Patent ID: 12233853

In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts. The figures are not to scale. Instead, the thickness of the layers or regions may be enlarged in the drawings. Although the figures show layers and regions with clean lines and boundaries, some or all of these lines and/or boundaries may be idealized. In reality, the boundaries and/or lines may be unobservable, blended, and/or irregular.

As used herein, unless otherwise stated, the term “above” describes the relationship of two parts relative to Earth. A first part is above a second part, if the second part has at least one part between Earth and the first part. Likewise, as used herein, a first part is “below” a second part when the first part is closer to the Earth than the second part. As noted above, a first part can be above or below a second part with one or more of: other parts therebetween, without other parts therebetween, with the first and second parts touching, or without the first and second parts being in direct contact with one another.

As used in this patent, stating that any part (e.g., a layer, film, area, region, or plate) is in any way on (e.g., positioned on, located on, disposed on, or formed on, etc.) another part, indicates that the referenced part is either in contact with the other part, or that the referenced part is above the other part with one or more intermediate part(s) located therebetween.

As used herein, connection references (e.g., attached, coupled, connected, and joined) may include intermediate members between the elements referenced by the connection reference and/or relative movement between those elements unless otherwise indicated. As such, connection references do not necessarily infer that two elements are directly connected and/or in fixed relation to each other. As used herein, stating that any part is in “contact” with another part is defined to mean that there is no intermediate part between the two parts.

Unless specifically stated otherwise, descriptors such as “first,” “second,” “third,” etc., are used herein without imputing or otherwise indicating any meaning of priority, physical order, arrangement in a list, and/or ordering in any way, but are merely used as labels and/or arbitrary names to distinguish elements for ease of understanding the disclosed examples. In some examples, the descriptor “first” may be used to refer to an element in the detailed description, while the same element may be referred to in a claim with a different descriptor such as “second” or “third.” In such instances, it should be understood that such descriptors are used merely for identifying those elements distinctly that might, for example, otherwise share a same name.

As used herein, “approximately” and “about” modify their subjects/values to recognize the potential presence of variations that occur in real world applications. For example, “approximately” and “about” may modify dimensions that may not be exact due to manufacturing tolerances and/or other real world imperfections as will be understood by persons of ordinary skill in the art. For example, “approximately” and “about” may indicate such dimensions may be within a tolerance range of +/−10% unless otherwise specified in the below description. As used herein “substantially real time” refers to occurrence in a near instantaneous manner recognizing there may be real world delays for computing time, transmission, etc. Thus, unless otherwise specified, “substantially real time” refers to real time+/−1 second.

As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events.

As used herein, “processor circuitry” is defined to include (i) one or more special purpose electrical circuits structured to perform specific operation(s) and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors), and/or (ii) one or more general purpose semiconductor-based electrical circuits programmable with instructions to perform specific operations and including one or more semiconductor-based logic devices (e.g., electrical hardware implemented by one or more transistors). Examples of processor circuitry include programmable microprocessors, Field Programmable Gate Arrays (FPGAs) that may instantiate instructions, Central Processor Units (CPUs), Graphics Processor Units (GPUs), Digital Signal Processors (DSPs), XPUs, or microcontrollers and integrated circuits such as Application Specific Integrated Circuits (ASICs). For example, an XPU may be implemented by a heterogeneous computing system including multiple types of processor circuitry (e.g., one or more FPGAs, one or more CPUs, one or more GPUs, one or more DSPs, etc., and/or a combination thereof) and application programming interface(s) (API(s)) that may assign computing task(s) to whichever one(s) of the multiple types of processor circuitry is/are best suited to execute the computing task(s).

DETAILED DESCRIPTION

A trailer can be coupled to a vehicle to increase a towing capacity of the vehicle. In some cases, a load secured to the trailer may shift, resulting in trailer sway during vehicle travel. In some cases, an upcoming road change (e.g., a curve, an incline, a speed limit change) may result in trailer sway. Such trailer sway may alter maneuverability and/or steering capabilities of the vehicle. For instance, the trailer sway may result in vehicle oversteer and/or understeer. As disclosed herein, “understeer” refers to a tendency of the vehicle to turn less than an amount commanded by an operator of the vehicle, resulting in a larger turn radius of the vehicle. Conversely, “oversteer” refers to a tendency of the vehicle to turn more than the amount commanded by the operator, resulting in a smaller turn radius of the vehicle. In some instances, the trailer sway may result in slippage of the wheels of the vehicle. In some instances, trailer sway may result in a significant shift in a load distribution of the vehicle.

Currently, some vehicles react to trailer sway after the trailer sway has occurred. Some vehicles include emergency vehicle control systems such as an anti-lock brake system (ABS), a traction control system (TCS), and/or electronic stability control (ESC) that utilizes a physical actuator (e.g., an actuator that is functionally similar to a master cylinder, a brake booster, and an ABS pump). One example of such an emergency vehicle control system is Trailer Sway Control™ as implemented by Ford®. Trailer Sway Control™ is an algorithm to detect and reduce trailer sway after trailer sway begins. Some vehicles also include adaptive cruise control systems (e.g., ADAS, active drive assist) such as automatic cruise control (ACC, iACC), lane centering, and semi-autonomous driving (e.g., partially autonomous driving). One example of such a semi-autonomous driving system is BlueCruise™ as implemented by Ford®. The adaptive cruise control systems control vehicles, but do not support use with a trailer coupled to the vehicle. For example, in response to trailer sway occurring while driving, the ABS activates the vehicle brakes, but the brakes on the trailer are not activated.

Examples disclosed herein control trailer sway of a vehicle to reduce or eliminate the need to activate vehicle emergency controls. Examples disclosed herein prevent premature activation (e.g., unnecessary activation) of vehicle emergency controls by preventing the conditions that may trigger the activation of the vehicle emergency controls. Example vehicle control circuitry disclosed herein obtains sensor data from one or more sensors on the vehicle and/or the trailer. In some examples, prediction circuitry predicts, based on the sensor data, whether trailer sway is likely to occur. In such examples, example trailer sway avoidance circuitry is to activate, in response to a prediction that trailer sway is likely to occur, at least one vehicle control of the vehicle to prevent the trailer sway from occurring. As used herein, a vehicle control includes limiting the vehicle speed, cancelling autonomous driving, instructing the operator of the vehicle to resecure the load in the trailer, pre-charging a trailer sway control system, and/or reducing available vehicle torque. In some examples, the at least one vehicle control includes communicating with the operator of the vehicle to warn the operator of a load shift. In some examples, the vehicle control includes applying torque to a wheel of a trailer coupled to the vehicle.

Advantageously, examples disclosed herein prevent trailer sway conditions from occurring by predicting trailer sway conditions and reacting to the prediction by activating vehicle controls such as applying a torque to wheels of the trailer coupled to the vehicle. Some advantages of the examples disclosed herein include an improved maneuverability of the vehicle and a reduced need for user input.

FIG.1illustrates an example vehicle100and an example trailer102coupled thereto, where the vehicle100implements example trailer sway control circuitry104in accordance with teachings of this disclosure. In some examples, the trailer sway control circuitry104determines a load distribution and notifies an operator of the vehicle to adjust the load distribution on example vehicle wheels106(e.g., front vehicle wheels106A,106B, rear vehicle wheels106C,106D) and example trailer wheels107A,107B. In this example, the trailer sway control circuitry104is electrically and/or communicatively coupled to one or more example vehicle sensors108implemented on the vehicle100and/or one or more example trailer sensors110implemented on the trailer102.

In the illustrated example ofFIG.1, the trailer102is coupled to the vehicle100via an example tongue112(e.g., trailer hitch). The trailer sway control circuitry104may determine various tongue loads (e.g., a stable tongue, a light tongue, a heavy tongue load) for a variety of roads (e.g., level ground, downhill, uphill, straight, curved, or a combination of the road type, etc.). As used herein, a stable tongue load is a load on the tongue112that is in between 10% and 15% of the weight of the vehicle100. As used herein, a light tongue load is a load on the tongue112that is less than a first tongue load threshold (e.g., less than 10%). As used herein, a heavy tongue load is a load on the tongue112that is greater than a second tongue load threshold (e.g., greater than 15%).

In the illustrated example ofFIG.1, the trailer sway control circuitry104is communicatively and/or operatively coupled to a vehicle acceleration system113(e.g., torque system) to control operation thereof. In some examples, the vehicle acceleration system113is implemented by one or more electric motors114operatively coupled to respective ones of the vehicle wheels106(e.g., a vehicle acceleration system, an electric motor is coupled to the vehicle wheel106A, etc.). In some examples, the vehicle acceleration system113is implemented by a first gas-powered axle118A operatively coupled to the front vehicle wheels106A,106B and/or a second gas-powered axle118B operatively coupled to the rear vehicle wheels106C,106D at respective ones of the vehicle wheels106. For example, the trailer sway control circuitry104provides control signals to the vehicle acceleration system113to cause the vehicle acceleration system113to increase and/or decrease (e.g., apply a positive torque and/or apply a negative torque) to the vehicle wheels106(e.g., the front vehicle wheels106A,106B, and the rear vehicle wheels106C,106D). In some examples, the vehicle acceleration system113applies a negative torque to the vehicle wheels106by using example vehicle brakes116A,116B,116C,116D operatively coupled to respective ones of the vehicle wheels106A,106B,106C,106D.

The trailer sway control circuitry104is communicatively and/or operatively coupled to a trailer acceleration system120(e.g., trailer torque system) to control operation thereof. In some examples, the trailer acceleration system120is implemented by one or more electric motors115A,115B operatively coupled to respective ones of the trailer wheels107(e.g., a trailer acceleration system120, an electric motor115A that is coupled to the trailer wheel107A). For example, the trailer sway control circuitry104provides control signals to the trailer acceleration system120to cause the trailer acceleration system120to increase and/or decrease torque (e.g., apply a positive torque and/or apply a negative torque) to the trailer wheels107(e.g., the trailer wheel107A, the trailer wheel107B).

In some examples, the trailer acceleration system120includes example trailer brakes117A,117B operatively coupled to respective ones of the trailer wheels107A,107B. In some examples, the trailer brakes117are regenerative brakes. In some examples, the trailer brakes117are conventional brakes.

In the illustrated example ofFIG.1, the trailer sway control circuitry104is communicatively coupled to one or more of the vehicle sensors108to obtain sensor data (e.g., vehicle sensor data) therefrom. In this example, the vehicle sensors108include one or more load sensors108A (e.g., Onboard Scales (OBS™) as commercialized by Ford®), an example backup camera108B (e.g., a rear camera), example ride height sensors108C, an example yaw rate sensor108D, an example vehicle pitch rate sensor108E, an example vehicle roll rate sensor108F, an example longitudinal acceleration sensor108G, an example lateral acceleration sensor108H, example wheel speed sensor(s)108I, an example passenger detection sensor108I (e.g., interior weight sensor), an example trailer hitch sensor108K (e.g., Smart Hitch™ as commercialized by Ford®), an example front camera108L, an example friction sensor108M, an example mass estimation sensor108N, an example driver state camera1080, an example wheel torque sensor108P, an example global positioning system (GPS)108Q, and an example turn signal sensor108R. In some examples, one or more other sensors may be used in addition to or instead of the vehicle sensors108shown inFIG.1. In some examples, the trailer sway control circuitry104is further communicatively coupled to the example trailer sensors110to obtain trailer sensor data therefrom. The trailer sensor data can include, for example, a trailer pitch, a trailer weight, a trailer acceleration, load (e.g., weight) on a side of the trailer etc.

In this example, the vehicle sensors108and/or the trailer sensors110are configured to send sensor data (e.g., the vehicle sensor data and/or the trailer sensor data) to the trailer sway control circuitry104for use in predicting of a trailer sway condition associated with the vehicle100.

In some examples, the trailer sway control circuitry104determines (e.g., calculates) and/or otherwise predicts a likelihood of trailer sway (e.g., a trailer sway likelihood score, a trailer sway probability) based on the sensor data. In such examples, the trailer sway control circuitry104compares the trailer sway likelihood score to one or more thresholds to determine whether trailer sway of the vehicle100and/or the trailer102is likely to occur. In some examples, in response to predicting that trailer sway of the vehicle100and/or the trailer102is likely to occur, the trailer sway control circuitry104notifies an operator of the vehicle100to adjust the load distribution of the vehicle100and/or the speed of the vehicle100to reduce a likelihood of the trailer sway occurring and/or improve maneuverability of the vehicle100. In some examples, the trailer sway control circuitry104instructs the operator of the vehicle to pull over and adjust the load, which may have shifted during operation of the vehicle100and the trailer102.

In the illustrated example ofFIG.1, the vehicle100includes an example user interface130to display instructions and/or indications to an operator of the vehicle100. In some examples, the user interface130(e.g., a human machine interface (HMI)) is communicatively coupled to the trailer sway control circuitry104. The trailer sway control circuitry104, in response to predicting that trailer sway of the vehicle100and/or the trailer102is likely to occur, causes the user interface130to display an indication (e.g., a warning) to the operator that trailer sway is likely to occur. In some examples, the user interface130displays that the trailer sway has been detected. In some examples, the trailer sway control circuitry104causes the user interface130to display instructions to the operator. For example, the trailer sway control circuitry104can cause the user interface130to generate a visual and/or audible (e.g., verbal) instruction to the operator to increase a speed of the vehicle100, reduce the speed of the vehicle100, steer the vehicle100to a side of the road, etc. In some examples, the trailer sway control circuitry104is to activate a torque of the vehicle wheels106and/or activate a torque of the trailer wheels107.

In some examples, the vehicle100is a conventionally fueled rear wheel drive truck that is pulling the trailer102(e.g., a tag trailer). In some examples, the vehicle wheels106A,106B,106C, and106D can provide a negative (e.g., decelerating) force on the vehicle100. For example, a master cylinder or an anti-lock brake (e.g., ABS or EBB) pump can control the vehicle brakes116A,116B,116C,116D and an example integrated trailer brake controller111, which can control the trailer brakes117A,117B to provide a negative force. In some examples, the rear vehicle wheels106C,106D may provide a positive (e.g., accelerating) force via the electric motors114C,114D and/or the second gas-powered axle118B. In some examples where the trailer102is a conventional trailer without an electric motor coupled to the trailer wheels107A,107B, the trailer102may only provide negative force.

In some examples, the vehicle100is an electric vehicle, wherein ones of the vehicle wheels106A,106B,106C, and106D are connected to one or more electric motors, where each electric motor, in response to a signal, may apply a positive (e.g., accelerating) torque or a negative (e.g., decelerating) torque to one or more of the vehicle wheels106. In some examples, a negative torque may be applied via a regenerative brake. In some examples, the trailer102is an electrically powered trailer, such that the trailer wheels107A,107B may provide a positive or a negative torque at the trailer wheels107A,107B via a connection to one or more electric motor.

FIG.2is a block diagram of the example trailer sway control circuitry104ofFIG.1. The trailer sway control circuitry104is to prevent trailer sway by monitoring load changes of the trailer102and/or changes in a projected path of the vehicle100. The trailer sway control circuitry104ofFIG.2may be instantiated (e.g., creating an instance of, bring into being for any length of time, materialize, implement, etc.) by processor circuitry such as a central processing unit executing instructions. Additionally or alternatively, the trailer sway control circuitry104ofFIG.2may be instantiated (e.g., creating an instance of, bring into being for any length of time, materialize, implement, etc.) by an ASIC or an FPGA structured to perform operations corresponding to the instructions. It should be understood that some or all of the circuitry ofFIG.2may, thus, be instantiated at the same or different times. Some or all of the circuitry may be instantiated, for example, in one or more threads executing concurrently on hardware and/or in series on hardware. In some examples, some or all of the circuitry ofFIG.2may be implemented by microprocessor circuitry executing instructions to implement one or more virtual machines and/or containers.

The trailer sway control circuitry104includes example sensor interface circuitry202, example prediction circuitry204, example control activation circuitry206, example parameter estimation circuitry208, example communication interface circuitry210, example path monitoring circuitry212, and an example sensor database214.

In the illustrated example ofFIG.2, the sensor interface circuitry202obtains and/or otherwise receives sensor data from the vehicle sensors108and/or the trailer sensors110ofFIG.1. In some examples, the sensor interface circuitry202retrieves sensor data from the sensor database214. In some examples, the sensor interface circuitry202is instantiated by processor circuitry executing sensor interface instructions and/or configured to perform operations such as those represented by the flowcharts ofFIGS.7-9. Various sensor data types are described in connection withFIG.3.

The sensor database214stores the sensor data utilized and/or obtained by the trailer sway control circuitry104and/or historical data (e.g., driver settings). The sensor database214ofFIG.2is implemented by any memory, storage device and/or storage disc for storing data such as, for example, flash memory, magnetic media, optical media, solid state memory, hard drive(s), thumb drive(s), etc. Furthermore, the sensor data stored in the example sensor database214may be in any data format such as, for example, binary data, comma delimited data, tab delimited data, structured query language (SQL) structures, etc. While, in the illustrated example, the example sensor database214is illustrated as a single device, the sensor database214and/or any other data storage devices described herein may be implemented by any number and/or type(s) of memories. In some examples, the sensor database214is instantiated by processor circuitry executing sensor database instructions and/or configured to perform operations such as those represented by the flowcharts ofFIGS.7-9.

In the illustrated example ofFIG.2, the prediction circuitry204predicts whether a trailer sway condition is likely to occur. In some examples, the prediction circuitry204is instantiated by processor circuitry executing prediction instructions and/or configured to perform operations such as those represented by the flowcharts ofFIGS.7-9. For example, the prediction circuitry204determines (e.g., calculates) a trailer sway likelihood score (e.g., a trailer sway probability) based on sensor data. The prediction circuitry204retrieves (e.g., accesses) sensor data from the example sensor database214to make a prediction. In some examples, the prediction circuitry204determines if the load experienced at respective ones of the vehicle wheels106or the trailer wheels107meets manufacturer recommendations. For example, the example load sensor108A may determine the load experienced at the vehicle wheel106A and the load experienced at vehicle wheel106B in substantially real-time and/or simultaneously. The parameter estimation circuitry208may perform calculations using the data generated from the load sensor108A. The parameter estimation circuitry208may provide the calculated result to the prediction circuitry204, for use in determining whether the load meets the manufacturer recommendations. In some examples, the prediction circuitry204may determine if the load experienced at a one or more of the vehicle wheels106and/or a one or more of the trailer wheels107is greater than a threshold. In some examples, the prediction circuitry204instructs the control activation circuitry206to apply, via the vehicle acceleration system113and/or the trailer acceleration system120, torque to the vehicle wheels106and/or the trailer wheels107in response to the load not satisfying the manufacturer recommendations.

In the illustrated example ofFIG.2, the control activation circuitry206activates at least one vehicle control. In some examples, the control activation circuitry206is instantiated by processor circuitry executing trailer sway avoidance instructions and/or configured to perform operations such as those represented by the flowcharts ofFIGS.7-9. For example, to active the at least one vehicle control, the control activation circuitry206may apply a torque at one or more of the vehicle wheels106and/or one or more of the trailer wheels107. The applied torque may increase acceleration (e.g., apply power, speed up, etc.) or decrease acceleration (e.g., brake, slow down, etc.) of the vehicle100and/or the trailer102. For example, the control activation circuitry206provides one or more control signals to the vehicle acceleration system113to cause the vehicle acceleration system113to increase and/or decrease torque (e.g., apply a positive torque and/or apply a negative torque) to the vehicle wheels106(e.g., the front vehicle wheels106A,106B, and the rear vehicle wheels106C,106D) and/or to cause the trailer acceleration system120to increase and/or decrease torque (e.g., apply a positive torque and/or apply a negative torque) to the trailer wheels107. In some examples, the control activation circuitry206causes the vehicle acceleration system113to apply a negative torque to the vehicle wheels106by using the vehicle brakes116, and/or causes the trailer acceleration system113to apply a negative torque to the trailer wheels107using the trailer brakes117.

In some examples, the vehicle100and the trailer102travel downhill along a curve. In such examples, the control activation circuitry206may apply a torque (e.g., a brake or negative acceleration) to the trailer102before applying a torque (e.g., a brake, or negative acceleration) to the vehicle100. In such examples, the control activation circuitry206prevents the trailer102from accelerating faster than the vehicle100when travelling downhill.

In some examples, the control activation circuitry206may simultaneously (e.g., within 0.1 seconds) apply a negative torque to one or more of the trailer wheels107and apply a positive torque to one or more of the vehicle wheels106. In such examples, a combination of the positive torque and the negative torque causes the vehicle100and the trailer102to move along a substantially straight path, thus reducing trailer sway. In some examples, the control activation circuitry206may apply a positive torque that is similar and/or substantially equal in magnitude to the negative torque. In such examples, the positive and negative torques do not cause unexpected and/or undesired changes in the speed of the vehicle100and the trailer102, thus resulting in a more pleasant driving experience for the operator of the vehicle100.

In some examples, where the vehicle100and the trailer102are accelerating out of a curve and the vehicle100and the trailer102is equipped with torque vectoring (e.g., mechanical or electrical), the control activation circuitry206limits the power to at least one inside wheel (e.g., the vehicle wheel106closer to the inside of the curve and/or the trailer wheel107closer to the inside of the curve) to prevent slip.

In some examples, prior to a curve, turn, or other road change, the control activation circuitry206adjusts suspension, steering, and/or braking of the vehicle100and/or the trailer102. In such examples, the control activation circuitry206activates previously saved settings for adjusting the suspension, steering and/or braking in response to an upcoming road change. For example, the trailer sway control circuitry104may store a first setting for a first curve with a first turn radius in the sensor database214(e.g., a two hundred and fifty meter radius curve corresponds to setting A). The trailer sway control circuitry104may store a second setting for a second curve with a second turn radius in the sensor database214(e.g., a seven hundred and fifty meter radius curve corresponds setting B). The control activation circuitry206executes an acceleration protocol (e.g., a braking protocol) in response to the example path monitoring circuitry212determining that the first curve with the first turn radius is in the next road segment.

In some examples, while the vehicle100drives over the curve, the trailer sway control circuitry104stores a plurality of settings relating to the curve as historical data. For example, in response to the first setting based on the first curve resulting in a stable ride (e.g., a ride without activation of the vehicle emergency controls), the trailer sway control circuitry104stores the first setting in the sensor database214. When a similar curve (e.g., a curve having a turn radius within a threshold percentage of the first turn radius) is approached, as determined by the path monitoring circuitry212, the trailer sway control circuitry104retrieves the first setting and applies the first setting to achieve a similarly stable ride. In some examples, known machine learning or artificial intelligence techniques may be used to determine the similarity of the curves and/or associate the likelihood of trailer sway of the first setting to other curves that are within the threshold percentage. The path monitoring circuitry212determines features of the road segments based on images of the road and compares the features of the road segments to other road segments. As used herein, a “feature” of the road segment includes a curve length, an incline, a grade, a road condition (e.g., slippery, smooth, bumpy), a weather condition (e.g., sunny, snowing, raining).

In some examples, in response to the trailer sway control circuitry104predicting a trailer sway condition (e.g., not a stable ride) during navigation of the first curve based on the first settings, the trailer sway control circuitry104stores information associated with the predicted trailer sway condition with the first setting in the sensor database214. The next time a similar curve is approached, the trailer sway control circuitry104may alter the first setting or select an alternative setting from the sensor database214that is more likely to result in a stable ride.

In some examples, the control activation circuitry206is to dynamically adjust trailer brake gain based on a grade of the road. For example, the sensor database214may store a first trailer brake gain setting for use with level (e.g., flat) ground, a second trailer brake gain setting for use with an uphill (e.g., positively inclined) grade, and a third trailer brake gain setting for use with a downhill (e.g., negatively inclined) grade. The trailer sway control circuitry104, in response to the path monitoring circuitry212determining the grade of the road segment, may retrieve the trailer brake gain setting that corresponds to the grade of the road segment. The trailer sway control circuitry104then instructs the control activation circuitry206to adjust the trailer brake gain based on the trailer brake gain setting retrieved.

In some examples, the control activation circuitry206adjusts a normal force used in emergency control system techniques (e.g., ESC, ABS, TCS). As used herein, the normal force corresponds to a product of a mass of the vehicle100, an acceleration of the vehicle100, and the cosine of an angle theta, where theta corresponds to an angle of an inclined plane on which the vehicle100is resting. In some examples, the control activation circuitry206estimates the normal force based on estimated height at a center of gravity of the vehicle100, measured acceleration of the vehicle100(e.g., lateral acceleration and/or longitudinal acceleration), an estimated bank of a road surface, and an estimated pitch of the road surface. For example, the load sensor108A may determine load data304(FIG.3) and the control activation circuitry206adjusts the normal load value based on the load data304. The load data304, determined by the load sensor108A, results in a more accurate normal force baseline and more accurate dynamic normal force estimation on ones of the vehicle wheels106and ones of the trailer wheels107. The more accurate normal force baseline and dynamic normal force estimation results in more accurate control, which enables the emergency control system techniques (e.g., ESC, ABS, TCS) to have more accurate starting torque targets. A more accurate starting torque target allows the emergency control system techniques to use all of the available grip at ones of the vehicle wheels106and/or the trailer wheels107.

In some examples, the control activation circuitry206may increase or decrease the sensitivity to a trailer sway control technique based on predicted trailer sway. For example, the trailer sway control circuitry104may determine, based on a trailer load estimation and tongue load estimation, to increase or decrease the sensitivity of a trailer sway control technique before instructing the control activation circuitry206to either gain or lose sensitivity in the trailer sway control techniques.

In the illustrated example ofFIG.2, the parameter estimation circuitry208is to calculate data from the vehicle sensors108, to be used by the example prediction circuitry204. In some examples, the parameter estimation circuitry208is instantiated by processor circuitry executing parameter estimation instructions and/or configured to perform operations such as those represented by the flowcharts ofFIGS.7-9. For example, the parameter estimation circuitry208may estimate the load of the vehicle100. As used herein, a load refers to cargo, occupants, and/or fuel carried by the trailer102and/or the vehicle100. In some examples, the load corresponds to a mass or a weight (e.g., a force based on mass and acceleration). In some examples, the parameter estimation circuitry208estimates a total mass and/or a total weight of the vehicle100and/or the trailer102based on a vehicle weight of the vehicle100, a trailer weight of the trailer102, and the load carried by the vehicle100and/or the trailer102.

The sensors (e.g., the load sensors108A, the mass estimation sensor108N, the trailer hitch sensor108K, etc.) may determine the load (e.g., a mass that corresponds to the load, a weight that corresponds to the load) of the vehicle100and stores the value in the sensor database214. The parameter estimation circuitry208may estimate the load of the vehicle100by using the values stored in the sensor database214. For example, the parameter estimation circuitry208estimates the mass of the vehicle100and/or trailer102by using known mass estimation techniques. An example of a mass estimation technique includes comparing the vehicle acceleration with the torque applied to determine the mass of the vehicle100.

The parameter estimation circuitry208may estimate the weight of the vehicle100based on different sources of sensor data. For example, a first sensor (e.g., the load sensor108A) may determine that the weight of the vehicle100is a first value (e.g., five thousand pounds), while a second sensor (e.g., the trailer hitch sensor108K) may determine that the weight of the vehicle100is a second value (e.g., four thousand pounds). The first value is stored in the example sensor database214and the second value is stored in the sensor database214. In some examples, the parameter estimation circuitry208may estimate the weight based on the first value and the second value (e.g., may estimate the weight of the vehicle100to be four thousand and eight hundred pounds).

In the illustrated example ofFIG.2, the communication interface circuitry210communicates to an operator of the vehicle100via the user interface130. In some examples, the communication interface circuitry210is instantiated by processor circuitry executing communication interface instructions and/or configured to perform operations such as those represented by the flowcharts ofFIGS.7-9. In some examples, the communication interface circuitry210controls a display of the user interface130. In some examples, in response to the prediction circuitry204predicting that a trailer sway condition is likely to occur, the communication interface circuitry210causes the display of the user interface130to display an indication (e.g., a warning) to an operator of the vehicle100. In some such examples, the communication interface circuitry210causes the display of the user interface130to display instructions to the operator, where the instructions instruct the operator to reduce a speed of the vehicle100, increase the speed of the vehicle100, pull the vehicle100over to a side of the road, resecure and/or redistribute a shifted load in the trailer102, etc.

In the illustrated example ofFIG.2, the path monitoring circuitry212monitors a path of the vehicle100and/or the trailer102. In some examples, the path monitoring circuitry212is instantiated by processor circuitry executing path monitoring instructions and/or configured to perform operations such as those represented by the flowcharts ofFIGS.7-9. For example, the path monitoring circuitry212may detect an upcoming road change (e.g., a stop sign, a speed limit, a curve) in the projected path of the vehicle100based on images generated by the example front camera108L. In some examples, the front camera108L is implemented by a radar sensor or a lidar sensor and the images generated are radar images or lidar images. In some examples, the path monitoring circuitry212determines a speed limit along segments of a road on which the vehicle100is to travel. For example, the example path monitoring circuitry212may determine a first road segment that has a first speed limit (e.g., fifty miles per hour), and a second road segment that has a second speed limit (e.g., forty miles per hour). The trailer sway control circuitry104may use the indication from the path monitoring circuitry212to determine a torque to apply to vehicle wheels106and/or the trailer wheels107. For example, the trailer sway control circuitry104uses the path monitoring circuitry212to detect upcoming road changes (e.g., route metrices, vehicle path data312), and the example prediction circuitry204uses the upcoming road changes to determine if a trailer sway condition is likely to occur. In response to determining that a trailer sway condition is likely to occur, the trailer sway control circuitry104may instruct the control activation circuitry206to apply a torque to ones of the vehicle wheels106and/or the trailer wheels107.

In some examples, the trailer sway control circuitry104includes means for obtaining sensor data. For example, the means for obtaining sensor data may be implemented by the sensor interface circuitry202. In some examples, the sensor interface circuitry202may be instantiated by processor circuitry such as the example processor circuitry1012of FIG.10. For instance, the sensor interface circuitry202may be instantiated by the example microprocessor1100ofFIG.11executing machine executable instructions such as those implemented by at least block702ofFIG.7, block802ofFIG.8, and block902ofFIG.9. In some examples, the sensor interface circuitry202may be instantiated by hardware logic circuitry, which may be implemented by an ASIC, XPU, or the FPGA circuitry1200ofFIG.12structured to perform operations corresponding to the machine readable instructions. Additionally or alternatively, the sensor interface circuitry202may be instantiated by any other combination of hardware, software, and/or firmware. For example, the sensor interface circuitry202may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, an XPU, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to execute some or all of the machine readable instructions and/or to perform some or all of the operations corresponding to the machine readable instructions without executing software or firmware, but other structures are likewise appropriate.

In some examples, the trailer sway control circuitry104includes means for monitoring the vehicle100. For example, the means for monitoring the vehicle may be implemented by the prediction circuitry204. In some examples, the prediction circuitry204may be instantiated by processor circuitry such as the example processor circuitry1012ofFIG.10. For instance, the prediction circuitry204may be instantiated by the example microprocessor1100ofFIG.11executing machine executable instructions such as those implemented by at least blocks704,714ofFIG.7, blocks812,818ofFIG.8, and blocks904,914ofFIG.9. In some examples, the prediction circuitry204may be instantiated by hardware logic circuitry, which may be implemented by an ASIC, XPU, or the FPGA circuitry1200ofFIG.12structured to perform operations corresponding to the machine readable instructions. Additionally or alternatively, the prediction circuitry204may be instantiated by any other combination of hardware, software, and/or firmware. For example, the prediction circuitry204may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, an XPU, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to execute some or all of the machine readable instructions and/or to perform some or all of the operations corresponding to the machine readable instructions without executing software or firmware, but other structures are likewise appropriate.

In some examples, the trailer sway control circuitry104includes means for predicting, based on sensor data from one or more sensors of the vehicle, whether a trailer sway condition is likely to occur. For example, the means for predicting, based on sensor data from one or more sensors of the vehicle, whether a trailer sway condition is likely to occur may be implemented by the prediction circuitry204. In some examples, the prediction circuitry204may be instantiated by processor circuitry such as the example processor circuitry1012ofFIG.10. For instance, the prediction circuitry204may be instantiated by the example microprocessor1100ofFIG.11executing machine executable instructions such as those implemented by at least block906ofFIG.9. In some examples, the prediction circuitry204may be instantiated by hardware logic circuitry, which may be implemented by an ASIC, XPU, or the FPGA circuitry1200ofFIG.12structured to perform operations corresponding to the machine readable instructions. Additionally or alternatively, the prediction circuitry204may be instantiated by any other combination of hardware, software, and/or firmware. For example, the prediction circuitry204may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, an XPU, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to execute some or all of the machine readable instructions and/or to perform some or all of the operations corresponding to the machine readable instructions without executing software or firmware, but other structures are likewise appropriate.

In some examples, the trailer sway control circuitry104includes means for activating at least one vehicle control of the vehicle to prevent the trailer sway condition from occurring. For example, the means for activating at least one vehicle control of the vehicle to prevent the trailer sway condition from occurring may be implemented by the control activation circuitry206. In some examples, the control activation circuitry206may be instantiated by processor circuitry such as the example processor circuitry1012ofFIG.10. For instance, the control activation circuitry206may be instantiated by the example microprocessor1100ofFIG.11executing machine executable instructions such as those implemented by at least blocks710,712ofFIG.7, blocks808,816ofFIG.8, and block910ofFIG.9. In some examples, the control activation circuitry206may be instantiated by hardware logic circuitry, which may be implemented by an ASIC, XPU, or the FPGA circuitry1200ofFIG.12structured to perform operations corresponding to the machine readable instructions. Additionally or alternatively, the control activation circuitry206may be instantiated by any other combination of hardware, software, and/or firmware. For example, the control activation circuitry206may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, an XPU, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to execute some or all of the machine readable instructions and/or to perform some or all of the operations corresponding to the machine readable instructions without executing software or firmware, but other structures are likewise appropriate.

In some examples, the trailer sway control circuitry104includes means for applying torque to a wheel of a trailer coupled to the vehicle. For example, the means for applying torque to a wheel of a trailer coupled to the vehicle may be implemented by the control activation circuitry206and/or the trailer acceleration system120. In some examples, the control activation circuitry206may be instantiated by processor circuitry such as the example processor circuitry1012ofFIG.10. For instance, the control activation circuitry206may be instantiated by the example microprocessor1100ofFIG.11executing machine executable instructions such as those implemented by at least block912ofFIG.9. In some examples, the control activation circuitry206may be instantiated by hardware logic circuitry, which may be implemented by an ASIC, XPU, or the FPGA circuitry1200ofFIG.12structured to perform operations corresponding to the machine readable instructions. Additionally or alternatively, the control activation circuitry206may be instantiated by any other combination of hardware, software, and/or firmware. For example, the control activation circuitry206may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, an XPU, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to execute some or all of the machine readable instructions and/or to perform some or all of the operations corresponding to the machine readable instructions without executing software or firmware, but other structures are likewise appropriate.

In some examples, the trailer sway control circuitry104includes means for estimating load at ones of the vehicle wheels and/or trailer wheels. For example, the means for estimating load at ones of the vehicle wheels and/or trailer wheels may be implemented by the parameter estimation circuitry208. In some examples, the parameter estimation circuitry208may be instantiated by processor circuitry such as the example processor circuitry1012ofFIG.10. For instance, the parameter estimation circuitry208may be instantiated by the example microprocessor1100ofFIG.11executing machine executable instructions such as those implemented by at least block804ofFIG.8. In some examples, the parameter estimation circuitry208may be instantiated by hardware logic circuitry, which may be implemented by an ASIC, XPU, or the FPGA circuitry1200ofFIG.12structured to perform operations corresponding to the machine readable instructions. Additionally or alternatively, the parameter estimation circuitry208may be instantiated by any other combination of hardware, software, and/or firmware. For example, the parameter estimation circuitry208may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, an XPU, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to execute some or all of the machine readable instructions and/or to perform some or all of the operations corresponding to the machine readable instructions without executing software or firmware, but other structures are likewise appropriate.

In some examples, the trailer sway control circuitry104includes means for warning an operator via a human machine interface. For example, the means for warning an operator via a human machine interface may be implemented by communication interface circuitry210. In some examples, the communication interface circuitry210may be instantiated by processor circuitry such as the example processor circuitry1012ofFIG.10. For instance, the communication interface circuitry210may be instantiated by the example microprocessor1100ofFIG.11executing machine executable instructions such as those implemented by at least block810ofFIG.8. In some examples, the communication interface circuitry210may be instantiated by hardware logic circuitry, which may be implemented by an ASIC, XPU, or the FPGA circuitry1200ofFIG.12structured to perform operations corresponding to the machine readable instructions. Additionally or alternatively, the communication interface circuitry210may be instantiated by any other combination of hardware, software, and/or firmware. For example, the communication interface circuitry210may be implemented by at least one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, an XPU, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to execute some or all of the machine readable instructions and/or to perform some or all of the operations corresponding to the machine readable instructions without executing software or firmware, but other structures are likewise appropriate.

While an example manner of implementing the trailer sway control circuitry104ofFIG.1is illustrated inFIG.2, one or more of the elements, processes, and/or devices illustrated inFIG.2may be combined, divided, re-arranged, omitted, eliminated, and/or implemented in any other way. Further, the example sensor interface circuitry202, the example prediction circuitry204, the example control activation circuitry206, the example parameter estimation circuitry208, the example communication interface circuitry210, the example path monitoring circuitry212, the example sensor database214, and/or, more generally, the example trailer sway control circuitry104ofFIG.1, may be implemented by hardware alone or by hardware in combination with software and/or firmware. Thus, for example, any of the example sensor interface circuitry202, the example prediction circuitry204, the example control activation circuitry206, the example parameter estimation circuitry208, the example communication interface circuitry210, the example path monitoring circuitry212, the an example sensor database214, and/or, more generally, the example trailer sway control circuitry104, could be implemented by processor circuitry, analog circuit(s), digital circuit(s), logic circuit(s), programmable processor(s), programmable microcontroller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), and/or field programmable logic device(s) (FPLD(s)) such as Field Programmable Gate Arrays (FPGAs). Further still, the example trailer sway control circuitry104ofFIG.1may include one or more elements, processes, and/or devices in addition to, or instead of, those illustrated inFIG.2, and/or may include more than one of any or all of the illustrated elements, processes and devices.

FIG.3is a block diagram of types of sensor data used by the trailer sway control circuitry104ofFIG.2. Example sensor data types are included in the sensor database214. The sensor database214includes example passenger information data302, example load data304, example hitch data306, example mass estimation data308, example friction estimation data310, example vehicle path data312, example yaw estimation data314, example vehicle speed data316, example turn signal indicator data318, example corner recognition data320, example terrain data322, example wheelbase data324, example steer coefficients data326, and example trailer sideslip data328.

In some examples, the passenger information data302may be representative of a number, location, and/or load (e.g., mass, weight) of passengers in the vehicle100. For example, the passenger information data302may represent two passengers on the left side of the vehicle100generating an approximate load (e.g., three hundred and five pounds). In some examples, the passenger detection sensor108J (e.g., a seat sensor, a weight sensor) may determine the weight of the passengers by measuring the weight of the passengers. In some examples, the passenger detection sensor108J is implemented by a seatbelt sensor. For example, once the seatbelt is locked, the passenger detection sensor108J determines that a passenger is sitting in the seat, and the parameter estimation circuitry208estimates a load (e.g., weight) based on the number of locked seatbelts. In some examples, the driver state camera1080(e.g., a passenger camera) may determine the location of the passengers of the vehicle based on image data. In such examples, the parameter estimation circuitry208may, based on the images of the passengers generated by the driver state camera1080, determine the location of the passengers of the vehicle and generate the passenger information data302. In some examples, an operator of the vehicle100may input the number of passengers and the respective seats of the passengers via the user interface130ofFIG.1. The parameter estimation circuitry208may, based on the input number of passengers and respective seats, determine the passenger information data302by estimating a passenger weight and assigning the estimated passenger weight to a corresponding seat.

The example load data304represents the load (e.g., weight, mass) at ones of the vehicle wheels106and/or the trailer wheels107. The load sensors108A generate the example load data304. The load data304may be used by the trailer sway control circuitry104to determine whether a load shift has occurred. For example, during operation of the vehicle100coupled to the trailer102, a load of the trailer102(e.g., cargo in the trailer102) may move toward a front end of the trailer102. In some examples, the load shift may affect operation of the vehicle100. The trailer sway control circuitry104may determine to apply a torque (e.g., acceleration, deceleration) to ones of the vehicle wheels106and/or ones of the trailer wheels107in response to the load shift. In some examples, the trailer sway control circuitry104may determine a magnitude and direction of the load shift based, partly, on the load data304. In some examples, the communication interface circuitry210communicates the magnitude and direction of the load shift to the operator. An operator of the vehicle100may determine that a small load shift (e.g., less than 10% of towing capability) does not require the operator to slow the vehicle100to a stop, while a large load shift (e.g., greater than 10% of towing capability) requires a stop. In some examples, the towing capability is based on a size of the vehicle100. For example, the trailer sway control circuitry104may determine that a two hundred pound load shift is a large load shift for a first truck, while the two hundred pound load shift is a small load shift for a second truck larger than the first truck. In some examples, towing capability information for the vehicle100may be preloaded in the trailer sway control circuitry104.

The example hitch data306represents the load of the trailer102. For example, the example backup camera108B (e.g., camera in the taillight of the vehicle100, rear view camera) may generate images of the trailer102with respect to the ground (e.g., surface, road). The parameter estimation circuitry208may determine the load (e.g., weight, mass) of the trailer102based on the images generated by the example backup camera108B. The parameter estimation circuitry208may determine, based on the height of the trailer102in the image captured by the example backup camera108B, the load of the trailer102. In some examples, the parameter estimation circuitry208determines a roll angle or a pitch angle of the trailer102relative to the ground (e.g., surface, road). The parameter estimation circuitry208retrieves images captured by the example backup camera108B to estimate the load of the trailer102. In some examples, the hitch data306is used to determine the tongue load (e.g., stable tongue load, light tongue load, heavy tongue load) as the load of the trailer102is compared to the load experienced by the tongue112.

The mass estimation data308represents the mass of the vehicle100and the trailer102. For example, the parameter estimation circuitry208executes mass estimation techniques based on comparing the acceleration of the vehicle100and/or the trailer102with the torque application to determine a combined mass of the vehicle100and the trailer102. For example, a wheel torque sensor108P determines the amount of torque (e.g., force) based on the energy provided by from the axle or the electric motor to the vehicle wheels106and/or the trailer wheels107. The longitudinal acceleration sensor108G and/or the lateral acceleration sensor108H determines the acceleration of the vehicle100and/or the trailer102. The parameter estimation circuitry208based on the example torque applied and the example acceleration estimates the example mass of the vehicle100and/or the trailer102. In some examples, the trailer102is not coupled to the vehicle100, such that the example mass estimation algorithm determines the mass of the vehicle100based on the torque applied at the vehicle wheels106and the acceleration of the vehicle100. In some examples, one or more other methods (e.g., a user input, etc.) may be used to estimate the mass of the vehicle100and/or the trailer102.

The example friction estimation data310represents frictional forces applied to respective ones of the vehicle wheels106A,106B,106C,106D. The friction sensor108M determines the terrain (e.g., surface, road, ground) and the available wheel grip that corresponds to the terrain. For example, the friction sensor108M may detect the type of terrain (e.g., asphalt, gravel, rocky, woodland, etc.) based on an image captured by a camera (e.g., the backup camera108B, the front camera108L). The parameter estimation circuitry208estimates the frictional forces of ones of the vehicle wheels106and/or the trailer wheels107based on the type of detected terrain. In some examples, the parameter estimation circuitry208estimates the frictional forces based on the emergency control systems (e.g., ESC, TCS, ABS). For example, the parameter estimation circuitry208may use emergency control based friction estimates. In such examples, the emergency control systems friction estimates are generated when the vehicle wheels106and/or the trailer wheels107are sliding and/or in a non-linear response range. In some examples, the parameter estimation circuitry208uses one or more artificial intelligence (AI) or machine learning (ML) models to estimate the frictional forces. In particular, the parameter estimation circuitry208can provide vision-based terrain data322(e.g., generated by the backup camera108B or the front camera108L) and/or the emergency control systems frictional estimates as input to the AI/ML model(s). In response to execution of the AI/ML model(s), the parameter estimation circuitry208outputs the friction estimation data310. In some examples, the friction sensor108M is to determine the frictional force based on a road condition (e.g., snow, ice, mud, and/or wet conditions) detected in the vision-based terrain data322. For example, when the vehicle100is driven in inclement weather conditions (e.g., a rainstorm, a snowstorm, etc.), the parameter estimation circuitry208determines that a frictional coefficient between the road and the vehicle wheels106is reduced compared to when the vehicle100is driven in sunny, dry conditions.

The vehicle path data312may represent path data (e.g., monitoring data) corresponding to events happening on the road (e.g., surface, path) ahead. For example, the vehicle path data312may be generated based on images captured by the front camera108L and/or based on data from the Global Positioning System (GPS)108Q of the vehicle100. For example, the front camera108L captures images of a projected path of the vehicle100, and the path monitoring circuitry212may detect an upcoming road change (e.g., a stop sign, a speed limit, a curve) in the projected path based on any known image recognition techniques. The projected path of the vehicle100may include a first portion of road on which the vehicle100is to travel at a first speed, and a second portion of road on which the vehicle100is to travel at a second speed different from the first speed. For example, the vehicle path data312may determine that the speed limit for the first portion of straight road is fifty miles per hour, while the second portion of road is a curve that has a speed limit of forty miles per hour. In some examples, the vehicle path data312includes a grade (e.g., incline and/or decline) of the road and/or weather conditions. In some examples, vehicle path data312(e.g., Electronic Horizon® data) is used for supplying road network information to a vehicle's on-board computer that can be used to support other vehicle systems, namely, automatic headlight aiming, intersection detection, adaptive cruise control, lane keeping, curve warning, and similar vehicle control measures.

After the path monitoring circuitry212determines upcoming road changes, the prediction circuitry204uses the determined upcoming road changes to predict whether a trailer sway condition is likely to occur. For example, the prediction circuitry204may determine a trailer sway condition is likely to occur by determining the speed of the vehicle100and the tongue load and the radius of the curve. In some examples, other data in the sensor database214in addition to the vehicle path data312is used by the prediction circuitry204to predict whether a trailer sway condition is likely to occur. In some examples, the friction sensor108M includes a surface condition sensor to detect road surface conditions on a road surface (e.g., detect a presence of snow, ice, mud, and/or moisture on the road surface). In such examples, the friction sensor108M determines weather condition data based on the road surface conditions, where the weather condition data indicates whether sunny conditions, rainy conditions, snowy conditions, etc. are present. In some examples, the friction sensor108M updates the vehicle path data312to include the weather condition data. In some examples, the prediction circuitry204determines, based on the vehicle path data312and/or the weather condition data, that a trailer sway condition is more likely to occur. For example, the prediction circuitry204may determine that the trailer sway condition is more likely to occur during snowy conditions compared to during dry and sunny conditions. The control activation circuitry206accelerates or decelerates the vehicle100and the trailer102in response to an indication from the prediction circuitry204that a trailer sway condition is likely to occur. By accelerating or decelerating the vehicle100and the trailer102, the control activation circuitry206is to cause the vehicle100and the trailer102to avoid the likely trailer sway condition. For example, in response to determining that the second portion of road is a curve with a second speed limit, the control activation circuitry206is to apply a torque to the trailer wheels107.

The example vehicle speed data316may be representative of a travel speed of the vehicle100and/or the trailer102. The vehicle speed data316may be generated based on input from an accelerometer (e.g., the longitudinal acceleration sensor108G, the lateral acceleration sensor108H, etc.). In some examples, the prediction circuitry204may use the vehicle speed data316to determine a first speed (e.g., a threshold speed) at which that the vehicle100and the trailer102are traveling. The example prediction circuitry204uses the first speed (e.g., threshold speed) to determine a speed threshold that the example prediction circuitry204may use to compare to a second speed. In some examples, when the second speed is different than the first speed (e.g., threshold speed), the example prediction circuitry204may determine that an example trailer sway condition is likely to occur.

The turn signal indicator data318represents when a right turn signal and/or a left turn signal is activated. For example, the turn signal sensor108R determines when a turn signal is activated. In some examples, the trailer sway control circuitry104uses the prediction circuitry204to predict whether the vehicle100is to turn right or left based on the turn signal indicator data318. As such, the turn signal indicator data318can be used to predict actions, maneuvers, and/or turns to be executed by the vehicle100.

The corner recognition data320identifies one or more corners (e.g., curves) in a projected path of the vehicle100. For example, the prediction circuitry204ofFIG.2generates the corner recognition data320based on images captured by the example front camera108L ofFIG.1and/or based on GPS data from the GPS108Q ofFIG.1.

The terrain data322represents the road surface conditions. In some examples, the backup camera108B and/or the front camera108L generates the terrain data322indicating whether the road surface has snow, ice, rain, mud thereon and/or whether the road surface is dry. In some examples, the parameter estimation circuitry208executes an artificial intelligence or machine learning model based on the terrain data322to estimate the friction estimation data310.

The trailer wheelbase data324represents relative positions (e.g., locations) of the trailer wheels107. In particular, the trailer wheelbase data324represents a distance between the pivot point on the tongue112and the trailer wheels107on the trailer102. In some examples, the trailer sway control circuitry104generates the trailer wheelbase data324based on input from the operator of the vehicle100into the communication interface circuitry210. In some examples, the trailer sway control circuitry104generates, with the parameter estimation circuitry208, the trailer wheelbase data324based on image data of the vehicle wheels106that is generated by the backup camera108B.

The steer coefficients data326includes understeer coefficient(s) and/or oversteer coefficient(s) of the vehicle100. As disclosed herein, an “understeer coefficient” refers to a measure of change between a steering wheel angle of the vehicle100and a trajectory curvature of the vehicle100as a function of the lateral acceleration of the vehicle100. In some examples, the trailer sway control circuitry104generates, with the parameter estimation circuitry208, the steer coefficients data326based on data from the yaw rate sensor108D, the lateral acceleration sensor108H, the hitch data306and/or the GPS108Q. In some examples, the trailer sway control circuitry104uses the steer coefficients data326(e.g., the understeer coefficient(s) and/or the oversteer coefficient(s)) and the wheelbase data324to estimate likelihood of trailer sway.

In some examples, a tongue load percentage is determined based on the hitch data306. In such examples, as the tongue load percentage decreases, the trailer sway control circuitry104determines that the vehicle100and the trailer102tend towards oversteer (e.g., corresponding to a negative understeer coefficient), which reduces a critical speed of the vehicle100and the trailer102. In particular, a negative tongue load tends to reduce the critical speed of the vehicle100and the trailer102. In some examples, the prediction circuitry204determines a speed at which a trailer sway is likely to occur (e.g., a critical speed) based on the steer coefficients data326and the wheelbase data324. In some examples, the control activation circuitry206reduces a travel speed of the vehicle100and the trailer102to a speed significantly slower than the critical speed.

In some examples, the trailer sway control circuitry104generates the trailer sideslip data328based on a trailer hitch angle measurement determined based on image data from the vehicle backup camera108B. In some examples, the trailer sway control circuitry104uses the trailer sideslip data328to determine whether a trailer sway condition is likely to occur.

In some examples, the example trailer sway control circuitry104uses other combinations of data included in the example sensor database214to predict vehicle instabilities.

FIG.4Ais a side view of the trailer102and the vehicle100ofFIG.1having a first load distribution. In this example, the trailer102is coupled to the vehicle100by via the tongue112(e.g., a hitch). Sensor data from the load sensor108A may be used to determine the load402(e.g., weight, mass) in the trailer102. During operation of the vehicle100and the trailer102, the load402may shift in the trailer102. Based on the position of the weight (e.g., the load402, a load shift), the strain or stress experienced by the tongue112may change, while the total weight of the vehicle100and the trailer102remains unchanged. A lighter tongue load causes that the rear vehicle wheels106C,106D to have less normal force and, as a result, less traction. Additionally, the lighter tongue load may result in the trailer102being more prone to sway.

In the example ofFIG.4A, the weight of the vehicle100is six thousand pounds, the weight of the trailer102and the load402of the trailer102is ten thousand pounds, and the combined weight of the vehicle100and the trailer102is sixteen thousand pounds. In this example, the trailer sway control circuitry104may determine that there is one occupant (e.g., operator, driver) based on the passenger information data302. In some examples, the passenger information data302is generated using the passenger detection sensor108J (e.g., a weight sensor in the seat, seatbelt information), the driver state camera1080, or the communication interface circuitry210(e.g., the driver inputs into a display that there is only one occupant).

For example, the trailer sway control circuitry104may determine that the weight of the vehicle100is based on vehicle configuration information, occupant information, the load data304generated by the load sensors108A, the hitch data306, and the mass estimation techniques. For example, the trailer sway control circuitry104may determine that the weight of the trailer102is based on the load data304, the hitch data306, and the mass estimation techniques. In some examples, the combined weight of the vehicle100and the trailer102is determined from the mass estimation techniques.

In the example ofFIG.4A, the trailer sway control circuitry104determines that the load402in the trailer102is shifted towards the rear of the trailer102. For example, the trailer sway control circuitry104may determine, based on the position of the load402, that the stress or strain on the tongue112is light (e.g., less than a stable threshold, 8%, less than 10%, etc.). In response to the light tongue determination, the trailer sway control circuitry104may increase a sensitivity setting (e.g., increase a control authority to a trailer sway control system). In some examples, in response to the light tongue determination, the trailer sway control circuitry104may bias additional deceleration torque from the rear axle of the vehicle100(e.g., from the rear vehicle brakes116C,116D) to the trailer102(e.g., to the trailer brakes117). For example, based on a torque request (e.g., a braking request), the trailer sway control circuitry104may use either the rear vehicle brakes116C,116D, the trailer brakes117, or a combination of the rear vehicle brakes116C,116D and the trailer brakes117to supply the desired total torque in the torque request. In examples in which a combination of the rear vehicle brakes116C,116D and the trailer brakes117are used, the trailer sway control circuitry104may use more torque from the trailer brakes117than the rear vehicle brakes116C,116D. For example, the trailer sway control circuitry104may cause seventy-five percent of the deceleration torque of the rear vehicle brakes116C,116D to be used and twenty-five percent of the deceleration torque of the trailer brakes117to be used. As a result, the trailer sway control circuitry104biases the torque from the rear vehicle brakes116C,116D to the trailer brakes117. In some such examples, the trailer sway control circuitry104does not cause a change in torque from the front vehicle brakes116A,116B.

The trailer sway control circuitry104(e.g., the prediction circuitry204) may determine an upcoming path change (e.g., speed change, upcoming curve, upcoming turn etc.) and apply additional deceleration torque to the trailer102before the applying deceleration torque to the vehicle100. In some examples, the trailer sway control circuitry104applies trailer brakes117during an Electronic Stability Control (ESC) activation process in response to a predicted trailer sway condition.

FIG.4Bis a side view of the trailer102and the vehicle100ofFIG.1having a second load distribution. The load404is positioned near the front of the trailer102, resulting in a heavy tongue load. The heavy tongue load may indicate that there is a smaller downward force on the front vehicle wheels106A,106B compared to the rear vehicle wheels106C,106D. The reduced downward force on the front wheels106A,106B results in reduced steering effectiveness of the vehicle100. The reduced downward force on the front wheels106A,106B also results in a reduction in torque capacity (e.g., braking capacity) of the front axle118A of the vehicle100. The trailer sway control circuitry104may determine, from the load data304, the hitch data306, and the mass estimation data308, that there is a heavy tongue load (e.g., a stress or strain greater than 15%). The trailer sway control circuitry104may determine to reduce sensitivity and decrease control authority to of a trailer sway control system. In some examples, reducing a sensitivity to a trailer sway control system avoids nuisance unnecessary activation thereof. In some examples, the trailer sway control circuitry104may bias additional acceleration torque and deceleration torque to the vehicle rear axle. In some examples, the trailer sway control circuitry104may bias understeer control torque to the vehicle rear axle118B.

In the example ofFIG.4A, the trailer sway control circuitry104may, in response to determining that the tongue load is less than the first tongue load threshold (e.g., a light tongue load), increase a sensitivity setting of the trailer sway control circuitry104and decelerate at least one of the one or more trailer wheels107. In the example ofFIG.4B, the trailer sway control circuitry104may, in response to determining that the tongue load is greater than the second tongue load threshold (e.g., a heavy tongue load), decrease a sensitivity setting to the trailer sway control circuitry104and decelerate a rear vehicle wheel106C,106D.

FIG.5is an illustration of the vehicle100and the trailer102ofFIG.1driving on a road that includes a curve. In this example, the vehicle100and trailer102are traveling on an example first road segment502at a cruise control speed. The path monitoring circuitry212determines that there is an example second road segment504and an example third road segment506following the first road segment502. The path monitoring circuitry212determines that the first road segment502includes an example curve510based on images captured by the front camera108L. The path monitoring circuitry212generates the vehicle path data312that identifies the upcoming curve510. In some examples, the path monitoring circuitry212continuously and/or periodically generates the vehicle path data312. During travel of the vehicle100and the trailer102along the road, the prediction circuitry204of the trailer sway control circuitry104monitors for a likelihood of trailer sway conditions. In some examples, the path monitoring circuitry212determines road segments (e.g., the first road segment502) based on a suggested and/or planned path determined by a navigation system and/or an autonomous driving system of the vehicle100. For example, the path monitoring circuitry212retrieves the suggested path from the autonomous driving system implemented by an on-board computer of the vehicle100, and determines the road segments, curves, and/or turns based on the suggested path.

In the example ofFIG.5, an example load508(e.g., cargo) is evenly distributed in the trailer102(e.g., does not result in a heavy tongue load or a light tongue load). Thus, the prediction circuitry204determines, based on sensor data (e.g., the load data304), that the loads experienced at respective ones of the vehicle wheels106and the trailer wheels107satisfy (e.g., are within) a predetermined threshold (e.g., each of the vehicle wheels106and trailer wheels107are within the predetermined threshold). In other examples, when the load (e.g., normal force) experienced at one of the vehicle wheels106or one of the trailer wheels107were to exceed the predetermined threshold, the prediction circuitry204determines that a trailer sway condition is likely to occur. In response to the prediction circuitry204predicting that a trailer sway condition is likely to occur, the control activation circuitry206activates at least one vehicle control to prevent the trailer sway condition from occurring.

In the example ofFIG.5, the prediction circuitry204may, based on the vehicle path data312generated by the path monitoring circuitry212, determine that the upcoming curve510in the first road segment502is likely to result in a trailer sway condition. The prediction circuitry204may then determine that a speed change is required to avoid the likely trailer sway condition based on the upcoming curve510in the first road segment502. In some examples, the prediction circuitry204first determines whether a reduction in speed will avoid the predicted likely trailer sway condition. Further, the prediction circuitry204determines whether to reduce the speed using the trailer brakes117(e.g., regenerative trailer braking) or whether to reduce the speed using both the vehicle brakes116and the trailer brakes117. In some examples, the prediction circuitry204uses friction estimation data310to determine whether the trailer brakes117are sufficient to reduce the speed or if the vehicle brakes116in combination with the trailer brakes117are to reduce the speed. In the example ofFIG.5, the control activation circuitry206instructs the trailer system to activate the trailer brakes117and/or the vehicle brakes116based on the determination by the prediction circuitry204.

In the example ofFIG.5, the path monitoring circuitry212determines that the second road segment504includes an example turn512. In some examples, the prediction circuitry204determines there is a likely trailer sway condition based on the speed of the vehicle100and a radius of the turn512. In some examples, to prevent the likely trailer sway condition, the prediction circuitry204determines that the vehicle brakes116and the trailer brakes117are to be activated to slow the vehicle100and the trailer102to less than a threshold speed to execute the turn512.

In some examples, the prediction circuitry204determines that the vehicle brakes116and/or the trailer brakes117are to be activated to reduce the speed of the vehicle100and the trailer102based on an estimated friction coefficient between the vehicle wheels106and the road determined by the friction sensor108M. In some examples, the friction sensor108M estimates the friction coefficient based on detected road conditions (e.g., whether the road is dry, wet, etc.). Further, the prediction circuitry204detects a radius of an upcoming corner or turn. In some examples, based on the friction coefficient and the detected radius, the prediction circuitry204reduces the speed of the vehicle100and the trailer102(e.g., by activating the vehicle brakes116and/or the trailer brakes117) to slow the vehicle100down to less than a threshold speed. In particular, the threshold speed is selected such that a lateral acceleration of the vehicle100and the trailer102is less than a threshold lateral acceleration when travelling along the turn. In some examples, the threshold lateral acceleration corresponds to a percentage (e.g., 50%) of the friction coefficient multiplied by a gravitational constant g.

In some examples, if the friction sensor108M determines that the road is snow-covered, the prediction circuitry204determines that the friction coefficient between the road and the vehicle wheels106is 0.25. In such examples, the prediction circuitry204determines a speed for the upcoming corner or turn that results in a lateral acceleration that is less than or equal to the threshold lateral acceleration. For example, if the friction coefficient is 0.25, the prediction circuitry204selects a speed such that the lateral acceleration of the vehicle100and the trailer102is less than or equal to 0.125 multiplied by the gravitational constant g. In such examples, reducing the vehicle speed improves driver comfort and/or reduces slippage of the vehicle wheels106when the vehicle100and the trailer102travel along the turn512.

In some examples, the prediction circuitry204is to distinguish (e.g., differentiate) between a curve (e.g., where the front camera108L is able to detect additional road segments that lie beyond the curve) and a turn (e.g., where the front camera108L is prevented from detecting additional road segments that lie beyond the turn). In some examples, the difference between a curve and a turn is based on a stub angle determined from image data generated by the front camera108L.

In the example ofFIG.5, the path monitoring circuitry212determines that the third road segment506is a straight path. The prediction circuitry204determines that likelihood of a trailer sway condition is low (e.g., less than a condition threshold). In such examples, the prediction circuitry204instructs the vehicle acceleration113to accelerate the vehicle100(e.g., speed up the vehicle100) and the trailer102coupled to the vehicle100for the third road segment506after the turn in the second road segment504. In some examples, the vehicle100and trailer102is accelerated back to a cruise control speed.

FIG.6is a table representative of trailer sway likelihood determination. For example, the table600may numerically represent a likelihood of a trailer sway condition occurring. The table600includes an example vehicle weight column602, an example trailer weight column604, an example estimated center of gravity (CG)606, an example tongue load column608, and an example trailer sway likelihood score column610. In some examples, the prediction circuitry204is to determine the likelihood of a trailer sway condition occurring based on the vehicle weight, the trailer weight, an estimated center of gravity, an estimated tongue load (e.g., determined from the hitch data306), the wheelbase data324, the steer coefficients data326, and/or the trailer sideslip data328. In the example ofFIG.6, a first row612of the table600lists represents the vehicle100(e.g., a first vehicle) with a weight of five thousand pounds and a trailer102with a weight of ten thousand pounds. In this example, the first row also indicates that the vehicle100has a low center of gravity and a tongue load of twenty percent. Further, in the example of the first row612, the trailer sway likelihood score is a four out of ten.

In some examples, in response to the trailer sway likelihood score being over a score threshold, the trailer sway control circuitry104may activate safety controls using the control activation circuitry206. Some examples of vehicle safety controls include applying a torque (e.g., an acceleration or a deceleration) to the trailer102. In some examples, the trailer sway control circuitry104may instruct the operator of the vehicle to pull over and resecure the load. Resecuring the load may alter the tongue load percentage and lower the trailer sway likelihood score.

For example, in response to the trailer sway likelihood score being between zero and three, the trailer sway control circuitry104may determine that the trailer sway likelihood score is not above the score threshold and, thus, does not activate a safety control. In some examples, at a trailer sway likelihood score of four, the trailer sway control circuitry104is to increase trailer braking. In some examples, at a trailer sway likelihood score of five, the trailer sway control circuitry104is to activate (e.g., pre-charge) the TCS, ESC, RSC, and TSC systems and increase sensitivity thereof. In some examples, at a trailer sway likelihood score of six, the trailer sway control circuitry104is to warn a driver of the vehicle100to adjust the load in the trailer102. In some examples, at a trailer sway likelihood score of seven, the trailer sway control circuitry104is to reduce propulsion torque (e.g., forward torque) of the vehicle100. In some examples, at a trailer sway likelihood score of eight, the trailer sway control circuitry104is to reduce the maximum speed to less than or equal to a first threshold speed (e.g., eighty kilometers per hour, forty nine miles per hour, etc.). In some examples, at a trailer sway likelihood score of nine, the trailer sway control circuitry104is to reduce the maximum speed to less than or equal to a second threshold speed (e.g., thirty kilometers per hour, eighteen miles per hour, etc.) less than the first threshold speed. In some examples, at a trailer sway likelihood score of ten, the trailer sway control circuitry104is to reduce the maximum speed to less than or equal to a third threshold speed (e.g., five kilometers per hour, three miles per hour, etc.) less than the first threshold speed and the second threshold speed. In some examples, the trailer sway control circuitry104executes the safety controls corresponding to a first trailer sway likelihood score at a second drive trailer sway likelihood score, where the second trailer sway likelihood score is greater than the first trailer sway likelihood score. For example, at a trailer sway likelihood score of nine, the trailer sway control circuitry104can execute safety controls corresponding to a trailer sway likelihood score of seven (e.g., reducing the propulsion torque) in addition to safety controls corresponding to the trailer sway likelihood score of nine (e.g., reducing the maximum vehicle speed to approximately thirty kilometers per hour).

The other rows of the table600illustrate other trailer sway likelihood scores. For example, the second row614illustrates that the load in the trailer102refers to a stable tongue load (e.g., fifteen percent) and results in a trailer sway likelihood score of two. For example, the third row616illustrates that the load in the trailer102refers to a slightly light tongue load (e.g., below ten percent) and results in a trailer sway likelihood score of seven. For example, the fourth row618illustrates that the trailer102is not coupled to the vehicle100which results in a trailer sway likelihood score of one. For example, the fifth row620illustrates that the load in the trailer102refers to a very light tongue load (e.g., five percent or less) and results in a trailer sway likelihood score of ten. As used herein, a lower trailer sway likelihood score indicates a reduced probability of a trailer sway condition occurring (e.g., a trailer sway condition with a trailer sway likelihood score of one is less likely to occur than a trailer sway condition with a trailer sway likelihood score of two).

Flowcharts representative of example machine readable instructions, which may be executed to configure processor circuitry to implement the trailer sway control circuitry104ofFIG.2, are shown inFIGS.7-9. The machine readable instructions may be one or more executable programs or portion(s) of an executable program for execution by processor circuitry, such as the processor circuitry1012shown in the example processor platform1000discussed below in connection withFIG.10and/or the example processor circuitry discussed below in connection withFIGS.11and/or12. The program may be embodied in software stored on one or more non-transitory computer readable storage media such as a compact disk (CD), a floppy disk, a hard disk drive (HDD), a solid-state drive (SSD), a digital versatile disk (DVD), a Blu-ray disk, a volatile memory (e.g., Random Access Memory (RAM) of any type, etc.), or a non-volatile memory (e.g., electrically erasable programmable read-only memory (EEPROM), FLASH memory, an HDD, an SSD, etc.) associated with processor circuitry located in one or more hardware devices, but the entire program and/or parts thereof could alternatively be executed by one or more hardware devices other than the processor circuitry and/or embodied in firmware or dedicated hardware. The machine readable instructions may be distributed across multiple hardware devices and/or executed by two or more hardware devices (e.g., a server and a client hardware device). For example, the client hardware device may be implemented by an endpoint client hardware device (e.g., a hardware device associated with a user) or an intermediate client hardware device (e.g., a radio access network (RAN)) gateway that may facilitate communication between a server and an endpoint client hardware device). Similarly, the non-transitory computer readable storage media may include one or more mediums located in one or more hardware devices. Further, although the example program is described with reference to the flowcharts illustrated inFIGS.7-9, many other methods of implementing the example trailer sway control circuitry104may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., processor circuitry, discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. The processor circuitry may be distributed in different network locations and/or local to one or more hardware devices (e.g., a single-core processor (e.g., a single core central processor unit (CPU)), a multi-core processor (e.g., a multi-core CPU, an XPU, etc.) in a single machine, multiple processors distributed across multiple servers of a server rack, multiple processors distributed across one or more server racks, a CPU and/or a FPGA located in the same package (e.g., the same integrated circuit (IC) package or in two or more separate housings, etc.).

The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data or a data structure (e.g., as portions of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers) located at the same or different locations of a network or collection of networks (e.g., in the cloud, in edge devices, etc.). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc., in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and/or stored on separate computing devices, wherein the parts when decrypted, decompressed, and/or combined form a set of machine executable instructions that implement one or more operations that may together form a program such as that described herein.

In another example, the machine readable instructions may be stored in a state in which they may be read by processor circuitry, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc., in order to execute the machine readable instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, machine readable media, as used herein, may include machine readable instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s) when stored or otherwise at rest or in transit.

The machine readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine readable instructions may be represented using any of the following languages: C, C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example operations ofFIGS.7-9may be implemented using executable instructions (e.g., computer and/or machine readable instructions) stored on one or more non-transitory computer and/or machine readable media such as optical storage devices, magnetic storage devices, an HDD, a flash memory, a read-only memory (ROM), a CD, a DVD, a cache, a RAM of any type, a register, and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the terms non-transitory computer readable medium, non-transitory computer readable storage medium, non-transitory machine readable medium, and non-transitory machine readable storage medium are expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, the terms “computer readable storage device” and “machine readable storage device” are defined to include any physical (mechanical and/or electrical) structure to store information, but to exclude propagating signals and to exclude transmission media. Examples of computer readable storage devices and machine readable storage devices include random access memory of any type, read only memory of any type, solid state memory, flash memory, optical discs, magnetic disks, disk drives, and/or redundant array of independent disks (RAID) systems. As used herein, the term “device” refers to physical structure such as mechanical and/or electrical equipment, hardware, and/or circuitry that may or may not be configured by computer readable instructions, machine readable instructions, etc., and/or manufactured to execute computer readable instructions, machine readable instructions, etc.

“Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc., may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, or (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, or (3) at least one A and at least one B.

As used herein, singular references (e.g., “a,” “an”, “first”, “second”, etc.) do not exclude a plurality. The term “a” or “an” object, as used herein, refers to one or more of that object. The terms “a” (or “an”), “one or more,” and “at least one” are used interchangeably herein. Furthermore, although individually listed, a plurality of means, elements or method actions may be implemented by, e.g., the same entity or object. Additionally, although individual features may be included in different examples or claims, these may possibly be combined, and the inclusion in different examples or claims does not imply that a combination of features is not feasible and/or advantageous.

FIG.7is a flowchart representative of example machine readable instructions and/or example operations700that may be executed and/or instantiated by processor circuitry to implement the example trailer sway control circuitry104ofFIG.2to determine whether a speed change will decrease likelihood of trailer sway. The machine readable instructions and/or operations700ofFIG.7begin at block702, at which the example trailer sway control circuitry104obtains sensor data from one or more of the vehicle sensors108and/or one or more of the trailer sensors110ofFIG.1. For example, the example sensor interface circuitry202ofFIG.2obtains and/or otherwise receives the sensor data from the vehicle sensors108and/or the trailer sensors110. In some examples, the sensor data includes passenger information data302(e.g., a number of passengers and the seats that are occupied by the number of passengers), load data304(e.g., a load at the vehicle wheels106and/or the trailer wheels107, a load at the tongue112, etc.), hitch data306(e.g., a position of the tongue112relative to the trailer102and the vehicle100, the ride height of the trailer102relative to the vehicle100), mass estimation data308(e.g., a combined weight of the vehicle100and the weight of the trailer102), friction estimation data310(e.g., a frictional force of the vehicle wheels106and/or trailer wheels107relative to the ground), vehicle path data312(e.g., upcoming turns, speed changes, inclines, declines in a vehicle path), yaw estimation data314(e.g., left/right acceleration of the vehicle100), vehicle speed data316, turn signal indicator data318(e.g., determining a predicted turn based on an indication from the turn signal), corner recognition data320(e.g., based on image data), terrain data322(e.g., road conditions such as muddy, wet, snowy, dry, etc.), wheelbase data324(e.g., the relative positions of the vehicle wheels106), steer coefficients data326(e.g., understeer coefficients and/or oversteer coefficients of the vehicle100), and/or trailer sideslip data328(e.g., whether the trailer102is likely to slip to a lateral side).

At block704, the trailer sway control circuitry104predicts behavior of the vehicle100and/or the trailer102. For example, the prediction circuitry204ofFIG.2predicts, based on the sensor data, a likelihood of trailer sway occurring. For example, the prediction circuitry204predicts the likelihood of trailer sway occurring by calculating a trailer sway likelihood score (e.g., as shown in the table600ofFIG.6) based on a vehicle weight, a trailer weight, a center of gravity estimate, and a tongue load. The tongue load may be a determination such as light tongue load, stable tongue load, or heavy tongue load based on the percentage of the tongue load relative to trailer weight. For example, a tongue load that is less than ten percent of the trailer weight is a light tongue load, while a tongue load that is more than fifteen percent of the trailer weight is a heavy tongue load. In some examples, the prediction circuitry204predicts the likelihood trailer sway of the vehicle100by comparing a manufacturer recommended speed with a speed measured by the wheel speed sensor(s)108I.

At block706, the trailer sway control circuitry104determines if a speed change is required for the vehicle100. For example, the prediction circuitry204may determine whether, based on the sensor data and a predicted trailer sway, a speed change of the vehicle100is required to avoid the likely trailer sway. For example, the vehicle100may be traveling on a road which has a first curve. The prediction circuitry204determines that based on the upcoming curve, the current speed, and the distribution of the load of the trailer102and the load on the tongue112that there is a likely trailer sway condition. The prediction circuitry204determines a speed change (e.g., reducing the speed, applying a negative torque) will avoid the likely trailer sway condition. For example, in response to the prediction circuitry204determining a speed change is not required (e.g., block706returns a result of NO), control flows to block714. Alternatively, in response to the prediction circuitry204determining a speed change is required (e.g., block706returns a result of YES), control flows to block708.

At block708, the prediction circuitry204determines whether a desired speed of the vehicle100can be achieved using the trailer brakes117alone. For example, the prediction circuitry204may determine a required torque to achieve a desired speed of the vehicle100and the trailer102based on friction estimation data310. The friction estimation data310is used to estimate the frictional forces of ones of the vehicle wheels106and/or trailer wheels107based on the type of detected terrain. The prediction circuitry204uses the friction estimation data310generated from the parameter estimation circuitry208to determine whether the trailer brakes117(e.g., regenerative braking) will achieve the desired speed of the vehicle100and the trailer102or whether the vehicle brakes116are also needed to achieve the desired speed of the vehicle100and the trailer102. For example, in response to the prediction circuitry204determining that the desired speed can be achieved using only regenerative brakes of the trailer102(e.g., block708returns a result of YES), control flows to block712. For example, in response to the prediction circuitry204determining that the desired speed cannot be achieved using only the regenerative brakes (e.g., block708returns a result of NO), control flows to block710.

At block710, the trailer sway control circuitry104applies a combination of trailer regenerative braking and vehicle braking. For example, the control activation circuitry206may instruct a braking system connected to the trailer102(e.g., the trailer brakes117) to apply a first decelerating torque and a braking system connected to the vehicle100(e.g., the vehicle brakes116) to apply a second decelerating torque. The combination of the first decelerating torque and the second decelerating torque will change the speed of the vehicle100and the trailer102. In some examples, the first decelerating torque to reduce the speed of the trailer102is applied before the second decelerating torque to reduce the speed of the vehicle100. The order in applying the different decelerating torques may reduce likelihood of a trailer sway condition. Control flows to block714.

At block712, the trailer sway control circuitry104applies trailer regenerative braking. For example, the control activation circuitry206may instruct a braking system connected to the trailer102(e.g., the trailer brakes117ofFIG.1) to apply a decelerating torque to the trailer wheels107A,107B. The decelerating torque may be applied using the trailer brakes117. In some examples, a speed change may be caused by an electric motor connected to the trailer wheels107A,107B. Control flows to block714.

At block714, the trailer sway control circuitry104determines to continue monitoring the behavior of the vehicle100and/or the trailer102. In some examples, the vehicle100has completed the trip and is powered off, so the trailer sway control circuitry104determines to not continue monitoring the behavior of the vehicle100and/or the trailer102when the vehicle100completes a trip and/or is powered off. For example, in response to the trailer sway control circuitry104determining to continue monitoring (e.g., block714returns a result of YES), control returns to block702. For example, in response to the trailer sway control circuitry104determining to not continue monitoring (e.g., block714returns a result of NO), control ends.

FIG.8is a flowchart representative of example machine readable instructions and/or example operations800that may be executed and/or instantiated by processor circuitry to implement the example trailer sway control circuitry104ofFIG.2to monitor the load at the wheels of the vehicle and the trailer to prevent a trailer sway. The machine readable instructions and/or operations800ofFIG.8begin at block802, at which the trailer sway control circuitry104obtains sensor data from one or more of the vehicle sensors108and/or one or more of the trailer sensors110ofFIG.1. For example, the sensor interface circuitry202ofFIG.2obtains and/or otherwise receives the sensor data from the vehicle sensors108and/or the trailer sensors110. In some examples, the sensor data includes passenger information data302(e.g., a number of passengers and the seats that are occupied by the number of passengers), load data304(e.g., a load at the vehicle wheels106and/or the trailer wheels107, a load at the tongue112, etc.), hitch data306(e.g., a position of the tongue112relative to the trailer102and the vehicle100, the ride height of the trailer102relative to the vehicle100), mass estimation data308(e.g., estimating the combined weight of the vehicle100and the weight of the trailer102), friction estimation data310(e.g., representing a frictional force of the vehicle wheels106and/or trailer wheels107relative to the ground), vehicle path data312(e.g., representing upcoming turns, speed changes, inclines, declines based on image data), yaw estimation data314(e.g., representing left/right acceleration of the vehicle100), vehicle speed data316, turn signal indicator data318(e.g., indicating a predicted turn based on an indication from the turn signal), and corner recognition data320(e.g., based on image data).

At block804, the trailer sway control circuitry104estimates the load at ones of the vehicle wheels106and the trailer wheels107. For example, the parameter estimation circuitry208determines, based on the sensor data, determine a load at each of the vehicle wheels106of the vehicle100(e.g., vehicle wheels106) and trailer wheels107of the trailer102(e.g., trailer wheels107). In some examples, the loads at the trailer wheels107are measured using the load sensor108A.

At block806, the trailer sway control circuitry104determines if the load satisfies a manufacturer recommendation. For example, a manufacturer recommendation for a load may be stored in the sensor database214. In some examples, the parameter estimation circuitry208compares the estimated loads at each of the vehicle wheels106and/or the trailer wheels107to the respective manufacturer recommendations. In some examples, the parameter estimation circuitry208determines that the estimated load meets the manufacturer recommendations when a difference between the estimated load and a manufacturer-recommended load at a particular wheel is less than a threshold difference. In response to the parameter estimation circuitry208determining that an estimated load for at least one wheel does not satisfy manufacturer recommendations (e.g., block806returns a result of NO), control flows to block808. In response to the parameter estimation circuitry208determining that the estimated load does satisfy manufacturer recommendations (e.g., block806returns a result of YES), control flows to block812. The prediction circuitry204may determine if the load meets manufacturer recommendations while the vehicle100and the trailer102are stationary or moving. In some examples, before the vehicle100has started moving and while the vehicle100is stationary, if the load does not meet manufacturer recommendation, the prediction circuitry204instructs the control activation circuitry206to lock the vehicle wheels106of the vehicle100to prevent movement. In some examples, while the vehicle100is moving, if the load does not satisfy manufacturer recommendations, the control activation circuitry206applies a negative torque to slow down and/or stop the vehicle so that an operator of the vehicle100may re-adjust the load.

At block808, the trailer sway control circuitry104locks the vehicle100in park. For example, the control activation circuitry206may control a torque on the vehicle wheels106and the trailer wheels107through the vehicle brakes116and the trailer brakes117to lock the vehicle100in park.

At block810, the trailer sway control circuitry104provides an indication via the user interface130ofFIG.1(e.g., HMI). For example, the communication interface circuitry210ofFIG.2causes the user interface130to display the indication, where the indication can indicate a trailer sway condition is likely to occur. Additionally or alternatively, the example indication can include operator instructions to the operator of the vehicle100, where the operator instructions instruct the operator to increase a speed of the vehicle100, reduce the speed of the vehicle100, pull the vehicle100to a side of the road, and/or resecure a load in the trailer102.

At block811, the trailer sway control circuitry104operates the vehicle100in a reduced-speed mode. The trailer sway control circuitry104engages the reduced-speed mode in response to the communication interface circuitry210retrieving an acknowledgement from the operator of the vehicle100. For example, the operator of the vehicle100, in response to the trailer sway condition indication presented by the communication interface circuitry210at block810, may acknowledge, via user input to the communication interface circuitry210, receipt of the trailer sway condition indication. The reduced-speed mode may correspond to a walking speed (e.g., 5 kph or less) or a speed that is predetermined during vehicle development to facilitate re-loading and/or re-calculation of loading while the vehicle100and the trailer102are on flat, level ground. Control returns to block802.

At block812, the trailer sway control circuitry104continuously monitors the load at the wheels and in the trailer102while an operator is driving the vehicle100. For example, the parameter estimation circuitry208may continue to estimate the load at ones of the vehicle wheels and ones of the trailer wheels while the vehicle100and the trailer102are in motion. In some examples, the parameter estimation circuitry208estimates the load while the motion of the vehicle100and the trailer102has temporarily ceased (e.g., while the vehicle100is at a stoplight).

At block814, the trailer sway control circuitry104determines if a change in the load at one or more wheels changes is greater than a predetermined threshold. For example, the prediction circuitry204may compare a first load experienced at a first vehicle wheel (e.g., vehicle wheel106A) at a first time with a second load experienced at the first vehicle wheel (e.g., vehicle wheel106A) at a second time after the first time. In response to the change in the load experienced at the first vehicle wheel that is greater than the predetermined threshold (e.g., block814returns a result of YES), control flows to block816. In response to the change in the load experienced at the first vehicle wheel not being greater than the predetermined threshold (e.g., block814returns a result of NO), control flows to block818. The predetermined threshold may be based on the table600ofFIG.6and/or manufacturer recommendations for the load at the vehicle wheels106and/or the trailer wheels107.

At block816, the example trailer sway control circuitry104activates at least one vehicle control. For example, the control activation circuitry206may activate a torque system to accelerate or decelerate at least one trailer wheel107A,107B (e.g., trailer brakes117or an electric motor115). In some examples, the activated vehicle control includes pre-charging the braking system, warning the operator of the vehicle100, instructing the operator of the vehicle100to navigate to a location to adjust or resecure a load in the trailer102, limit a speed of the vehicle100, biasing acceleration to at least one of the vehicle front wheels106A,106B, the rear vehicle wheels106C,106D, and the trailer wheels107A,107B. In some examples, activation of the vehicle controls avoids activation of one or more emergency control systems of the vehicle100. In some examples, the emergency control systems include anti-lock brakes (e.g., ABS), traction control (e.g., TCS), Electronic stability control (e.g., ESC), rollover stability control (e.g., RSC™).

At block818, the trailer sway control circuitry104is to determine whether to continue monitoring. For example, in response to the prediction circuitry204determining to continue monitoring the load at the vehicle wheels106and the trailer wheels107(e.g., block818returns a result of YES), control returns to block802. Alternatively, in response to the prediction circuitry204determining not to continue monitoring the load at the vehicle wheels106and the trailer wheels107(e.g., block818returns a result of NO), control ends.

FIG.9is a flowchart representative of example machine readable instructions and/or example operations900that may be executed and/or instantiated by processor circuitry to implement the example trailer sway control circuitry104ofFIG.2to prevent a trailer sway by applying torque to at least one wheel of the vehicle100and/or the trailer102. At block902, the sensor interface circuitry202obtains sensor data. For example, the sensor interface circuitry202may obtain sensor data from the example sensor database214as described in connection with block702ofFIG.7and/or block802ofFIG.8above.

At block904, the trailer sway control circuitry104monitors a load distribution of the vehicle100. For example, the prediction circuitry204monitors the load distribution of the vehicle100based on an estimated load at ones of the vehicle wheels106and/or the trailer wheels107, upcoming path changes (e.g., represented by the vehicle path data312, road changes, slope changes), and tongue measurements.

At block906, the trailer sway control circuitry104predicts whether a trailer sway condition is likely to occur. For example, the prediction circuitry204may predict, based on sensor data from one or more sensors of the vehicle100and/or the trailer102, whether a trailer sway condition is likely to occur. In some examples, the likelihood of a trailer sway condition occurring is determined based on the table600ofFIG.6.

At block908, the trailer sway control circuitry104determines if a trailer sway is likely to occur. The prediction circuitry204determines that, in response to a likelihood of a trailer sway condition being at or above a predetermined threshold, the trailer sway condition is likely to occur. The prediction circuitry204is to instruct the control activation circuitry206to activate at least one vehicle control. For example, in determining that a trailer sway condition is not likely to occur (e.g., block908returns a result of NO), control flows to block914. For example, in determining that a trailer sway condition is likely to occur (e.g., block908returns a result of YES), control flows to block910.

At block910, the trailer sway control circuitry104activates at least one vehicle control of the vehicle100to prevent the predicted trailer sway condition from occurring. For example, the control activation circuitry206may activate a warning system, bias torque to rear vehicle wheels106C,106D, and/or reduce a vehicle speed. In some examples, the control activation circuitry206instructs the communication interface circuitry210to present a message (e.g., a warning) to the operator of the vehicle100. The message may instruct the operator to safely slow the vehicle100and the trailer102to a stop. In some examples, the warning message may instruct the operator to rearrange a load in the trailer102. In some examples, the control activation circuitry206may bias torque to the rear vehicle wheels106C,106D. The increased torque in the rear vehicle wheels106C,106D (and decreased torque in the front vehicle wheels106A,106B, where the torque is adjusted in proportion to the estimated load on each of the vehicle wheels106) improves grip of the vehicle wheels106. In some examples, the control activation circuitry206may bias torque between the front vehicle wheels106A,106B and the rear vehicle wheels106C,106D. In some examples, the control activation circuitry206may bias torque between the first vehicle side wheels106A,106C, and the second vehicle side wheels106B,106D. The control activation circuitry206may bias the torque between the front and rear of the vehicle100and the first and second side of the vehicle100at the same time.

At block912, the trailer sway control circuitry104applies torque to at least one wheel of the vehicle100and/or a trailer102coupled to the vehicle100. For example, the control activation circuitry206instructs a regenerative braking system to apply a decelerating torque to at least one of the trailer wheels107A,107B. For example, the control activation circuitry206may instruct trailer acceleration system120(e.g., an electric motor115A) coupled to at least one trailer wheel107A,107B to apply an accelerating torque to the at least one trailer wheel107A,107B. In some examples, the control activation circuitry206may instruct vehicle acceleration system113(e.g., an electric motor114A) coupled to at least one vehicle wheel106A,106B to apply an accelerating torque to the at least one vehicle wheel106A,106B. In some examples, the control activation circuitry206may instruct the vehicle acceleration system113and/or the trailer acceleration system120to apply increase or decrease a torque currently demanded by the operator of the vehicle100.

At block914, the trailer sway control circuitry104determines whether to continue monitoring. For example, in response to the prediction circuitry204determining to continue monitoring (e.g., block914returns a result of YES), control returns to block902. Alternatively, in response to the control activation circuitry206determining to not continue monitoring (e.g., block914returns a result of NO), control ends.

FIG.10is a block diagram of an example processor platform1000structured to execute and/or instantiate the machine readable instructions and/or the operations ofFIGS.7-9to implement the trailer sway control circuitry104ofFIG.2. The processor platform1000can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, a headset (e.g., an augmented reality (AR) headset, a virtual reality (VR) headset, etc.) or other wearable device, or any other type of computing device.

The processor platform1000of the illustrated example includes processor circuitry1012. The processor circuitry1012of the illustrated example is hardware. For example, the processor circuitry1012can be implemented by one or more integrated circuits, logic circuits, FPGAs, microprocessors, CPUs, GPUs, DSPs, and/or microcontrollers from any desired family or manufacturer. The processor circuitry1012may be implemented by one or more semiconductor based (e.g., silicon based) devices. In this example, the processor circuitry1012implements the sensor interface circuitry202, the example prediction circuitry204, the example control activation circuitry206, the example parameter estimation circuitry208, the example communication interface circuitry210, the example path monitoring circuitry212, and the example sensor database214, and, more generally the example trailer sway control circuitry104.

The processor circuitry1012of the illustrated example includes a local memory1013(e.g., a cache, registers, etc.). The processor circuitry1012of the illustrated example is in communication with a main memory including a volatile memory1014and a non-volatile memory1016by a bus1018. The volatile memory1014may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory (RDRAM®), and/or any other type of RAM device. The non-volatile memory1016may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory1014,1016of the illustrated example is controlled by a memory controller1017.

The processor platform1000of the illustrated example also includes interface circuitry1020. The interface circuitry1020may be implemented by hardware in accordance with any type of interface standard, such as an Ethernet interface, a universal serial bus (USB) interface, a Bluetooth® interface, a near field communication (NFC) interface, a Peripheral Component Interconnect (PCI) interface, and/or a Peripheral Component Interconnect Express (PCIe) interface.

In the illustrated example, one or more input devices1022are connected to the interface circuitry1020. The input device(s)1022permit(s) a user to enter data and/or commands into the processor circuitry1012. The input device(s)1022can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, an isopoint device, and/or a voice recognition system.

One or more output devices1024are also connected to the interface circuitry1020of the illustrated example. The output device(s)1024can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube (CRT) display, an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer, and/or speaker. The interface circuitry1020of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip, and/or graphics processor circuitry such as a GPU.

The interface circuitry1020of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) by a network1026. The communication can be by, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, an optical connection, etc.

The processor platform1000of the illustrated example also includes one or more mass storage devices1028to store software and/or data. Examples of such mass storage devices1028include magnetic storage devices, optical storage devices, floppy disk drives, HDDs, CDs, Blu-ray disk drives, redundant array of independent disks (RAID) systems, solid state storage devices such as flash memory devices and/or SSDs, and DVD drives.

The machine readable instructions1032, which may be implemented by the machine readable instructions ofFIGS.7-9, may be stored in the mass storage device1028, in the volatile memory1014, in the non-volatile memory1016, and/or on a removable non-transitory computer readable storage medium such as a CD or DVD.

FIG.11is a block diagram of an example implementation of the processor circuitry1012ofFIG.10. In this example, the processor circuitry1012ofFIG.10is implemented by a microprocessor1100. For example, the microprocessor1100may be a general purpose microprocessor (e.g., general purpose microprocessor circuitry). The microprocessor1100executes some or all of the machine readable instructions of the flowcharts ofFIGS.7-9to effectively instantiate the circuitry ofFIG.2[er diagram] as logic circuits to perform the operations corresponding to those machine readable instructions. In some such examples, the circuitry ofFIG.2[er diagram] is instantiated by the hardware circuits of the microprocessor1100in combination with the instructions. For example, the microprocessor1100may be implemented by multi-core hardware circuitry such as a CPU, a DSP, a GPU, an XPU, etc. Although it may include any number of example cores1102(e.g., 1 core), the microprocessor1100of this example is a multi-core semiconductor device including N cores. The cores1102of the microprocessor1100may operate independently or may cooperate to execute machine readable instructions. For example, machine code corresponding to a firmware program, an embedded software program, or a software program may be executed by one of the cores1102or may be executed by multiple ones of the cores1102at the same or different times. In some examples, the machine code corresponding to the firmware program, the embedded software program, or the software program is split into threads and executed in parallel by two or more of the cores1102. The software program may correspond to a portion or all of the machine readable instructions and/or operations represented by the flowcharts ofFIGS.7-9.

The cores1102may communicate by a first example bus1104. In some examples, the first bus1104may be implemented by a communication bus to effectuate communication associated with one(s) of the cores1102. For example, the first bus1104may be implemented by at least one of an Inter-Integrated Circuit (I2C) bus, a Serial Peripheral Interface (SPI) bus, a PCI bus, or a PCIe bus. Additionally or alternatively, the first bus1104may be implemented by any other type of computing or electrical bus. The cores1102may obtain data, instructions, and/or signals from one or more external devices by example interface circuitry1106. The cores1102may output data, instructions, and/or signals to the one or more external devices by the interface circuitry1106. Although the cores1102of this example include example local memory1120(e.g., Level 1 (L1) cache that may be split into an L1 data cache and an L1 instruction cache), the microprocessor1100also includes example shared memory1110that may be shared by the cores (e.g., Level 2 (L2 cache)) for high-speed access to data and/or instructions. Data and/or instructions may be transferred (e.g., shared) by writing to and/or reading from the shared memory1110. The local memory1120of each of the cores1102and the shared memory1110may be part of a hierarchy of storage devices including multiple levels of cache memory and the main memory (e.g., the main memory1014,1016ofFIG.10). Typically, higher levels of memory in the hierarchy exhibit lower access time and have smaller storage capacity than lower levels of memory. Changes in the various levels of the cache hierarchy are managed (e.g., coordinated) by a cache coherency policy.

Each core1102may be referred to as a CPU, DSP, GPU, etc., or any other type of hardware circuitry. Each core1102includes control unit circuitry1114, arithmetic and logic (AL) circuitry (sometimes referred to as an ALU)1116, a plurality of registers1118, the local memory1120, and a second example bus1122. Other structures may be present. For example, each core1102may include vector unit circuitry, single instruction multiple data (SIMD) unit circuitry, load/store unit (LSU) circuitry, branch/jump unit circuitry, floating-point unit (FPU) circuitry, etc. The control unit circuitry1114includes semiconductor-based circuits structured to control (e.g., coordinate) data movement within the corresponding core1102. The AL circuitry1116includes semiconductor-based circuits structured to perform one or more mathematic and/or logic operations on the data within the corresponding core1102. The AL circuitry1116of some examples performs integer based operations. In other examples, the AL circuitry1116also performs floating point operations. In yet other examples, the AL circuitry1116may include first AL circuitry that performs integer based operations and second AL circuitry that performs floating point operations. In some examples, the AL circuitry1116may be referred to as an Arithmetic Logic Unit (ALU). The registers1118are semiconductor-based structures to store data and/or instructions such as results of one or more of the operations performed by the AL circuitry1116of the corresponding core1102. For example, the registers1118may include vector register(s), SIMD register(s), general purpose register(s), flag register(s), segment register(s), machine specific register(s), instruction pointer register(s), control register(s), debug register(s), memory management register(s), machine check register(s), etc. The registers1118may be arranged in a bank as shown inFIG.11. Alternatively, the registers1118may be organized in any other arrangement, format, or structure including distributed throughout the core1102to shorten access time. The second bus1122may be implemented by at least one of an I2C bus, a SPI bus, a PCI bus, or a PCIe bus

Each core1102and/or, more generally, the microprocessor1100may include additional and/or alternate structures to those shown and described above. For example, one or more clock circuits, one or more power supplies, one or more power gates, one or more cache home agents (CHAs), one or more converged/common mesh stops (CMSs), one or more shifters (e.g., barrel shifter(s)) and/or other circuitry may be present. The microprocessor1100is a semiconductor device fabricated to include many transistors interconnected to implement the structures described above in one or more integrated circuits (ICs) contained in one or more packages. The processor circuitry may include and/or cooperate with one or more accelerators. In some examples, accelerators are implemented by logic circuitry to perform certain tasks more quickly and/or efficiently than can be done by a general purpose processor. Examples of accelerators include ASICs and FPGAs such as those discussed herein. A GPU or other programmable device can also be an accelerator. Accelerators may be on-board the processor circuitry, in the same chip package as the processor circuitry and/or in one or more separate packages from the processor circuitry.

FIG.12is a block diagram of another example implementation of the processor circuitry1012ofFIG.10. In this example, the processor circuitry1012is implemented by FPGA circuitry1200. For example, the FPGA circuitry1200may be implemented by an FPGA. The FPGA circuitry1200can be used, for example, to perform operations that could otherwise be performed by the example microprocessor1100ofFIG.11executing corresponding machine readable instructions. However, once configured, the FPGA circuitry1200instantiates the machine readable instructions in hardware and, thus, can often execute the operations faster than they could be performed by a general purpose microprocessor executing the corresponding software.

More specifically, in contrast to the microprocessor1100ofFIG.11described above (which is a general purpose device that may be programmed to execute some or all of the machine readable instructions represented by the flowcharts ofFIGS.7-9but whose interconnections and logic circuitry are fixed once fabricated), the FPGA circuitry1200of the example ofFIG.12includes interconnections and logic circuitry that may be configured and/or interconnected in different ways after fabrication to instantiate, for example, some or all of the machine readable instructions represented by the flowcharts ofFIGS.7-9. In particular, the FPGA circuitry1200may be thought of as an array of logic gates, interconnections, and switches. The switches can be programmed to change how the logic gates are interconnected by the interconnections, effectively forming one or more dedicated logic circuits (unless and until the FPGA circuitry1200is reprogrammed). The configured logic circuits enable the logic gates to cooperate in different ways to perform different operations on data received by input circuitry. Those operations may correspond to some or all of the software represented by the flowcharts ofFIGS.7-9. As such, the FPGA circuitry1200may be structured to effectively instantiate some or all of the machine readable instructions of the flowcharts ofFIGS.7-9as dedicated logic circuits to perform the operations corresponding to those software instructions in a dedicated manner analogous to an ASIC. Therefore, the FPGA circuitry1200may perform the operations corresponding to the some or all of the machine readable instructions ofFIGS.7-9faster than the general purpose microprocessor can execute the same.

In the example ofFIG.12, the FPGA circuitry1200is structured to be programmed (and/or reprogrammed one or more times) by an end user by a hardware description language (HDL) such as Verilog. The FPGA circuitry1200ofFIG.6, includes example input/output (I/O) circuitry1202to obtain and/or output data to/from example configuration circuitry1204and/or external hardware1206. For example, the configuration circuitry1204may be implemented by interface circuitry that may obtain machine readable instructions to configure the FPGA circuitry1200, or portion(s) thereof. In some such examples, the configuration circuitry1204may obtain the machine readable instructions from a user, a machine (e.g., hardware circuitry (e.g., programmed, or dedicated circuitry) that may implement an Artificial Intelligence/Machine Learning (AI/ML) model to generate the instructions), etc. In some examples, the external hardware1206may be implemented by external hardware circuitry. For example, the external hardware1206may be implemented by the microprocessor1100ofFIG.11. The FPGA circuitry1200also includes an array of example logic gate circuitry1208, a plurality of example configurable interconnections1210, and example storage circuitry1212. The logic gate circuitry1208and the configurable interconnections1210are configurable to instantiate one or more operations that may correspond to at least some of the machine readable instructions ofFIGS.7-9and/or other desired operations. The logic gate circuitry1208shown inFIG.12is fabricated in groups or blocks. Each block includes semiconductor-based electrical structures that may be configured into logic circuits. In some examples, the electrical structures include logic gates (e.g., And gates, Or gates, Nor gates, etc.) that provide basic building blocks for logic circuits. Electrically controllable switches (e.g., transistors) are present within each of the logic gate circuitry1208to enable configuration of the electrical structures and/or the logic gates to form circuits to perform desired operations. The logic gate circuitry1208may include other electrical structures such as look-up tables (LUTs), registers (e.g., flip-flops or latches), multiplexers, etc.

The configurable interconnections1210of the illustrated example are conductive pathways, traces, vias, or the like that may include electrically controllable switches (e.g., transistors) whose state can be changed by programming (e.g., using an HDL instruction language) to activate or deactivate one or more connections between one or more of the logic gate circuitry1208to program desired logic circuits.

The storage circuitry1212of the illustrated example is structured to store result(s) of the one or more of the operations performed by corresponding logic gates. The storage circuitry1212may be implemented by registers or the like. In the illustrated example, the storage circuitry1212is distributed amongst the logic gate circuitry1208to facilitate access and increase execution speed.

The example FPGA circuitry1200ofFIG.12also includes example Dedicated Operations Circuitry1214. In this example, the Dedicated Operations Circuitry1214includes special purpose circuitry1216that may be invoked to implement commonly used functions to avoid the need to program those functions in the field. Examples of such special purpose circuitry1216include memory (e.g., DRAM) controller circuitry, PCIe controller circuitry, clock circuitry, transceiver circuitry, memory, and multiplier-accumulator circuitry. Other types of special purpose circuitry may be present. In some examples, the FPGA circuitry1200may also include example general purpose programmable circuitry1218such as an example CPU1220and/or an example DSP1222. Other general purpose programmable circuitry1218may additionally or alternatively be present such as a GPU, an XPU, etc., that can be programmed to perform other operations.

AlthoughFIGS.11and12illustrate two example implementations of the processor circuitry1012ofFIG.10, many other approaches are contemplated. For example, as mentioned above, modern FPGA circuitry may include an on-board CPU, such as one or more of the example CPU1220ofFIG.12. Therefore, the processor circuitry1012of FIG.10may additionally be implemented by combining the example microprocessor1100ofFIG.11and the example FPGA circuitry1200ofFIG.12. In some such hybrid examples, a first portion of the machine readable instructions represented by the flowcharts ofFIGS.7-9may be executed by one or more of the cores1102ofFIG.11, a second portion of the machine readable instructions represented by the flowcharts ofFIGS.7-9may be executed by the FPGA circuitry1200ofFIG.12, and/or a third portion of the machine readable instructions represented by the flowcharts ofFIGS.7-9may be executed by an ASIC. It should be understood that some or all of the circuitry ofFIG.2may, thus, be instantiated at the same or different times. Some or all of the circuitry may be instantiated, for example, in one or more threads executing concurrently and/or in series. Moreover, in some examples, some or all of the circuitry ofFIG.2may be implemented within one or more virtual machines and/or containers executing on the microprocessor.

In some examples, the processor circuitry1012ofFIG.10may be in one or more packages. For example, the microprocessor1100ofFIG.11and/or the FPGA circuitry1200ofFIG.12may be in one or more packages. In some examples, an XPU may be implemented by the processor circuitry1012ofFIG.10, which may be in one or more packages. For example, the XPU may include a CPU in one package, a DSP in another package, a GPU in yet another package, and an FPGA in still yet another package.

From the foregoing, it will be appreciated that example systems, methods, apparatus, and articles of manufacture have been disclosed that predict and prevent a trailer sway based on sensor data. Disclosed systems, methods, apparatus, and articles of manufacture improve the safety of a vehicle by activating a torque on a wheel of trailer coupled to the vehicle. Disclosed systems, methods, apparatus, and articles of manufacture improve the efficiency of a computing device by using various sensor data to predict when a trailer sway is likely to occur, which prevents or reduces activation of emergency vehicle controls which saves electricity. Disclosed systems, methods, apparatus, and articles of manufacture are accordingly directed to one or more improvement(s) in the operation of a machine such as a computer or other electronic and/or mechanical device.

Example 1 includes an apparatus to control trailer sway of a trailer coupled to a vehicle, the apparatus comprising prediction circuitry to predict, based on sensor data from one or more sensors of the vehicle, whether a trailer sway condition associated with the vehicle is likely to occur, and control activation circuitry to, in response to a prediction that the trailer sway condition is likely to occur, activate at least one vehicle control of the vehicle to prevent the trailer sway condition from occurring, the at least one vehicle control to include applying torque to at least one of one or more trailer wheels of the trailer or one or more vehicle wheels of the vehicle.

Example 2 includes the apparatus of example 1, wherein the sensor data includes load data, and further including parameter estimation circuitry to estimate, based on the load data, a load distribution on at least one of the one or more vehicle wheels or the one or more trailer wheels, and the prediction circuitry to predict the trailer sway condition based on the load distribution.

Example 3 includes the apparatus of example 2, wherein the parameter estimation circuitry is to determine the load distribution of the trailer in response to detection of a curve in a projected path of the vehicle.

Example 4 includes the apparatus of example 3, wherein the control activation circuitry is to adjust, based on historical data, a speed of the vehicle prior to the vehicle entering the curve, wherein to adjust the speed of the vehicle includes at least one of accelerating or decelerating the trailer.

Example 5 includes the apparatus of example 2, wherein the parameter estimation circuitry is to estimate frictional forces based on a detected road condition, and emergency control based friction estimates, the emergency control based friction estimates determined when at least one of the one or more vehicle wheels is in a non-linear response range.

Example 6 includes the apparatus of example 2, wherein the parameter estimation circuitry is to determine a tongue load on a vehicle tongue, the vehicle tongue to couple the vehicle to the trailer, wherein the control activation circuitry is to in response to the tongue load being less than or equal to a first tongue load threshold, adjust the at least one vehicle control based on a first setting, in response to the tongue load being between the first tongue load threshold and a second tongue load threshold, adjust the at least one vehicle control based on a second setting, and in response to the tongue load being greater than or equal to the second tongue load threshold, adjust the at least one vehicle control based on a third setting different from the first setting and the second setting.

Example 7 includes the apparatus of example 6, wherein in response to the parameter estimation circuitry determining that the tongue load is less than the first tongue load threshold, the control activation circuitry is to (i) increase a sensitivity setting of a trailer sway control system, and (ii) increase a deceleration torque setting to at least one of the one or more trailer wheels.

Example 8 includes the apparatus of example 6, wherein in response to the parameter estimation circuitry determining that the tongue load is greater than the second tongue load threshold, the control activation circuitry is to (i) decrease a sensitivity setting of a trailer sway control system, and (ii) increase a deceleration torque of a rear wheel of the one or more vehicle wheels.

Example 9 includes the apparatus of example 1, wherein the control activation circuitry is to determine a likelihood of the trailer sway condition based on vehicle weight, trailer weight, center of gravity, and tongue load data, and determine a torque output for at least one of the one or more vehicle wheels or the one or more trailer wheels.

Example 10 includes the apparatus of example 1, wherein the control activation circuitry applies a negative torque to at least one of the one or more trailer wheels of the trailer and applies a positive torque on at least one of the one or more vehicle wheels of the vehicle to prevent trailer sway.

Example 11 includes a method for controlling trailer sway of a trailer coupled to a vehicle, the method comprising predicting, based on sensor data from one or more sensors of the vehicle, whether a trailer sway condition associated with the vehicle is likely to occur, and in response to a prediction that the trailer sway condition is likely to occur, activating at least one vehicle control of the vehicle to prevent the trailer sway condition from occurring, the at least one vehicle control to include applying torque to at least one of one or more trailer wheels of the trailer or one or more vehicle wheels of the vehicle.

Example 12 includes the method of example 11, further including estimating, based on load data, a load distribution on at least one of the one or more vehicle wheels or the one or more trailer wheels, and predicting the trailer sway condition based on the load distribution.

Example 13 includes the method of example 12, further including adjusting, based on historical data, a speed of the vehicle prior to the vehicle entering a curve, wherein adjusting the speed of the vehicle includes at least one of accelerating or decelerating the trailer.

Example 14 includes the method of example 12, further including determining a tongue load on a vehicle tongue, the vehicle tongue to couple the vehicle to the trailer, in response to the tongue load being less than or equal to a first tongue load threshold, adjusting the at least one vehicle control based on a first setting, in response to the tongue load being between the first tongue load threshold and a second tongue load threshold, adjust the at least one vehicle control based on a second setting, and in response to the tongue load being greater than or equal to the second tongue load threshold, adjusting the at least one vehicle control based on a third setting different from the first setting and the second setting.

Example 15 includes the method of example 14, further including, in response to determining that the tongue load is less than the first tongue load threshold, (i) increasing a sensitivity setting of a trailer sway control system, and (ii) decelerating at least one of the one or more trailer wheels.

Example 16 includes the method of example 14, further including, in response to determining that the tongue load is greater than the second tongue load threshold, (i) decreasing a sensitivity setting of a trailer sway control system, and (ii) decelerating a rear wheel of the one or more vehicle wheels.

Example 17 includes an apparatus to control trailer sway of a trailer coupled to a vehicle, the apparatus comprising at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to predict, based on sensor data from one or more sensors of the vehicle, whether a trailer sway condition associated with the vehicle is likely to occur, and in response to a prediction that the trailer sway condition is likely to occur, activate at least one vehicle control of the vehicle to prevent the trailer sway condition from occurring, the vehicle control of the vehicle to include applying torque to at least one of one or more trailer wheels of the trailer or one or more vehicle wheels of the vehicle.

Example 18 includes the apparatus of example 17, wherein the instructions are further to cause the processor circuitry to estimate, based on load data, a load distribution on at least one of the one or more vehicle wheels or the one or more trailer wheels, and predict the trailer sway condition based on the load distribution.

Example 19 includes the apparatus of example 18, wherein the instructions are further to cause the processor circuitry to determine the load distribution of the trailer in response to detection of a curve in a projected path of the vehicle.

Example 20 includes the apparatus of example 19, wherein the instructions are further to cause the processor circuitry to adjust, based on historical data, a speed of the vehicle prior to the vehicle entering the curve, wherein to adjust the speed of the vehicle includes at least one of accelerating or decelerating the trailer.

The following claims are hereby incorporated into this Detailed Description by this reference. Although certain example systems, methods, apparatus, and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all systems, methods, apparatus, and articles of manufacture fairly falling within the scope of the claims of this patent.