog domain which corresponds to the correlation delay in the time domain
  • the circles move from left to right on the x-axis.
  • Three different roads with different road coarseness are displayed in Fig. 8.
  • the expected side lobes are stronger so that the likelihood that a correlation peak (the global maximum of the correlation function of the batch data) will fall into one of the side lobes is larger.
  • the number of circles which are close to the expected value of the main lobe maximum tends to decrease, and the number of circles which are close to the expected side lobe maxima tends to increase.
  • the expected side lobes are weaker so that the likelihood that a determined correlation peak will fall into one of the side lobes is smaller.
  • the circles plotted in Fig; 8 show a band structure with a main band and side bands.
  • the main band reflects the main lobe of the correlation function and the side bands reflect the side lobes of the correlation function.
  • the more circles there are in the side bands the smoother the road is.
  • the more circles there are in the main band the rougher the road is.
  • the values 65, 59 and 71 are related to the cogs of a wheel speed sensor.
  • the statistical dispersion of the measured correlation delay e.g. the variance
  • Var ) E((i -E(i )) 2 ) (5) of the position of the correlation peak can be used as a measure for the ground condition under the driving vehicle.
  • the variance of the positions of the correlation peaks is small for road surface 1 (rough road) in which the main band is strongly filled with circles and the side bands are only weakly filled.
  • the variance of the positions of the correlation peaks gets larger for road surface 2 in which the occupation of the main band is diminished and the side bands get stronger occupied with circles.
  • the. variance of the positions of the correlation peaks gets largest for road surface 3 (smooth road) in which the occupation of the main band is even more diminished and the side bands get even stronger occupied by the circles.
  • Fig. 9 shows a flow-diagram which demonstrates the basic steps of an exemplary method to determine the ground condition under a driving vehicle as explained above, in steps 91 and 93, front and rear wheel speed signals are obtained from wheel speed sensors of the vehicle. In step 95, these wheel speed signals are correlated with each other, e.g. by a correlation function R13 according to equation (1). In step 97, the characteristics of the correlation function, respectively the characteristics of a correlation signal, e.g. of a correlation maximum are determined. This may be done on the principles as described above, e.g. by evaluating the strength of the side lobes, e.g. by comparing the strength of side lobes to the strength of the main lobe (main maximum). Finally, in step 99, the ground condition can be determined from these characteristics of the correlation signal. If for example, the shape of the correlation signal is evaluated, e.g. the breadth of the correlation signal, then the broader the correlation signal is, the smoother the road surface is.
  • each circle may be attributed to either the expected main band or to an expected side band of the correlation signal.
  • the number of circles falling in the side band of the correlation signal may then be compared with the number of circles falling in the main band of the correlation signal in order to obtain a measure for the road surface condition.
  • Fig. 10 This alternative is depicted in Fig. 10.
  • steps 101 and 103 a total number of N batches of wheel speed signals are accumulated over time.
  • step 105 for each batch, the correlation of the front and wheel speed signals is calculated.
  • the maximum of each correlation function is determined.
  • step 111 the total number N1 of batches whose maximum falls into the main lobe is counted.
  • step 113 the total number N2 of batches whose maximum falls into side lobes is counted.
  • step 113 the road condition is determined from the ratio of N1 and N2. The larger the ratio N1/N2 is, the rougher the road is.
  • a correlation analysis of a measurement batch as described above provides one or more output values which reflect e.g. the (current) strength of the side lobes of the correlation function.
  • this output value may for example be the variance of the correlation peak.
  • a real-time road classification can be based on these output values.
  • the variance may be approximated in real-time by a Least-Mean-Square filter:
  • ⁇ ⁇ is the raw correlation peak
  • ⁇ ⁇ is the tracked correlation peak
  • k is the current batch number
  • the step length ⁇ ⁇ is a filter parameter.
  • the classification of the road surface is updated based on the new, filtered value from the correlation analysis.
  • the variance may further be estimated with a combination of a Least-Mean- Square filter and a forgetting factor according to:
  • u is the forgetting factor of the filter.
  • a classification of the road condition based on the filtered output value may then be obtained by calibration, i.e. the output results are related to known road conditions in test drives.
  • a sequential analysis technique might be used to evaluate the output of the correlation analysis, e.g. a cumulative summation technique (CUSUM) may be used to detect changes in the output values.
  • CCSUM cumulative summation technique
  • Hysteresis function may be used to prevent fast fluctuation of the classification results in situation where the surface changes smoothly.
  • the embodiments of the computer program products with program code for performing the described methods include any machine-readable medium that is capable of storing or encoding the program code.
  • the term "machine-readable medium” shall accordingly be taken to include, but not to be limited to, solid state memories, optical and magnetic storage media, and carrier wave signals.
  • the program code may be machine code or another code which can be converted into machine code by compilation and/or interpretation, such as source code in a high- level programming language, such as C++, or in any other suitable imperative or functional programming language, or virtual-machine code.
  • the computer program product may comprise a data carrier provided with the program code or other means devised to control or direct a data processing apparatus to perform the method in accordance with the description.
  • a data processing apparatus running the method typically includes a central processing unit, data storage means and an l/O-interface for signals or parameter values.
  • a general purpose of the disclosed embodiments is to provide improved methods and products which enable to more accurately determine a ground condition by means of sensors located at first and second wheels of a vehicle.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
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Abstract

L'invention concerne un procédé d'estimation de la condition au niveau du sol sous un véhicule automobile comprenant au moins une paire de première et seconde roues, le procédé comprenant pour au moins une paire de roues : la détermination de premier et second signaux de capteur indiquant le temps en fonction des vibrations au niveau des roues avant et arrière, respectivement ; la corrélation des premier et second signaux de capteur afin de déterminer un signal de corrélation indiquant le décalage temporel entre les premier et second signaux de capteur ; la détermination de la condition au niveau du sol sur la base des caractéristiques du signal de corrélation.
PCT/EP2009/007912 2009-11-04 2009-11-04 Classification de surface WO2011054363A1 (fr)

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WO2018234152A1 (fr) 2017-06-19 2018-12-27 Nira Dynamics Ab Surveillance des roues dans un véhicule
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WO2021094073A1 (fr) 2019-11-15 2021-05-20 Pirelli Tyre S.P.A. Procédé et système permettant d'estimer un mouvement de carrosserie de véhicule pendant le déplacement d'un véhicule le long d'un segment de route
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WO2014004119A1 (fr) * 2012-06-27 2014-01-03 Bose Corporation Amortissement de roue actif
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US9259976B2 (en) 2013-08-12 2016-02-16 The Goodyear Tire & Rubber Company Torsional mode tire wear state estimation system and method
US8983749B1 (en) 2013-10-24 2015-03-17 The Goodyear Tire & Rubber Company Road friction estimation system and method
US9442045B2 (en) 2014-04-03 2016-09-13 The Goodyear Tire & Rubber Company Model-based longitudinal stiffness estimation system and method
US9751533B2 (en) 2014-04-03 2017-09-05 The Goodyear Tire & Rubber Company Road surface friction and surface type estimation system and method
WO2015188930A1 (fr) 2014-06-09 2015-12-17 Nira Dynamics Ab Détection d'irrégularités à court terme dans une surface de route
CN111504248A (zh) * 2014-06-09 2020-08-07 尼拉动力公司 路面上的短期不平度的检测
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DE102014008588B4 (de) 2014-06-10 2021-09-30 Nira Dynamics Ab Erfassung kurzzeitiger Unregelmässigkeiten in einer Strassenoberfläche
DE102014008588A1 (de) 2014-06-10 2015-12-17 Nira Dynamics Ab Erfassung kurzzeitiger Unregelmässigkeiten in einer Strassenoberfläche
WO2016012185A1 (fr) * 2014-07-25 2016-01-28 Siemens Aktiengesellschaft Procédé de surveillance de l'état de roulement d'un véhicule et véhicule comprenant un tel dispositif
FR3027666A1 (fr) * 2014-10-27 2016-04-29 Renault Sa Procede de detection de l'etat d'une route
US9963132B2 (en) 2014-11-10 2018-05-08 The Goodyear Tire & Rubber Company Tire sensor-based vehicle control system optimization and method
US10245906B2 (en) 2014-11-11 2019-04-02 The Goodyear Tire & Rubber Company Tire wear compensated load estimation system and method
FR3028827A1 (fr) * 2014-11-20 2016-05-27 Renault Sa Procede de detection de la perte de vigilance d'un conducteur de vehicule
US9739689B2 (en) 2014-11-21 2017-08-22 The Goodyear Tire & Rubber Company Tire cornering stiffness estimation system and method
US9650053B2 (en) 2014-12-03 2017-05-16 The Goodyear Tire & Rubber Company Slip ratio point optimization system and method for vehicle control
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US9340211B1 (en) 2014-12-03 2016-05-17 The Goodyear Tire & Rubber Company Intelligent tire-based road friction estimation system and method
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US11230292B2 (en) 2015-06-26 2022-01-25 Bayerische Motoren Werke Aktiengesellschaft Method, apparatus, computer program and computer program product for processing data of a route profile for a vehicle
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US11186285B2 (en) 2016-12-16 2021-11-30 Volkswagen Aktiengesellschaft Method for estimating a friction coefficient of a roadway by a transportation vehicle, control device, and transportation vehicle
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WO2019122218A1 (fr) 2017-12-20 2019-06-27 Nira Dynamics Ab Détermination d'un état de pression des pneus dans un véhicule
US11505015B2 (en) 2017-12-20 2022-11-22 Nira Dynamics Ab Determining a tire pressure status in a vehicle
US10766494B2 (en) 2018-02-16 2020-09-08 GM Global Technology Operations LLC Road surface characterization based upon filter coefficients
WO2020083569A1 (fr) * 2018-10-22 2020-04-30 Zf Friedrichshafen Ag Procédé de fonctionnement d'un actionneur d'un mécanisme de roulement actif et mécanisme de roulement actif
US11298991B2 (en) 2018-11-28 2022-04-12 The Goodyear Tire & Rubber Company Tire load estimation system and method
IT201900006614A1 (it) * 2019-05-07 2020-11-07 Bridgestone Europe Nv Sa Metodo e sistema per il riconoscimento delle irregolarita' di una pavimentazione stradale
WO2020225702A1 (fr) * 2019-05-07 2020-11-12 Bridgestone Europe Nv/Sa Procédé et système de reconnaissance des irrégularités d'une chaussée routière
AT522741A1 (de) * 2019-07-05 2021-01-15 Avl List Gmbh Verfahren zur beurteilung der fahrbarkeit von fahrzeugen
IT201900021270A1 (it) 2019-11-15 2021-05-15 Pirelli Metodo e sistema per stimare un parametro di disuniformitá di un segmento stradale
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