TECHNICAL FIELD

This disclosure relates generally to control of heavy-duty vehicles such as trucks, busses and construction equipment. In particular aspects, the disclosure relates to computer-implemented methods and systems for road surface slope estimation. Although the disclosure may be described with respect to a particular vehicle, the disclosure is not restricted to any particular vehicle.

BACKGROUND

Heavy-duty vehicles such as trucks and busses need to be carefully controlled in order to ensure safe and efficient operation, at least in part due their large weight and size. Automated and semi-automated vehicle motion management (VMM) systems have been developed in order to assist a driver in operating the vehicle. An important part of such advanced driver assistance systems (ADAS) and autonomous drive systems (AD) is the motion estimation function which continuously estimates how the vehicle moves relative to the road surface, i.e., its speed, acceleration, yaw rate, and so on.

There is a continuing need for improved motion estimation functions for heavy-duty vehicles, both in terms of accuracy and reliability.

SUMMARY

The present disclosure relates at least in part to a computer-implemented method for determining road slope of a road surface that is supporting a heavy-duty vehicle. The method comprises transmitting, by a radar transceiver, a radar signal from the heavy-duty vehicle towards the road surface, at an angle relative to a normal of the road surface, and receiving, by the radar transceiver, backscatter of the radar signal from the road surface. The method also comprises determining, by a processor device of a computer system, a sequence of distances from the radar transceiver to the road surface based on the received backscatter of the radar signal, and also determining a difference sequence from the sequence of distances, as well as determining, by the processor device, the road slope based on an integration of the difference sequence. A change of sign may also be required, depending on the definition of slope direction.

Some aspects of the disclosure may seek to provide information to, e.g., a vehicle control system, related to a road slope of a road surface in vicinity of the vehicle. A technical benefit of this may comprise more accurate vehicle control and more accurate data regarding the geometry of the road surface in vicinity of the vehicle. A similar method may be used to only detect an upcoming or present change in road slope, and not estimate the actual slope value of the road surface.

The difference sequence may be determined as a time difference sequence of the sequence of distances, or a difference between the sequence of distances and a nominal distance from the radar transceiver to the road surface.

The method may also comprise obtaining, by the processor device, reference road slope data from a secondary information source, and calibrating the determined road slope based on the reference road slope data. The proposed method for determining road slope based on radar backscatter from the road surface may provide accurate information only over a limited time period due to drift, as will be explained in the following. The obtained road slope information may therefore benefit from periodic calibration based on a secondary source of information, which then does not have to be a low latency information source. The proposed radar-based method for road slope determination and one or more secondary (higher delay) road slope information sources may combine in an advantageous manner to provide accurate road slope information at low latency. The method may for instance comprise obtaining the reference data as data indicative of road slope from an inclinometer, and obtaining data indicative of an acceleration by the heavy-duty vehicle from an inertial measurement unit (IMU), a vehicle motion management (VMM) function and/or a motion support device (MSD) controller. The method then also comprises calibrating the determined road slope using the data indicative of road slope from the inclinometer in case the acceleration by the heavy-duty vehicle meets an acceptance criterion, such as being below a predetermined threshold value. It is known, e.g., from Einsteins equivalence principle, that inclination and change in velocity cannot be identified at the same time, since the two cannot be distinguished from each other. However, it is possible to use data from an inclinometer to calibrate the road slope data in a reliable manner if it is first ascertained that no significant vehicle acceleration is ongoing.

According to some aspects, the method comprises obtaining the reference data as data indicative of road slope from a map database and also obtaining data indicative of a geographic location of the heavy-duty vehicle together with an associated location accuracy from a navigation system of the heavy-duty vehicle. The method may then also comprise calibrating the determined road slope using the data indicative of road slope from the map database in case the location accuracy meets a location accuracy acceptance criterion. Map data may comprise accurate information regarding road slope in a given area, and can be used for calibrating the radar-based road slope estimator disclosed herein. However, the road slope data from the map will only be as accurate as the geographic position of the vehicle. If this position is erroneous then the road slope data may also be erroneous. It is appreciated that the relationship between road slope information accuracy in a map and geographic position also depends on the changes in road slope over a geographical area in which the vehicle is located. This road slope variation can also be taken into account when determining if the location accuracy meets an acceptance criterion. Indeed, some parts of the world are mostly flat, with no changes in road slope for miles, in which case a very crude location accuracy may still meet the acceptance criterion. Other parts of the world are more hilly with frequent and significant changes in road slope over smaller areas.

According to some aspects, the method comprises transmitting, by a first and a second radar transceiver, a first and a second radar signal from the heavy-duty vehicle towards the road surface, at respective angles relative to a normal of the road surface. The method also comprises receiving, by the radar transceiver, backscatter of the radar signals from the road surface, and determining, by the processor device, a first and a second sequence of distances from the radar transceivers to the road surface based on the received backscatter of the radar signal. By determining a plurality of distance sequences using two or more radar transceivers, preferably distanced from each other on the vehicle, more data is obtained which can be used to improve the accuracy of the slope estimation process. One radar transceiver can for instance be mounted at the front of the vehicle where it will see a road slope change before it happens, and one radar transceiver can be mounted at the rear of the vehicle to verify that the road slope change seen by the front radar transceiver is also corroborated by the rear radar transceiver.

Some aspects of the method also comprises determining, by the processor device, a low pass filtered or averaged difference sequence from the sequence of distances, in order to suppress noise and distortion. This may improve the accuracy of the road slope estimation process, and make the system more robust to noise and disturbances in the received radar signal.

According to some other aspects, the method also comprises determining, by the processor device, a level of surface evenness of the road surface based on a variation in the sequence of distances. This surface evenness metric may be useful in controlling the vehicle operation. For instance, the vehicle operational design domain (ODD) can be adjusted based on the detected surface evenness. In case the road surface is not even, some of the more aggressive vehicle maneuvers may not be permitted.

It is understood that the radar-based road slope monitoring system can also be used just to detect a change in road slope, i.e., not actually determine the road slope value. This can be performed by monitoring the difference sequence in order to see if there is a large and abrupt change in road slope. Thus, there is also disclosed herein computer-implemented method for determining road slope of a road surface that is supporting a heavy-duty vehicle. The method comprises transmitting, by a radar transceiver, a radar signal from the heavy-duty vehicle towards the road surface, at an angle relative to a normal of the road surface and receiving, by the radar transceiver, backscatter of the radar signal from the road surface. The method also comprises determining, by a processor device of a computer system, a sequence of distances from the radar transceiver to the road surface based on the received backscatter of the radar signal, and detecting, by the processor device, a change in road slope based on the difference sequence. The detection can, for instance, be based on a thresholding operation or the like configured to detect a change in the difference sequence, which is indicative of a change in slope (or an upcoming change in slope for a forward looking radar transceiver). This way the VMM system of the vehicle can prepare for the change in road slope before it happens, which is an advantage.

The above aspects, accompanying claims, and/or examples disclosed herein above and later below may be suitably combined with each other as would be apparent to anyone of ordinary skill in the art.

Additional features and advantages are disclosed in the following description, claims, and drawings, and in part will be readily apparent therefrom to those skilled in the art or recognized by practicing the disclosure as described herein. There are also disclosed herein control units, computer readable media, and computer program products associated with the above discussed technical benefits.

BRIEF DESCRIPTION OF THE DRAWINGS

With reference to the appended drawings, below follows a more detailed description of aspects of the disclosure cited as examples.

Figure 1 illustrates an example heavy-duty vehicle,

Figure 2 is a graph showing example tyre forces as function of wheel slip,

Figure 3 schematically illustrates aspects of an example vehicle control system,

Figure 4 shows an example slope estimation principle using a ground radar system;

Figure 5 schematically illustrates aspects of an example vehicle control system,

Figure 6 is a schematic diagram of an exemplary computer system,

Figure 7 is a flow chart illustrating methods, and

Figure 8 shows an example computer program product.

DETAILED DESCRIPTION

The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. The disclosure may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided for thoroughness and completeness. Like reference character refer to like elements throughout the description. Aspects set forth below represent the necessary information to enable those skilled in the art to practice the disclosure.

Figure 1 illustrates an example heavy-duty vehicle 100, here in the form of a truck. The vehicle comprises a plurality of wheels 102, wherein at least a subset of the wheels 102 comprises a respective motion support device (MSD) 104. Although the embodiment depicted in Figure 1 illustrates an MSD for each of the wheels 102, it should be readily understood that e.g., one pair of wheels 102 may be arranged without such an MSD 104. Also, an MSD may be arranged connected to more than one wheel, e.g., via a differential arrangement.

It is appreciated that the herein disclosed methods and control units can be applied with advantage also in other types of heavy-duty vehicles, such as trucks with drawbar connections, construction equipment, buses, and the like. The vehicle 100 may also comprise more than two vehicle units, i.e., a dolly vehicle unit may be used to tow more than one trailer.

The MSDs 104 may be arranged for generating a torque on a respective wheel of the vehicle or for both wheels of an axle. An MSD may be a propulsion device, such as an electric machine 106 or a combustion engine arranged to e.g., provide a longitudinal wheel force to the wheel(s) of the vehicle 100. The MSDs 104 may also comprise friction brakes such as disc brakes or drum brakes arranged to generate a braking torque by the wheel 102 in order to decelerate the vehicle.

Moreover, each of the MSDs 104 may be connected to a respective MSD control system or control unit 330 arranged for controlling operation of the MSD 104. The MSD control system 330 is preferably a decentralized motion support system 330, although centralized implementations are also possible. It is furthermore appreciated that some parts of the MSD control system may be implemented on processing circuitry remote from the vehicle, such as on a remote server 120 accessible from the vehicle via wireless link. Still further, each MSD control system 330 is connected to a VMM system or function 360 of the vehicle 100 via a data bus communication arrangement 114 that can be either wired, wireless or both wired and wireless. Hereby, control signals can be transmitted between the vehicle motion management system 360 and the MSD control system 330. The vehicle motion management system 360 and the MSD control system 330 will be described in further detail below with reference to Figure 3 and Figure 5.

Longitudinal wheel slip _x may, in accordance with SAE J370 (SAE Vehicle Dynamics Standards Committee January 24, 2008) be defined as where R is an effective wheel radius in meters, a> _x is the angular velocity of the wheel, and v _x is the longitudinal speed of the wheel (in the coordinate system of the wheel). Thus, X _x is bounded between -1 and 1 and quantifies how much the wheel is slipping with respect to the road surface. Wheel slip is, in essence, a speed difference measured between the wheel and the vehicle. Thus, the herein disclosed techniques can be adapted for use with any type of wheel slip definition. It is also appreciated that a wheel slip value is equivalent to a wheel speed value given a velocity of the wheel over the surface, in the coordinate system of the wheel. The VMM 360 and optionally also the MSD control system 330 maintains information on v _x in the reference frame of the wheel, while a wheel speed sensor or the like can be used to determine a> _x (the rotational velocity of the wheel).

Slip angle a, also known as sideslip angle, is the angle between the direction in which a wheel is pointing and the direction in which it is actually traveling (i.e., the angle between the longitudinal velocity component v _x and the vector sum of wheel forward velocity v _x and lateral velocity v _y . This slip angle results in a force, the cornering force, which is in the plane of the contact patch and perpendicular to the intersection of the contact patch and the midplane of the wheel. The cornering force increases approximately linearly for the first few degrees of slip angle, then increases non-linearly to a maximum before beginning to decrease.

The slip angle, a is often defined as

( v _y \ a = arctan ■ — r E / where v _y is the lateral speed of the wheel in the coordinate system of the wheel.

In order for a wheel (or tyre) to produce a wheel force which affects the motion state of the heavy-duty vehicle, such as an acceleration, slip must occur. For smaller slip values the relationship between slip and generated force is approximately linear, where the proportionality constant is often denoted as the slip stiffness C _x of the tyre. A tyre is subject to a longitudinal force F _x , a lateral force F _y , and a normal force F _z . The normal force F _z is key to determining some important vehicle properties. For instance, the normal force to a large extent determines the achievable longitudinal tyre force F _x by the wheel since, normally, F _x < F _z , where /J. is a friction coefficient associated with a road friction condition. The maximum available lateral force for a given wheel slip can be described by the so-called Magic Formula as described in “Tyre and vehicle dynamics”, Elsevier Ltd. 2012, ISBN 978-0-08-097016-5, by Hans Pacejka, where wheel slip and tyre force is also discussed in detail.

Figure 2 is a graph showing an example 200 of achievable tyre forces as function of longitudinal wheel slip. Fx is the longitudinal tyre force while Fy is the maximum obtainable lateral wheel force for a given wheel slip. This type of relationship between wheel slip and generated tyre force is often referred to as an inverse tyre model, and it is generally known. The examples in Figure 2 are for positive wheel forces, i.e., acceleration. Similar relationships exist between wheel slip and negative wheel force, i.e., braking.

An inverse tyre model can be used to translate between a desired longitudinal tyre force F _x and longitudinal wheel slip X _x . The interface between VMM and MSDs capable of delivering torque to the vehicle’s wheels has as mentioned above traditionally been focused on torquebased requests to each MSD from the VMM without any consideration towards wheel slip. However, this approach has some performance limitations. In case a safety critical or excessive slip situation arises, then a relevant safety function (traction control, anti-lock brakes, etc.) operated on a separate control unit normally steps in and requests a torque override in order to bring the slip back into control. The problem with this approach is that since the primary control of the actuator and the slip control of the actuator are allocated to different electronic control units (ECUs), the latencies involved in the communication between them significantly limits the slip control performance. Moreover, the related actuator and slip assumptions made in the two ECUs that are used to achieve the actual slip control can be inconsistent and this in turn can lead to sub-optimal performance. Significant benefits can be achieved by instead using a wheel speed or wheel slip-based request on the interface between VMM 360 and the MSD controller or controllers 330, thereby shifting the difficult actuator speed control loop to the MSD controllers, which generally operate with a much shorter sample time compared to that of the VMM system. Such an architecture can provide much better disturbance rejection compared to a torque-based control interface and thus improves the predictability of the forces generated at the tyre road contact patch.

A problem encountered when using wheel slip to actively control one or more wheels on a heavy-duty vehicle, such as the vehicle 100, and also when executing more low complex control such as imposing the above-mentioned wheel slip limit iim locally at wheel end, is that the speed over ground v _x of the wheel (and of the vehicle) may not be accurately known. For instance, if wheel speed sensors such as Hall effect sensors or rotational encoders are used to determine vehicle speed over ground, then the vehicle speed over ground will be erroneously determined in case the wheels used for estimating the speed over ground are themselves slipping. Also, vehicle speed over ground determined based on wheel rotation is onedimensional, i.e., the method does not allow determining a wheel lateral speed over ground v _y in addition to the longitudinal speed over ground v _x , i.e., a speed vector in two dimensions. This of course makes estimating the sideslip angle a challenging.

Satellite based positioning systems can be used to determine the speed over ground of a heavy- duty vehicle 100 and of any given wheel on the vehicle 100. However, these systems do not function well in some environments, such as environments without a clear view of the sky. Multipath propagation of the satellite radio signals can also induce large errors in the estimated vehicle position, which then translates into errors in the estimated vehicle speed over ground.

Vision-based sensor systems and radar systems can also be used to determine vehicle speed over ground. However, such systems are relatively costly and not always without issues when it comes to accuracy and reliability. Vision-based sensor may for instance suffer from performance degradation due to sun glare while radar sensor systems may be prone to interference from other radar transceivers. The present disclosure proposes the use of radar to determine both longitudinal and lateral velocity of a vehicle with respect to ground. With reference to Figure 1, a radar module 110 can be configured to determine a two-dimensional velocity vector [v _x , v _y ] of a heavy-duty vehicle 100 with respect to a ground plane or road surface 101 supporting the vehicle 100. This type of radar system comprises a radar transceiver arranged to transmit and to receive a radar signal 115 via an antenna array. The general principle of determining speed over ground using a ground radar was described in, e.g., US 2004/0138802 and will therefore not be discussed in more detail herein.

The radar transceiver illuminates a small portion of the road surface. Various illumination patterns can be selected, but it is an advantage if the illuminated portion of the ground surface by a given radar transceiver is close to a wheel and relatively small in size. It is appreciated that the radar system on the vehicle 100 will be able to determine vehicle speed relative to the road surface 101. However, the Doppler measurement principle will only give information about radial velocity of the road surface 101 relative to the radar transceiver. It will give the same output regardless of road surface slope. Thus, a vehicle 100 travelling uphill at a given velocity will not be distinguishable from a vehicle travelling downhill at the same velocity, or one that is travelling on a horizontal road surface. The effect of gravity is dependent on the road slope, and it is therefore desired to be able to also determine road slope, i.e., to determine vehicle speed and acceleration in a global coordinate system, relative to, e.g., the horizontal plane H.

The road slope (also called grade, incline, gradient, main-fall, pitch or rise) of a physical feature, landform or constructed line normally refers to the tangent of the angle of that surface to the horizontal plane. A larger number indicates higher or steeper degree of "tilt". Often slope is given as a ratio of "rise" to "run", or as a fraction ("rise over run") in which run is the horizontal distance (not the distance along the slope) and rise is the vertical distance.

Inclinometers, also called tilt sensors, clinometers or slope sensors, are designed to measure the angle of an object with respect to the force of gravity. These tilt or level meters determine the pitch and/or roll angle and output these values via the appropriate electrical interface. The sensors will, however, output inaccurate data if the vehicle is accelerating. Einstein’s equivalence principle gives that a change in velocity of the vehicle 100 cannot be distinguished from a change in gravity. To see this, consider a simple pendulum, which will move both in response to a change in inclination and to acceleration in the horizontal plane.

A ground speed radar system can be used to determine road slope by observing changes in distance to the road surface 101. Figure 4 illustrates the general principle of operation. A vehicle 100 is travelling on a road surface 101. The road surface 101 is first level, then has an upward slope, and is then level again. A ground speed radar sensor measured the distance d from the radar transceiver to the road surface 101. As the vehicle 100 approaches the start of the slope, this distance temporarily decreases, as illustrated in Figure 4. At the end of the slope another change is observed, this time with the opposite sign. Thus, by differentiating a sequence of recorded distances over the travelled distance, and then integrating or accumulating the differentiated sequence, an idea of the road slope can be obtained, as illustrated in Figure 4.

It is appreciated that noise and disturbance from the distance data sequence will propagate to the estimated road slope and cause errors which will accumulate overtime. This effect is simply due to the differential nature of the road slope estimation principle illustrated in Figure 4. To increase reliability of the method also over longer time periods, the estimated road slope can be calibrated using one or more secondary road slope information sources.

For instance, an IMU 420 can be used to determine when the vehicle is not accelerating in any direction, i.e., is travelling with constant velocity in constant direction, or is stationary. When this condition occurs, the output signal from an inclinometer 410 can be trusted to give an accurate representation of the road slope. Hence, the system can be set up to calibrate the road slope estimated using the ground radar during periods of vehicle stationarity (nonacceleration), based on the signal from an inclinometer of some sort. The inclinometer and the IMU may be the same device, or different devices. The MSD controller 330 can also be relied upon to make sure that no acceleration or retardation request has been sent to the MSDs of the vehicle 100, which is likely to have an effect on the acceleration of the vehicle 100.

By determining road slope in this manner the VMM function 360 and/or the TSM function 370 may obtain an early warning of a change in road slope. This means that the MSD control can be implemented as a preemptive control system in the sense that the change in road slope can be accounted for in the MSD control requests before the road slope change actually occurs, which is an advantage.

Most or at least some heavy-duty vehicles 100 are associated with an operational design domain (ODD), which can be a range of allowable vehicle states in a state space comprising, e.g., velocity, acceleration, yaw rate, curvature, and the like. A vehicle can, for instance, be associated with a maximum allowable velocity as function of yaw rate or curvature. Limitations on lateral and longitudinal wheel slip may also be part of the ODD of the heavy-duty vehicle 100. Detected (and/or upcoming) slope changes can be used to dynamically adjust the ODD of the vehicle 100. For instance, if the vehicle is travelling on a flat surface then a higher velocity and/or a higher wheel slip may be permitted compared to if the vehicle is travelling up or down a steep hill. Given the road slope information from the road slope estimation functions discussed herein, the VMM and/or the TSM functions of the heavy-duty vehicle may adjust the ODD of the vehicle 100 in a dynamic manner. For instance, by reducing the scope of the ODD as function of increasing magnitude road slope. This may, for instance, comprise reducing a maximum allowable velocity, acceleration, or wheel slip as function of road slope magnitude.

The ground speed radar system can also be used to determine a level of surface evenness of the road surface based on a variation in the sequence of distances, e.g., as a predetermined function of the variation in the sequence of distances obtained from the radar system. This surface evenness metric may also be useful in controlling the vehicle operation. Given the road evenness data from the road slope estimation function, the VMM and/or the TSM functions of the heavy-duty vehicle may adjust the ODD of the vehicle 100 in a dynamic manner. For instance, by reducing the scope of the ODD as function of decreasing road unevenness. This may, for instance, comprise reducing a maximum allowable velocity, acceleration, or wheel slip as function of road evenness.

Figure 3 schematically illustrates functionality 300 for controlling an example wheel 310 on the vehicle 100 by some example MSDs here comprising a friction brake 320 (such as a disc brake or a drum brake), a propulsion device 340 and a power steering arrangement 330. The friction brake 320 and the propulsion device are examples of wheel torque generating devices, which can be controlled by one or more motion support device control units 330. The control is based on, e.g., measurement data obtained from a wheel speed sensor 350 and from other vehicle state sensors, such as radar sensors, lidar sensors, and also vision-based sensors such as camera sensors and infra-red detectors. An MSD control system 330 may be arranged to control one or more actuators. For instance, it is common that an MSD control system 330 is arranged to control both wheels on an axle.

The TSM function 370 plans driving operation with a time horizon of 10 seconds or so. This time frame corresponds to, e.g., the time it takes for the vehicle 100 to negotiate a curve or the like. The vehicle maneuvers, planned and executed by the TSM function, can be associated with acceleration profiles and curvature profiles which describe a desired target vehicle velocity in the vehicle forward direction and turning to be maintained for a given maneuver. The TSM function continuously requests the desired acceleration profiles a _re q and steering angles (or curvature profiles c _re q) from the VMM system 360 which performs force allocation to meet the requests from the TSM function in a safe and robust manner. The VMM system 360 operates on a timescale of below one second or so and will be discussed in more detail below. An important input to the system is the road slope information 395 which is obtained from the ground speed radar sensor-based slope estimation function 390. This road slope information 395 allows, e.g., the VMM function 360 to predict impact of various torque actuators, since it indicates how gravity will affect the behavior of the vehicle 100, as illustrated in the insert 301 of Figure 3.

The wheel 310 has a longitudinal velocity component v _x and a lateral velocity component v _y (in the coordinate system of the wheel or in the coordinate system of the vehicle, depending on implementation). There is a longitudinal wheel force F _x and a lateral wheel force F _y , and also a normal force F _z acting on the wheel (not shown in Figure 3). Unless explicitly stated otherwise, the wheel forces are defined in the coordinate system of the wheel, i.e., the longitudinal force is directed in the rolling plane of the wheel, while the lateral wheel force is directed normal to the rolling plane of the wheel. The wheel has a rotational velocity and a radius R. A vehicle speed sensor 380 based on the herein disclosed radar systems is used to determine vehicle speed over ground, which can then be translated into wheel speed components v _x and/or v _y , in the coordinate system of the wheel. This means that the wheel steering angle d is taken into account if the wheel is a steered wheel, while a non-steered wheel has a longitudinal velocity component which is the same as the vehicle unit to which the wheel is attached. It is noted that the radar-based vehicle speed sensor 380 only provides information about the motion of the vehicle relative to the road surface. However, when complemented by the slope data 395 from the slope estimation function 390 of the present disclosure, the vehicle motion in a global coordinate system, e.g., relative to horizontal plane, can be determined, which is an advantage.

The type of inverse tyre models exemplified by the graph 200 in Figure 2 can be used by the VMM 360 to generate a desired tyre force at some wheel. Instead of requesting a torque corresponding to the desired tyre force, the VMM can translate the desired tyre force into an equivalent wheel slip (or, equivalently, a wheel speed relative to a speed over ground) and request this slip instead. The main advantage being that the MSD control device 330 will be able to deliver the requested torque with much higher bandwidth by maintaining operation at the desired wheel slip, using the vehicle speed v _x from the vehicle speed sensor 380 and the wheel rotational velocity a> _x , obtained from the wheel speed sensor 350.

The control unit or units can be arranged to store one or more pre-determined inverse tyre models in memory, e.g., as look-up tables or parameterized functions. An inverse tyre model can also be arranged to be stored in the memory as a function of the current operating condition of the wheel 310.

Figure 5 illustrates an example vehicle control function architecture applicable with the herein disclosed methods, where the TSM function 370 generates vehicle motion requests 375, which may comprise a desired steering angle 5 or an equivalent curvature c _re q to be followed by the vehicle, and which may also comprise desired vehicle unit accelerations a _re q and also other types of vehicle motion requests, which together describe a desired motion by the vehicle along a desired path at a desired velocity profile. It is understood that the motion requests can be used as base for determining or predicting a required amount of longitudinal and lateral forces which needs to be generated in order to successfully complete a maneuver. The VMM system 360 operates with a time horizon of about 1 second or so, and continuously transforms the acceleration profiles a _re q and curvature profiles c _re q from the TSM function into control commands for controlling vehicle motion functions, actuated by the different MSDs of the vehicle 100 which report back capabilities to the VMM, which in turn are used as constraints in the vehicle control. The VMM system 360 performs vehicle state or motion estimation 510, i.e., the VMM system 360 continuously determines a vehicle state s comprising positions, speeds, accelerations, and articulation angles of the different units in the vehicle combination by monitoring operations using various sensors 550 arranged on the vehicle 100, often but not always in connection to the MSDs. The motion estimation function 510 optionally comprises motion prediction, i.e., the inference of future motion by the vehicle given its current state. An important input to the motion estimation 510 may of course be the signals from the vehicle speed sensor 380 and the wheel speed sensors 350 on the heavy-duty vehicle 100, where the vehicle speed sensor 380 comprises a radar-based system as discussed herein. The output 395 of the slope estimation function 390 discussed above is important for both motion estimation 510 (including motion prediction), force generation 520, and MSD coordination 530.

The result of the motion estimation 510, i.e., the estimated vehicle state s, is input to a force generation module 520 which determines the required global forces V=[Vi, V2] for the different vehicle units to cause the vehicle 100 to move according to the requested acceleration and curvature profiles a _re q, c _re q, and to behave according to the desired vehicle behavior. The required global force vector V is input to an MSD coordination function 530 which allocates wheel forces and coordinates other MSDs such as steering and suspension. The MSD coordination function outputs an MSD control allocation for the i:th wheel, which may comprise any of a torque Ti, a longitudinal wheel slip Xi, a wheel rotational speed coi, and/or a wheel steering angle 5i. The coordinated MSDs then together provide the desired lateral Fy and longitudinal Fx forces on the vehicle units, as well as the required moments Mz, to obtain the desired motion by the vehicle combination 100.

The MSD control units may obtain wheel speed from one or more wheel speed sensors 350, and also a reliable vehicle speed over ground 380 from the radar modules discussed herein. Thus, according to some aspects of the present disclosure, the VMM system 360 manages both force generation and MSD coordination, i.e., it determines what forces that are required at the vehicle units in order to fulfil the requests from the TSM function 370, for instance to accelerate the vehicle according to a requested acceleration profile requested by TSM and/or to generate a certain curvature motion by the vehicle also requested by TSM. The forces may comprise e.g., yaw moments Mz, longitudinal forces Fx and lateral forces Fy, as well as different types of torques to be applied at different wheels. The forces are determined such as to generate the vehicle behavior which is expected by the TSM function in response to the control inputs generated by the TSM function 370.

Figure 7 shows a flow chart that illustrates a method which summarizes at least some of the discussion above. The method may be executed on one or more processing circuits, control units, or computer systems of the vehicle 100 and/or the remote server 120. Figure 7 illustrates a computer-implemented method for determining the road slope s of a road surface 101 that is supporting a heavy-duty vehicle 100. The method comprises transmitting SI, by a radar transceiver 110, a radar signal 115 from the heavy-duty vehicle 100 towards the road surface 101, at an angle a relative to a normal n of the road surface 101 and receiving S2, by the radar transceiver 110, backscatter of the radar signal 115 from the road surface 101. The radar system may comprise one or more radar transceivers, as illustrated in Figure 1. The radar signal 115 can be formatted in various ways, but a frequency modulated continuous wave (FMCW) signal may be preferred, since this signal is suitable for estimating both radial speed and radial distance with respect to the road surface 101. A pulsed radar or continuous wave signal format can also be used, as well as an orthogonal frequency division multiplexed (OFDM) signal format.

The method also comprises determining S3, by a processor device of a computer system such as will be discussed in more detail below in connection to Figure 6, a sequence of distances d from the radar transceiver 110 to the road surface 101 based on the received backscatter of the radar signal 115. The distances may comprise distances measured from the front of the vehicle as illustrated in Figure 4, but also distances measured from the back of the vehicle. Radar transceivers can also be arranged between the forward and rear parts of the heavy-duty vehicle 100. The sequence of distances d may be a vector of distance values sampled at some frequency, such as 10-100 Hz or the like. Higher distance sampling frequencies are also possible, as well as a continuous polynomial function that is fitted, e.g., using a least-squares fit, to measurements of distance. This sequence of distances contains information regarding changes in road slope since the distance from radar transceiver to ground changes as road slope changes. This concept is illustrated in Figure 4.

The method comprises determining S4, by the processor device, a difference sequence Ad from the sequence of distances d. This difference sequence can be determined in various ways.

A straight-forward way to determine the difference sequence Ad is to simply differentiate the sequence of distances with respect to time. This means that an element of the difference sequence Ad is determined as the difference of two consecutive elements of the sequence of distances d. In other words, if d[i] and d[i+l] are consecutive elements of the sequence of distances, then an element i of the difference sequence can be determined as Ad[i]= d[i+ 1 ]-d[i] , while Ad[i+1]= d[i+2]-d[i+l], and so on. Various forms of difference filters can of course also be applied to the sequence of distances in order to determine the difference sequence, which essentially is the time derivative of the sequence of distances.

Another way to determine the difference sequence is to simply take the difference between each distance value in the sequence of distances and a nominal distance corresponding to a distance from the radar transceiver to the road surface, i.e., the i-th element of the difference sequence is then determined as Ad[i]= d[i]-dO, where dO is the nominal distance from the radar transceiver to ground. This nominal distance can be determined by calibration as the vehicle is driving on a horizontal surface, or be configured manually. The nominal distance can also be determined as a long time average of the elements in the sequence of distances. A vehicle with a forward looking radar approaching an increase in slope will determine a difference sequence which is negative for a given time period (since the distance to ground temporarily decreases when approaching the upwards slope as illustrated in Figure 4). A vehicle approaching a decrease in slope (and having the forward looking radar) will determine a difference sequence which is positive for a given time period (since the distance to ground then temporarily increases as illustrated in Figure 4). A rearward looking radar will have the same behavior. The method also comprises determining S5, by the processor device, the road slope s based on an integration or accumulation of the difference sequence Ad. This integration or accumulation can also be performed in many different ways. A change of sign may also be required, depending on the definition of slope direction. One straight forward way to estimate the road slope s based on the difference sequence Ad is to simply calculate the accumulated sum of the difference sequence Ad, as illustrated in Figure 4. However, other types of integration filters can also be applied to estimate the road slope from the difference sequence.

The method may of course also comprise detecting S9, by the processor device, a change in road slope based on the difference sequence Ad. This detection can be performed based on a straight forward thresholding operation, or based on a shorter time window moving average determined over the difference sequence.

The method optionally comprises determining S41, by the processor device, a low pass filtered or averaged difference sequence Ad from the sequence of distances d. This may reduce in a suppressing of noise and other disturbances in the difference sequence Ad, which is an advantage.

It is appreciated that this method of estimating road slope is not absolute in the sense that the road slope is obtained relative to the horizontal plane. It is a differential approach which determines changes in road slope as they occur. This has the associated drawback of accumulating error over time. Hence, the accuracy of the determined road slope deteriorates over time. According to some aspects of the method, the estimated road slope s is periodically calibrated using one or more secondary information sources, i.e., the method may comprise obtaining S6, by the processor device, reference road slope data from a secondary information source, and calibrating S7 the determined road slope s based on the reference road slope data. This means that the road slope estimated by the herein proposed method is corrected for accumulated error regularly, and therefore becomes more dependable over longer time periods.

The method may for instance comprise obtaining S61 the reference data as data indicative of road slope s from an inclinometer 410, obtaining S62 data indicative of an acceleration by the heavy-duty vehicle 100 from an inertial measurement unit, IMU, 420, a vehicle motion management, VMM, function 360 and/or a motion support device, MSD, controller 330, and calibrating S63 the determined road slope s using the data indicative of road slope s from the inclinometer 410, in case the acceleration by the heavy-duty vehicle 100 meets an acceptance criterion. The acceleration by the heavy-duty vehicle 100 can for instance be compared to a threshold or acceptable range of accelerations. Requirements on wheel slip can also be imposed, such that the calibration is performed only if the wheel slip is sufficiently low. Einstein’s equivalence principle gives that a change in velocity of the vehicle 100 cannot be distinguished from a change in gravity. To see this, consider a simple pendulum, which will move both in response to a change in inclination and to acceleration in the horizontal plane. However, by making sure that the vehicle is not accelerating, or is at least not accelerating significantly, the output data from an inclinometer can be relied upon to accurately represent road slope.

The method may also comprise obtaining S64 the reference data as data indicative of road slope s from a map database, obtaining S65 data indicative of a geographic location of the heavy- duty vehicle 100, and an associated location accuracy, from a navigation system of the heavy- duty vehicle 100, and calibrating S66 the determined road slope s using the data indicative of road slope s from the map database in case the location accuracy meets a location accuracy acceptance criterion. This method of calibration is similar to that discussed above, except that now a map database is used to extract road slope information, but the map database is only used for calibration in case the position of the vehicle on the map is known with sufficient accuracy. If the geographic location of the vehicle is not known with sufficient accuracy, or the road slope information of the map database cannot be relied upon, then the calibration is deferred. Whether to perform calibration or not can also be determined based on the terrain surrounding the heavy-duty vehicle 100. If there are dense changes in road slope in an area around the heavy-duty vehicle, then the calibration may not be advisable, while less dense changes in road slope means that map-based road slope calibration can be done with more confidence.

The method may also comprise transmitting SI 1, by a first and a second radar transceiver 110, a first and a second radar signal 115 from the heavy-duty vehicle 100 towards the road surface 101, at respective angles a relative to a normal n of the road surface 101, receiving S21, by the radar transceiver 110, backscatter of the radar signals 115 from the road surface 101, and determining S31, by the processor device, a first and a second sequence of distances d from the radar transceivers 110 to the road surface 101 based on the received backscatter of the radar signal 115. This essentially means that multiple radar transceivers are used to determine road slope. It may be advantageous to mount one radar system at the front of the vehicle to detect upcoming changes in slope, and to mount another radar system at the back of the vehicle to corroborate a change in slope detected by the front radar system. More than two radar systems may also be used, as illustrated in Figure 1.

The wheelbase of the heavy-duty vehicle 100 will have an effect on the output of the distance data from the radar transceiver. Hence, according to some aspects the method comprises determining S51, by the processor device, the road slope s also based on a wheelbase of the heavy-duty vehicle 100. To understand this, note that a long wheelbase vehicle will see a change in measured distance for a longer period of time compared to a short wheelbase vehicle. The impact of wheelbase on road slope estimation can be determined from practical experimentation and/or from mathematical analysis.

The method may also comprise determining S8, by the processor device, a level of surface evenness of the road surface 101 based on a variation in the sequence of distances d. The distance from the radar transceiver to ground will of course vary more if there are large potholes and the like in the road surface. Thus, the output from the radar transceivers can be used to get an idea of the evenness of the road surface. The surface evenness can be determined from a measured sample variance of the sequence of distances.

There is also disclosed herein a computer system comprising a processor device and a radar transceiver arranged to determine a road slope s of a road surface 101 supporting a heavy-duty vehicle 100, where the processor device and radar transceiver are configured to transmit, by the radar transceiver 110, a radar signal 115 from the heavy-duty vehicle 100 towards the road surface 101, at an angle a relative to a normal n of the road surface 101, receive, by the radar transceiver 110, backscatter of the radar signal 115 from the road surface 101, determine, by the processor device, a sequence of distances d from the radar transceiver 110 to the road surface 101 based on the received backscatter of the radar signal 115, determine, by the processor device, a difference sequence Ad from the sequence of distances d, and determine, by the processor device, the road slope s based on an integration of the difference sequence Ad. A change of sign may also be required, depending on the definition of slope direction.

Figure 6 is a schematic diagram of a computer system 600 for implementing examples disclosed herein. The computer system 600 is adapted to execute instructions from a computer- readable medium to perform these and/or any of the functions or processing described herein. The computer system 600 may be connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. While only a single device is illustrated, the computer system 600 may include any collection of devices that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Accordingly, any reference in the disclosure and/or claims to a computer system, computing system, computer device, computing device, control system, control unit, electronic control unit (ECU), processor device, etc., includes reference to one or more such devices to individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. For example, control system may include a single control unit, or a plurality of control units connected or otherwise communicatively coupled to each other, such that any performed function may be distributed between the control units as desired. Further, such devices may communicate with each other or other devices by various system architectures, such as directly or via a Controller Area Network (CAN) bus, etc.

The computer system 600 may comprise at least one computing device or electronic device capable of including firmware, hardware, and/or executing software instructions to implement the functionality described herein. The computer system 600 may include a processor device 602 (may also be referred to as a control unit), a memory 604, and a system bus 606. The computer system 600 may include at least one computing device having the processor device 602. The system bus 606 provides an interface for system components including, but not limited to, the memory 604 and the processor device 602. The processor device 602 may include any number of hardware components for conducting data or signal processing or for executing computer code stored in memory 604. The processor device 602 (e.g., control unit) may, for example, include a general-purpose processor, an application specific processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a circuit containing processing components, a group of distributed processing components, a group of distributed computers configured for processing, or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processor device may further include computer executable code that controls operation of the programmable device.

The system bus 606 may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and/or a local bus using any of a variety of bus architectures. The memory 604 may be one or more devices for storing data and/or computer code for completing or facilitating methods described herein. The memory 604 may include database components, object code components, script components, or other types of information structure for supporting the various activities herein. Any distributed or local memory device may be utilized with the systems and methods of this description. The memory 604 may be communicably connected to the processor device 602 (e.g., via a circuit or any other wired, wireless, or network connection) and may include computer code for executing one or more processes described herein. The memory 604 may include non-volatile memory 608 (e.g., read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.), and volatile memory 610 (e.g., random-access memory (RAM)), or any other medium which can be used to carry or store desired program code in the form of machineexecutable instructions or data structures and which can be accessed by a computer or other machine with a processor device 602. A basic input/output system (BIOS) 612 may be stored in the non-volatile memory 608 and can include the basic routines that help to transfer information between elements within the computer system 600.

The computer system 600 may further include or be coupled to a non-transitory computer- readable storage medium such as the storage device 614, which may comprise, for example, an internal or external hard disk drive (HDD) (e.g., enhanced integrated drive electronics (EIDE) or serial advanced technology attachment (SATA)), HDD (e.g., EIDE or SATA) for storage, flash memory, or the like. The storage device 614 and other drives associated with computer- readable media and computer-usable media may provide non-volatile storage of data, data structures, computer-executable instructions, and the like. A number of modules can be implemented as software and/or hard coded in circuitry to implement the functionality described herein in whole or in part. The modules may be stored in the storage device 614 and/or in the volatile memory 610, which may include an operating system 616 and/or one or more program modules 618. All or a portion of the examples disclosed herein may be implemented as a computer program product 620 stored on a transitory or non-transitory computer-usable or computer-readable storage medium (e.g., single medium or multiple media), such as the storage device 614, which includes complex programming instructions (e.g., complex computer-readable program code) to cause the processor device 602 to carry out the steps described herein. Thus, the computer-readable program code can comprise software instructions for implementing the functionality of the examples described herein when executed by the processor device 602. The processor device 602 may serve as a controller or control system for the computer system 600 that is to implement the functionality described herein.

The computer system 600 also may include an input device interface 622 (e.g., input device interface and/or output device interface). The input device interface 622 may be configured to receive input and selections to be communicated to the computer system 600 when executing instructions, such as from a keyboard, mouse, touch-sensitive surface, etc. Such input devices may be connected to the processor device 602 through the input device interface 622 coupled to the system bus 606 but can be connected through other interfaces such as a parallel port, an Institute of Electrical and Electronic Engineers (IEEE) 1394 serial port, a Universal Serial Bus (USB) port, an IR interface, and the like. The computer system 600 may include an output device interface 624 configured to forward output, such as to a display, a video display unit (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system 600 may also include a communications interface 626 suitable for communicating with a network as appropriate or desired.

Figure 8 illustrates a computer readable medium 810 carrying a computer program comprising program code means 820 for performing the methods illustrated in Figure 7 and the techniques discussed herein, when said program product is run on a computer. The computer readable medium and the code means may together form a computer program product 800. The operational steps described in any of the exemplary aspects herein are described to provide examples and discussion. The steps may be performed by hardware components, may be embodied in machine-executable instructions to cause a processor to perform the steps, or may be performed by a combination of hardware and software. Although a specific order of method steps may be shown or described, the order of the steps may differ. In addition, two or more steps may be performed concurrently or with partial concurrence.

The terminology used herein is for the purpose of describing particular aspects only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be further understood that the terms "comprises," "comprising," "includes," and/or "including" when used herein specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It will be understood that, although the terms first, second, etc., may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the present disclosure.

Relative terms such as "below" or "above" or "upper" or "lower" or "horizontal" or "vertical" may be used herein to describe a relationship of one element to another element as illustrated in the Figures. It will be understood that these terms and those discussed above are intended to encompass different orientations of the device in addition to the orientation depicted in the Figures. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

It is to be understood that the present disclosure is not limited to the aspects described above and illustrated in the drawings; rather, the skilled person will recognize that many changes and modifications may be made within the scope of the present disclosure and appended claims. In the drawings and specification, there have been disclosed aspects for purposes of illustration only and not for purposes of limitation, the scope of the inventive concepts being set forth in the following claims.