Patent ID: 12246700

DETAILED DESCRIPTION

The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses.

Vehicles operating in the real world are expected to perform under conditions that are changing and unpredictable. As an example, the road surface may include sections that are paved with one of several possible paving materials, and/or unpaved. Additionally, the road surface may be wet, dry, icy, and/or covered in loose debris such as sand, gravel, or snow. These various conditions may change the available tire traction, reducing the frictional forces generated by the tires and making it challenging to maintain vehicle control.

As depicted inFIG.1, a vehicle10generally includes a chassis12, a body14, front and rear wheels17. The body14is arranged on the chassis12and substantially encloses components of the vehicle10. The body14and the chassis12may jointly form a frame. The wheels17are each rotationally coupled to the chassis12near a respective corner of the body14. Each wheel17has a tire18mounted to its outer circumference.

The vehicle10is depicted in the illustrated embodiment as a passenger car, but it should be appreciated that another vehicle including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), marine vessels, aircraft, etc., can also be used.

As shown, the vehicle10may include a propulsion system20, a transmission system22, a steering system24, a brake system26, a sensor system28, an actuator system30, at least one data storage device32, at least one controller34, and a communication system36. The propulsion system20may, in various embodiments, include an electric machine such as a traction motor and/or a fuel cell propulsion system. The vehicle10further includes a battery (or battery pack)21electrically connected to the propulsion system20. Accordingly, the battery21is configured to store electrical energy and to provide electrical energy to the propulsion system20. Additionally, the propulsion system20may include an internal combustion engine. The transmission system22is configured to transmit power from the propulsion system20to the vehicle wheels17according to selectable speed ratios. According to various embodiments, the transmission system22may include a step-ratio automatic transmission, a continuously-variable transmission, or other appropriate transmission. The brake system26is configured to provide braking torque to the vehicle wheels17. The brake system26may, in various embodiments, include friction brakes, brake by wire, a regenerative braking system such as an electric machine, and/or other appropriate braking systems. The steering system24influences a position of the vehicle wheels17. While depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of the present disclosure, the steering system24may not include a steering wheel.

The sensor system28includes one or more sensors40(i.e., sensing devices) that sense observable conditions of the exterior environment and/or the interior environment of the vehicle10. The sensors40may include, but are not limited to, a longitudinal velocity sensor, a longitudinal acceleration sensor, a lateral acceleration sensor, a yaw rate sensor, a wheel angular velocity sensor, a wheel angular acceleration sensor, a normal force sensor, a steering wheel angle sensor, and/or other sensors. The actuator system30includes one or more actuator devices42that control one or more vehicle features such as, but not limited to, drive torque delivered to the wheels by the propulsion system20and by the transmission system22, the steering angle delivered by the steering system24, and the brake torque delivered by the brake system26. In various embodiments, the vehicle features can further include interior and/or exterior vehicle features such as, but are not limited to, doors, a trunk, and cabin features such as air, music, lighting, etc. (not numbered). Because the sensor system28provides object data to the controller34, the sensor system28and its sensors40are considered sources of information (or simply sources).

The data storage device32stores data for use in controlling the vehicle10. As can be appreciated, the data storage device32may be part of the controller34, separate from the controller34, or part of the controller34and part of a separate system.

The controller34includes at least one processor44and a computer non-transitory readable storage device or media46. The processor44can be a custom made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the controller34, a semiconductor-based microprocessor (in the form of a microchip or chip set), a macroprocessor, a combination thereof, or generally a device for executing instructions. The computer readable storage device or media46may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM), for example. KAM is a persistent or non-volatile memory that may be used to store various operating variables while the processor44is powered down. The computer-readable storage device or media46may be implemented using a number of known memory devices such as PROMs (programmable read-only memory), EPROMs (electrically PROM), EEPROMs (electrically erasable PROM), flash memory, or another electric, magnetic, optical, or combination memory devices capable of storing data, some of which represent executable instructions, used by the controller34in controlling the vehicle10.

The instructions may include one or more separate programs, each of which comprises an ordered listing of executable instructions for implementing logical functions. The instructions, when executed by the processor44, receive and process signals from the sensor system28, perform logic, calculations, methods and/or algorithms for automatically controlling the components of the vehicle10, and generate control signals to the actuator system30to automatically control the components of the vehicle10based on the logic, calculations, methods, and/or algorithms. Although a single controller34is shown inFIG.1, embodiments of the vehicle10may include a number of controllers34that communicate over a suitable communication medium or a combination of communication mediums and that cooperate to process the sensor signals, perform logic, calculations, methods, and/or algorithms, and generate control signals to automatically control features of the vehicle10.

In various embodiments, one or more instructions of the controller34are embodied in the control system98. The vehicle10includes a user interface23, which may be a touchscreen in the dashboard. The user interface23is in electronic communication with the controller34and is configured to receive inputs by a user (e.g., vehicle operator). Accordingly, the controller34is configured receive inputs from the user via the user interface23. The user interface23includes a display configured to display information to the user (e.g., vehicle operator or passenger).

The communication system36is configured to wirelessly communicate information to and from other entities48, such as but not limited to, other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems, and/or personal devices. In an exemplary embodiment, the communication system36is a wireless communication system configured to communicate via a wireless local area network (WLAN) using IEEE 802.11 standards or by using cellular data communication. However, additional or alternate communication methods, such as a dedicated short-range communications (DSRC) channel, are also considered within the scope of the present disclosure. DSRC channels refer to one-way or two-way short-range to medium-range wireless communication channels specifically designed for automotive use and a corresponding set of protocols and standards. Accordingly, the communication system36may include one or more antennas and/or transceivers for receiving and/or transmitting signals, such as cooperative sensing messages (CSMs).

FIG.1includes a schematic block diagram of the control system98, which is configured to control the vehicle10. The controller34of the control system98is in electronic communication with the braking system26, the propulsion system20, and the sensor system28. The braking system26includes one or more brake actuators (e.g., brake calipers) coupled to one or more wheels17. Upon actuation, the brake actuators apply braking pressure on one or more wheels17to decelerate the vehicle10. The propulsion system20includes one or more propulsion actuators for controlling the propulsion of the vehicle10. For example, as discussed above, the propulsion system20may include an internal combustion engine and, in that case, the propulsion actuator may be a throttle specially configured to control the airflow in the internal combustion engine. The sensor system28may include one or more accelerometers (or one or more gyroscopes) coupled to one or more wheels17. The accelerometer is in electronic communication with the controller34and is configured to measure and monitor the longitudinal and lateral accelerations of the vehicle10. The sensor system28may include one or more speed sensors configured to measure the speed (or velocity) of the vehicle10. The speed sensor is coupled to the controller34and is in electronic communication with one or more wheels17.

Referring toFIG.2, a vehicle wheel17on which is mounted an automobile tire18is illustrated, along with arrows representing force vectors that are present at the interface between the tire18and the road surface. The first arrow52represents the longitudinal force component Fx, which is the force acting in the direction of vehicle travel. The longitudinal force Fxis responsible for vehicle acceleration and braking. The second arrow54represents the lateral force component Fy, which is the force acting in the plane of the road surface perpendicular to the direction of vehicle travel. The lateral force Fyis responsible for vehicle cornering and yaw plane stability. The third arrow56represents the normal force Fz, which is the force acting in the direction normal to the road surface.

Referring toFIG.3, a graph60of normalized longitudinal tire force as a function of slip ratio for various road surfaces is presented. The y-axis62represents the normalized longitudinal tire force, commonly referred to as μ, which is defined as the longitudinal force Fxdivided by the normal force Fz, i.e.

μ=FxFz(Equation⁢1)
In the graph60, the x-axis64represents the slip ratio, which is defined as:

slip⁢ratio=ω·Reff-VV(Equation⁢2)where ω=angular velocity of the wheelReff=effective radius of the tireV=forward velocity of the vehicle

With continued reference toFIG.3, examples of the relationship between normalized longitudinal tire force μ and slip ratio are plotted for various road surfaces and conditions. InFIG.3, trace66represents a dry asphalt road, trace68represents wet asphalt, trace70represents cobblestone, trace72represents snow, and trace74represents ice. The maximum longitudinal force that can be generated at the tire-road interface, also known as the tractive limit, depends not only on the road surface but also on the slip ratio. The behavior of the μ-slip relationship can be generally divided into three regions. At low values of slip, μ increases in a generally linear fashion as slip increases. As slip continues to increase, the slope of the μ-slip relationship decreases to zero and a peak value of μ is reached. Further increases in slip result in a tire saturation region of decreasing μ with relatively flat slope.

The graph60depicts normalized longitudinal tire force FX for a tire rolling in the direction of vehicle travel in the absence of lateral force Fy. In the presence of lateral force Fy, for example if the vehicle is cornering or undergoing yaw motion, the capacity to generate longitudinal force can be estimated using a friction ellipse as:
Fx,cap=√{square root over ((μFz)2−Fy2)}  (Equation 3)

It is desirable to operate the tire near the peak of the μ-slip curve to prevent tire saturation while maximizing control performance. In practice, it is challenging to estimate forces and friction coefficient μ in real time using readily available vehicle sensor readings. Additionally, the longitudinal force capacity Fx,capvaries from road to road.

Referring toFIG.4, a flow chart of a machine learning-based tractive limit and wheel stability estimation algorithm100is presented. As used herein, the term “machine learning” refers to algorithms that build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. As a non-limiting example, a machine learning algorithm may be a regression algorithm where the output is continuous in nature and the inputs may be numeric or categorical. As another non-limiting example, a machine learning algorithm may be a classification algorithm which classifies a data point into one of a fixed number of classes. A hyperparameter is a parameter that is set before the learning process begins. These parameters are tunable and can directly affect how well a model trains.

With continued reference toFIG.4, in the input selection step102, relevant input features are selected based on wheel dynamics and tire behavior. Wheel dynamics are described by the equation:
Iw{dot over (ω)}=Tdr−FxReff(Equation 4)
whereIwis the wheel inertia;{dot over (ω)} is the wheel angular acceleration'Tdris the drive torque to the wheelFx, the longitudinal force, is a function of slip ratio, slip angle, μ, and FzReffis the effective tire radius.

The relevant input features may include longitudinal velocity, longitudinal acceleration, lateral acceleration, yaw rate, wheel angular velocity, wheel angular acceleration, normal force, steering wheel angle, and lateral distance between vehicle center of gravity and wheel center. Rather than using all of the input feature listed, it may be desirable to only use a subset of the listed features. It may also be desirable to use one or more input features not included in the foregoing list. Selection of input features is based on reaching a balance between algorithm performance and computational demands.

With continued reference toFIG.4, algorithm step104includes ground truth estimating and labeling. In the ground truth estimating and labeling step104, an experimental dataset is collected using an instrumented vehicle, covering various driving maneuvers and road surfaces. As a non-limiting example, data may be recorded with increasing speed while cornering on dry asphalt, straight-line acceleration on dry asphalt, straight-line acceleration on ceramic tiles, acceleration in a turn from rest on basalt, and straight-line acceleration on a split surface type. After collecting the data, in a labeling procedure the data is analyzed to identify wheel stability status as a function of time. The behavior of the wheel with time can be visualized as a trajectory on a longitudinal force vs. slip plot200such as is shown inFIG.5. The longitudinal force vs. slip plot200has a shape generally like the μ-slip relationship depicted inFIG.3, but the y-axis scale has units of force (e.g., Newtons), and ranges from zero to a maximum value somewhat less than the normal force Fzat the wheel. In a non-limiting example, wheel stability status may be correlated to three regions of the longitudinal force vs. slip relationship200. In a first region202, referred to herein as the linear region, the slope of the longitudinal force vs. slip relationship is positive. In a second region204, referred to herein as the near-peak region, the longitudinal force reaches a maximum value and the slope of the of the longitudinal force vs. slip relationship is zero. In a third region206, referred to herein as the saturated region, the slope of the of the longitudinal force vs. slip relationship is approximately zero or is negative. In the wheel stability status labeling procedure, it may be advantageous to omit data corresponding to certain vehicle operating conditions, for example vehicle speed less than a predetermined threshold, wheel drive torque less than a predetermined threshold, and/or braking.

The tractive limit, i.e., the maximum longitudinal force that can be generated at the tire-road interface, can be estimated for each region202,204,206. In the linear region202, a friction ellipse is used to estimate the maximum capacity of the longitudinal friction coefficient at a time t as:

μx,cap(t)=1Fz(t)⁢(μ⁢Fz(t))2-Fy(t)2(Equation⁢5)

In the near-peak region204, the maximum capacity of the longitudinal friction coefficient at a time t is estimated as:

μx,cap(t)=Fx(t)Fz(t)(Equation⁢6)

In the saturated region206, in a first method the value of μx,cap(t) that was calculated in the most recent near-peak region is held for subsequent calculations. In an alternative method in the saturated region206, the current value of μx(t) is used, i.e., μx,cap=μx(t).

With continued reference toFIG.4, from the ground truth estimating and labeling step104the algorithm proceeds to a training step106. The training step106includes training a regression model to predict tractive limit. The training step106also includes training a classification model to predict wheel stability status (e.g., linear region, near-peak region, or saturated region).

In algorithm step108, testing and prediction of the training model is performed using input features from a new dataset that was not used to develop the training model. For each wheel, input features may include longitudinal velocity, longitudinal acceleration, lateral acceleration, yaw rate, wheel angular velocity, wheel angular acceleration, normal force, steering wheel angle, and lateral distance between vehicle center of gravity and wheel center. For each wheel, predicted output includes a tractive limit prediction with associated uncertainty from a machine learning regression algorithm. Predicted output also includes a wheel stability status and uncertainty from a classification algorithm. Predicted tractive limit and wheel stability status classification outputs are compared to ground truth.

Continuing to refer toFIG.3, the algorithm proceeds to decision block110. If the predicted outputs in step108are judged to satisfy performance requirements for the algorithm, the algorithm ends at block112. If the outputs in step108are judged to not meet performance requirements for the algorithm, execution returns to step104and training is repeated. For subsequent iterations, hyperparameter tuning of the model may use random search to define distributions for each hyperparameter, sample a number of combinations of hyperparameter values, and determine the combination that provides the best model performance.

Testing results have verified that machine learning regression algorithm tractive limit estimations produce accurate predictions that correlate with ground truth. Thereby, it can be concluded that a relationship between tractive limit and measurement signals can be learned using machine learning principles. Similarly, testing results have verified that wheel stability status can be predicted accurately with machine learning classification algorithms.

The processes, methods, or algorithms disclosed herein can be deliverable to/implemented by a processing device, controller, or computer, which can include any existing programmable electronic control unit or dedicated electronic control unit. Similarly, the processes, methods, or algorithms can be stored as data and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in a software executable object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software and firmware components. Such example devices may be on-board as part of a vehicle computing system or be located off-board and conduct remote communication with devices on one or more vehicles.

The tractive limit estimate and wheel stability status estimate obtained from the disclosed method may be provided to a controller to improve vehicle control performance. By way of nonlimiting example, a traction control system, a yaw control system, and an antilock braking system may all benefit from having tractive limit estimate and/or wheel stability status estimate information available.

A machine learning-based tractive limit and wheel stability status estimation method of the present disclosure offers several advantages. Learning the relationship between the input features and the tractive limit avoids the need to determine road surface condition or estimate the tire-road friction coefficient. The method of the present disclosure uses only on-board sensor data and learns online. Performance can continuously improve by learning from more data and enhancing the regression and classification models. The method is applicable to all of the wheels on the vehicle and is scalable to different vehicles. By recognizing and minimizing tire saturation, control performance can be maximized, thereby improving safety and maneuverability.

The description of the present disclosure is merely exemplary in nature and variations that do not depart from the gist of the present disclosure are intended to be within the scope of the present disclosure. Such variations are not to be regarded as a departure from the spirit and scope of the present disclosure.