COMPUTER-IMPLEMENTED METHOD FOR DETECTING A VEHICLE FRONT OR REAR MISALIGNMENT USING A VEHICLE SENSOR

Abstract

The examples described here provide a method for detecting a vehicle's front or rear misalignment using a vehicle sensor. The method includes determining whether the vehicle is in a misalignment detection state. In response to determining that the vehicle is in a misalignment detection state, the method further includes determining whether the vehicle is experiencing a misalignment. In response to determining that the vehicle is experiencing a misalignment, the method further includes classifying the misalignment as either a front misalignment or a rear misalignment by comparing a vehicle heading angle with a vehicle movement angle, the vehicle movement angle being determined using sensor data received from the vehicle sensor.The procedure also includes performing an alignment mitigation action to reduce the negative effects of the misalignment on the vehicle.

Description

BACKGROUND

[0001] The present disclosure relates to vehicles and in particular to the detection of a vehicle front or rear misalignment using vehicle sensors.

[0002] German patent application DE 10 2014 117 926 A1 discloses a computer-implemented method according to the preamble of claim 1. German patent application EP 2 315 690 B1 describes a related method.

[0003] Modern vehicles (e.g., a passenger car, motorcycle, boat, or other type of motor vehicle) may be equipped with one or more cameras that provide reversing assistance, capture images of the driver to determine driver drowsiness or attentiveness, provide images of the road while the vehicle is in motion for collision avoidance purposes, provide structure detection (e.g., road signs, etc.), and / or the like. For example, a vehicle may be equipped with multiple cameras, and images from several cameras (referred to as "surround view" cameras) may be used to create an "environmental" or "bird's-eye view" of the vehicle. Some of the cameras (referred to as "long-range cameras") may be used to capture images at long range (e.g., for object detection for collision avoidance, structure detection, etc.).

[0004] Such vehicles may also be equipped with sensors such as one or more radio-based detection and distance measurement devices (RADAR devices), one or more LiDAR devices, and / or similar devices for perception tasks. LiDAR involves using light (e.g., a pulsed laser) to measure the distance to objects by emitting laser pulses, detecting a reflection (e.g., from an object) of the emitted laser pulse, and measuring the time between emission and detection. The measured time can be used to determine the distance between the LiDAR device and the detected object. Perception tasks may include object detection, classification, tracking, lane detection, traffic sign recognition, and / or obstacle avoidance.Perception tasks are particularly useful for an autonomous or semi-autonomous vehicle, providing it with real-time awareness of its surroundings to make safe and informed driving decisions. Images from the vehicle's one or more cameras can also be used for object detection, target tracking, and / or similar tasks, including combinations and / or multiple functions.

[0005] In one embodiment of the invention, a method for detecting a vehicle front or rear misalignment using a vehicle sensor is provided. The method includes determining whether the vehicle is in a misalignment detection state. The method further includes, in response to determining that the vehicle is in a misalignment detection state, determining whether the vehicle is experiencing a misalignment. The method further includes, in response to determining that the vehicle is experiencing a misalignment, classifying the misalignment as a front misalignment or a rear misalignment by comparing a vehicle heading angle with a vehicle movement angle, wherein the vehicle movement angle is determined using sensor data received from the vehicle sensor.The procedure also includes performing an alignment mitigation action to reduce the negative effects of the misalignment on the vehicle.

[0006] The misalignment is classified as front misalignment in response to the vehicle heading angle being within a threshold difference of the vehicle movement angle.

[0007] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include taking no action in response to the determination that the vehicle is not in the misalignment detection state.

[0008] The misalignment is classified as rear misalignment in response to the vehicle heading angle not being within a threshold difference of the vehicle movement angle.

[0009] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include the fact that, in response to the vehicle's speed being greater than a threshold, wherein the vehicle's lateral acceleration is essentially zero, and in response to the vehicle traveling straight ahead, the vehicle is in the misalignment detection state.

[0010] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include determining that, in response to a steering wheel angle of the vehicle's steering wheel being greater than a threshold value, the vehicle experiences the misalignment.

[0011] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include the threshold being essentially zero.

[0012] The vehicle uses an active rear-wheel steering system.

[0013] Performing the alignment mitigation action to mitigate the negative effects of the misalignment on the vehicle includes implementing an active rear steering correction action in response to classifying the misalignment as the rear misalignment.

[0014] In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include implementing the active rear steering aid action by measuring the steering angle during straight-ahead driving, calculating a target rear wheel angle offset during straight-ahead driving, and applying the target rear wheel angle offset to the active rear steering system using the sensor data collected by the vehicle sensor.

[0015] In a further, non-independently claimed embodiment, a vehicle is provided. The vehicle includes a vehicle sensor, an active rear-wheel steering system, and a processing system. The processing system includes a working memory containing computer-readable instructions and a processing device for executing the computer-readable instructions, wherein the computer-readable instructions control the processing device to perform operations for detecting a vehicle front or rear misalignment using the vehicle sensor. The operations include determining whether the vehicle is in a misalignment detection state. The operations further include, in response to the determination that the vehicle is in a misalignment detection state, determining whether the vehicle is experiencing a misalignment.The operations further include, in response to the determination that the vehicle is experiencing misalignment, classifying the misalignment as either frontal or rearal by comparing a vehicle heading angle with a vehicle movement angle, the vehicle movement angle being determined using sensor data received from the vehicle sensor. The operations further include causing the active rear steering system to perform an alignment mitigation action to reduce the adverse effects of the misalignment on the vehicle.

[0016] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the misalignment being classified as front misalignment in response to the vehicle heading angle being within a threshold difference of the vehicle movement angle.

[0017] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that no action is taken in response to the determination that the vehicle is not in the misalignment detection state.

[0018] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the fact that, in response to the vehicle heading angle not being within a threshold difference of the vehicle movement angle, the misalignment is classified as the rear misalignment.

[0019] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the fact that, in response to the vehicle's speed exceeding a threshold, wherein the vehicle's lateral acceleration is essentially zero, and in response to the vehicle traveling straight ahead, the vehicle is in the misalignment detection state.

[0020] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include determining that, in response to a steering angle of the vehicle's steering wheel being greater than a threshold, the vehicle experiences misalignment, the threshold being essentially zero.

[0021] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the implementation of an active rear steering correction action in response to classifying the misalignment as the rear misalignment, in order to mitigate the negative effects of the misalignment on the vehicle.

[0022] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the implementation of the active rear steering aid action comprising measuring the steering angle during straight-ahead driving, calculating a target rear wheel angle offset during straight-ahead driving, and applying the target rear wheel angle offset to the active rear steering system using the sensor data collected by the vehicle sensor.

[0023] In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include the vehicle sensor being a camera.

[0024] In a further, non-independently claimed embodiment, a computer program product is provided. The computer program product includes a computer-readable storage medium containing program instructions embodied therein, wherein the program instructions are executable by at least one processor to cause the at least one processor to perform operations for detecting a vehicle front or rear misalignment using a vehicle sensor of the vehicle. The operations include determining whether the vehicle is in a misalignment detection state. The operations further include, in response to determining that the vehicle is in a misalignment detection state, determining whether the vehicle is experiencing a misalignment.The operations further include, in response to the determination that the vehicle is experiencing the misalignment, classifying the misalignment as either a front misalignment or a rear misalignment by comparing a vehicle heading angle with a vehicle movement angle, the vehicle movement angle being determined using sensor data received from the vehicle sensor. The operations further include causing an active rear steering system of the vehicle to perform an alignment mitigation action to reduce the adverse effects of the misalignment on the vehicle.

[0025] The features and advantages described above, and further features and advantages of the disclosure, will become apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Further features, advantages and details appear only as examples in the following detailed description, which refers to the drawings; they show: Fig. 1 an illustration of a vehicle comprising a processing system for detecting a vehicle front or rear misalignment using vehicle sensors according to one or more embodiments; Fig. 2 a block diagram of the processing system of Fig. 1 for detecting a vehicle front or rear misalignment using vehicle sensors according to one or more embodiments; Fig. 3 a diagram showing a front misalignment and a rear misalignment of a vehicle according to one or more embodiments; Fig. 4 a diagram comparing a vehicle movement angle and a vehicle heading angle for a vehicle according to one or more embodiments; Fig. 5 a flowchart of a method for detecting a vehicle front or rear misalignment using vehicle sensors according to one or more embodiments; Fig. 6. A flowchart of a method for detecting a vehicle front or rear misalignment using vehicle sensors according to one or more embodiments and Fig. 7 a block diagram of a processing system for implementing one or more embodiments described herein. DETAILED DESCRIPTION

[0027] The following description is for illustrative purposes only. It should be understood that throughout the drawings, corresponding reference numerals denote similar or corresponding sections and features. As used here, the term "module" refers to a processing circuit arrangement that may include an application-specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or grouped) with memory executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that provide the described functionality.

[0028] One or more embodiments described here relate to detecting a vehicle front or rear misalignment using vehicle sensors.

[0029] Vehicles can use advanced driver assistance systems (ADAS) to enhance vehicle performance and driving comfort by providing automation, adaptation, or enhancement of vehicle systems to provide better awareness, decision-making, and control.

[0030] An example of an ADAS is an adaptive cruise control system (ACC system), which automatically adjusts the speed of a host vehicle to maintain a safe following distance from another vehicle ahead. Another example of an ADAS is an automated lane change system (ALC system), which causes the host vehicle to change lanes. Another example of an ADAS is a forward collision warning system (FCA system), which warns the operator of the host vehicle of a potential head-on collision. Another example of an ADAS is a collision threat braking system (CIB system), which brakes the host vehicle to reduce its speed. Another example of an ADAS is an automated evasive steering system (AES system), which adjusts the trajectory of the host vehicle.

[0031] ADAS often use data (referred to as "sensor data") from sensors (e.g., radar sensors, LiDAR sensor, proximity sensors, etc.), images from cameras, and / or the like, which may include combinations and / or multiples of these, to make decisions and control one or more aspects of the vehicle.

[0032] One or more of the embodiments described here use vehicle sensors, such as those associated with ADAS, to detect front or rear vehicle misalignment. A vehicle can become misaligned due to various steering faults. For example, if a rear suspension component of a vehicle is damaged while the vehicle is in motion, the resulting rear suspension misalignment will cause "dog-hunting" behavior, in which the vehicle's heading angle (referred to as a "vehicle heading angle") differs from the direction in which the vehicle is traveling (referred to as a "vehicle motion angle"). In a severe case, this can be considered a weakening of lateral motion control for both the chassis and active safety systems.One or more of the embodiments described herein address this deficiency by detecting vehicle misalignment, quantifying the severity of the misalignment, and identifying the misalignment as frontal or rearal. The term "misalignment" refers to the incorrect position of one or more wheels of a vehicle relative to the other wheels. Proper wheel alignment ensures that the vehicle travels straight and correctly, maximizing tire life and providing optimal handling and ride quality / comfort. Misalignment can result from normal wear and tear, driving over potholes or curbs, collisions with other vehicles, and / or the like, including combinations and / or multiple causes.

[0033] According to one or more embodiments, the difference between the vehicle heading angle and the vehicle movement angle is determined using standard vehicle sensors, such as those commonly associated with or connected to ADAS systems. One or more embodiments provide effective detection of misalignment and enable a usable action, while mitigating the robustness of the vehicle control system.

[0034] It is acknowledged that the function of a vehicle implementing one or more of the embodiments described herein is improved. For example, detecting and classifying a misalignment (such as a front misalignment or a rear misalignment) improves the vehicle's operation by mitigating negative effects caused by the misalignment. For instance, if a vehicle incorporates an active rear steering system, the active rear steering can be adjusted to account for the misalignment. In particular, the active rear steering can be "reset to zero" by calculating and implementing a target rear wheel angle offset during straight-ahead driving conditions. This reduces or eliminates the undesirable "searching for dogs" behavior caused by a rear misalignment. Further benefits and advantages are also apparent to those skilled in the art.

[0035] Fig. Figure 1 illustrates a vehicle 100 comprising a processing system 102 for detecting a vehicle front or rear misalignment using one or more vehicle sensors 104 according to one or more embodiments. As described herein, misalignment refers to the incorrect position of one or more wheels 106 of the vehicle 100 relative to the other wheels 106 of the vehicle.

[0036] Vehicle 100 can be a passenger car, a truck, a van, a bus, a motorcycle, a boat, or any other type of motor vehicle. According to one embodiment, Vehicle 100 includes an internal combustion engine powered by gasoline, diesel, or the like. According to another embodiment, Vehicle 100 is a hybrid electric vehicle that is partially or fully powered by electricity. According to yet another embodiment, Vehicle 100 is an electric vehicle powered by electricity. According to one or more embodiments, Vehicle 100 is an autonomous or a semi-autonomous vehicle. An autonomous vehicle is a vehicle that has self-driving capabilities. A semi-autonomous vehicle is a vehicle that has certain autonomous features (e.g., self-parking, lane keeping, etc.) but does not have fully autonomous control.

[0037] According to one or more embodiments, the vehicle 100 includes the processing system 102. The processing system 102 can process data (e.g., sensor data 212, which is stored in Fig. 2), which are received by one or more sensors 104, to detect a front or rear misalignment of the vehicle 100. The one or more sensors 104 can be any suitable sensors for collecting data about their environment and transmitting the data to another device, system, cloud-based service, and / or the like, including combinations and / or multiples thereof, such as the processing system 102. Non-limiting examples of the one or more sensors 104 include a camera, a radar device, a LiDAR device, and / or the like, including combinations and / or multiples thereof.

[0038] According to one or more embodiments, the processing system 102 may also include one or more vehicle devices 214 that collect vehicle data 216. In one or more embodiments, the misalignment detection machine 210 uses the vehicle data 216 to detect a vehicle front or rear misalignment. The one or more vehicle devices 214 may include one or more of any suitable device, component, or system that may be contained in or otherwise associated with the vehicle 100.Non-restrictive examples of the one or more vehicle devices 214 include a front control module (FCM) and / or a global positioning system (GPS) and / or a wheel speed sensor (WSS) and / or an inertial measurement unit (IMU) and / or a steering angle sensor (SAS) and / or an active rear steering system (ARS) and / or an electric power steering system (EPS) and / or the like, including combinations and / or multiples thereof. The vehicle data 216 may include data from one or more of the one or more vehicle devices 214 and / or data from another source. For example, the sensor data 212 may include one or more of the following non-restrictive data elements: an indication of road straightness / curvature (e.g.,from a high-resolution map (HD map)), lateral speed from lane markings, heading and curvature information, vehicle heading information, differential odometry information, steering torque conditions, map roll angle (e.g. from an HD map), IMU roll angle and / or the like, which includes combinations and / or several thereof.

[0039] The misalignment detection machine 210 can monitor alignment observer excitation criteria (e.g., zero lateral acceleration with a non-zero steering wheel angle), calculate an expected vehicle course, estimate an alignment error, apply statistical filters (e.g., moving average), and detect a misalignment (e.g., whether a misalignment exists and whether such a misalignment is a front misalignment or a rear misalignment).

[0040] According to one or more embodiments, the vehicle 100 can be equipped with ARS. If the vehicle 100 has any wheel misalignment and is equipped with the ARS system, the ARS system does not need to adjust to a zero value of the steering angle sensor (SAS), which is used to maintain straight-line driving, and will induce a wheel actuator change (RWA change) that could cause an unintended path deviation. Because the ARS system's RWA changes are based on vehicle speed and SAS, the SAS value, in order to maintain a straight path in the case where one or more of the wheels 106 are misaligned, will also change with the vehicle speed. In such cases, it may be necessary for the driver (or the autonomous system) to change the steering angle to maintain straight-line driving when vehicle speeds change.Chassis control systems that use the SAS value to maintain a straight path must also continuously adapt to changing SAS values. If such an adaptation occurs too rapidly, the chassis control system may malfunction. Front steering features such as active feedback also work against driver input due to ARS misalignment behavior. Accordingly, it is desirable to detect misalignment, as described here.

[0041] Further features of the processing system 102 will now be described with reference to Fig. 2 to Fig. 7 described.

[0042] In particular, Fig. 2 a block diagram of the processing system 102 of Fig. 1 for detecting a vehicle front or rear misalignment using one or more vehicle sensors 104 according to one or more embodiments. The processing system 102 comprises a processing device 202, a working memory 204, and a misalignment detection machine 210. It is to be acknowledged that the processing system 102 can be any device suitable for detecting a vehicle front or rear misalignment using one or more vehicle sensors 104. For example, the processing system 102 can be a device implemented in the vehicle 100 or otherwise associated with it. As another example, the processing system 102 can be a smartphone, a tablet computer, a laptop computer, a desktop computer, a portable computing device, and / or the like, including combinations and / or multiples thereof.

[0043] The processing device 202 is any suitable processing circuit arrangement for processing data (e.g., sensor data 212) and / or commands. The processing device 202 is an example of one or more of the processing devices 721 of Fig. 7, as described in more detail here.

[0044] The working memory 204 is any suitable device for storing data and / or instructions. The working memory 204 is an example of the system memory 722 and / or the read / write memory 723 and / or the read-only memory 724 of Fig. 7, as described in more detail here.

[0045] The misalignment detection machine 210 provides cybersecurity for detecting a vehicle front or rear misalignment using one or more vehicle sensors 104, as described in more detail herein. According to one or more embodiments, the misalignment detection machine 210 uses sensor data 212 from the one or more sensors 104 to detect a vehicle front or rear misalignment for the vehicle 100.

[0046] The misalignment detection machine 210 provides real-time and rapid detection of a vehicle's front or rear misalignment during straight-ahead driving conditions using one or more sensors 104, such as one or more cameras associated with an ADAS, to determine a difference between the vehicle's heading angle and its movement angle. If a rear misalignment is present, a threshold difference exists between the vehicle's heading angle and its movement angle while the vehicle 100 is essentially traveling in a straight line, causing the dog-searching behavior described herein. If a front misalignment is present, no threshold difference exists between the vehicle's heading angle and its movement angle while the vehicle 100 is essentially traveling in a straight line.A "threshold difference" is a difference that is greater than a threshold value. For example, when comparing the vehicle heading angle and the vehicle movement angle, a threshold difference can be a difference expressed as a percentage, a total angle amount, or another measure. For example, the threshold difference can be 1%, 1 degree, and / or the like, including combinations and / or multiples thereof. The misalignment detection machine 210 can holistically determine misalignment caused by various factors such as suspension, axle, or steering, without relying on steering information.

[0047] Furthermore, aspects and features of the misalignment detection machine 210 are discussed here in relation to Fig. 3- Fig. 7 described.

[0048] The various components, modules, machines, etc., which with regard to Fig. The machines described in Section 2 (e.g., the misalignment detection machine 210) can be implemented as instructions stored in a computer-readable storage medium, as hardware modules, as special-purpose hardware (e.g., application-specific hardware, application-specific integrated circuits (ASICs), application-specific special-purpose processors (ASSPs), field-programmable gate arrays (FPGAs), as embedded controllers, a hard-wired circuit arrangement, etc.), or as one or more combinations thereof. According to aspects of this disclosure, the one or more machines described herein can be a combination of hardware and programming. The programming can be processor-executable instructions stored in physical working memory, and the hardware can include the processing device 202 for executing these instructions. Thus, a system memory (e.g.,The main memory (204) stores program instructions which, when executed by the processing device (202), implement the machines described herein. Additional machines may also be used to incorporate further features and functionalities, which are described in further examples herein.

[0049] Fig. Figure 3 is a diagram showing a front misalignment 302 and a rear misalignment 304 of the vehicle 100 according to one or more embodiments. In this example, the wheels 106 of the vehicle 100 are shown. In the case of front misalignment 302, wheel 106a is misaligned with respect to the wheels 106. In the case of rear misalignment 304, wheel 106b is misaligned with respect to the wheels 106. To determine whether the vehicle is experiencing front misalignment 302 or rear misalignment 304, the misalignment detection machine 210 compares the vehicle movement angle 310 with the vehicle heading angle 312. The vehicle movement angle 310 is determined using the sensor data 212 received from the one or more vehicle sensors 104. For example, one or more cameras can take pictures of the road and determine the vehicle's movement angle 310 from the images, e.g.by extracting features from multiple frames of a video recorded by the camera and processing the frames to determine the vehicle movement angle 310 with respect to the road on which the vehicle is traveling. If the vehicle movement angle 310 and the vehicle heading angle 312 are in agreement (e.g., within a threshold difference such as 1%, 1 degree, and / or the like, which includes combinations and / or multiples thereof), the vehicle heading angle 312 is considered to be in agreement with or corresponding to the vehicle movement angle 310, which indicates the front misalignment 302. However, if the vehicle movement angle 310 and the vehicle heading angle 312 are not in agreement (e.g., not within the threshold difference), the vehicle heading angle 312 is not considered to be in agreement with or corresponding to the vehicle movement angle 310, which indicates the rear misalignment 304.

[0050] Fig. Figure 4 is a diagram comparing the vehicle movement angle 310 and the vehicle heading angle 312 for the vehicle 100 according to one or more embodiments. In this embodiment, the vehicle movement angle 310 and the vehicle heading angle 312 are considered to be out of agreement (e.g., not within the threshold difference), which indicates the rear misalignment 304.

[0051] Fig. Figure 5 is a flowchart of a method 500 for detecting a vehicle front or rear misalignment using vehicle sensors according to one or more embodiments. The method 500 can be implemented using any suitable system or device. For example, the method 500 can be implemented using the processing system 102 of Fig. 1 and Fig. 2, through the processing system 700 of Fig. 7 and / or the like, which contains combinations and / or several thereof, must be implemented. Procedure 500 is now implemented with reference to Fig. 1 and Fig. The method described in section 2 is not so limited. For example, method 500 is useful for vehicles that do not implement ARS, but is not so limited.

[0052] In block 502, the misalignment detection machine 210, using sensor data 212 from one or more sensors 104 (e.g., sensors associated with one or more ADAS), detects that the vehicle 100 is traveling straight ahead. The misalignment detection machine 210 also determines whether the vehicle 100's speed is greater than a threshold value, whether the vehicle's lateral acceleration is essentially zero, and whether the vehicle 100 is traveling straight ahead (e.g., whether the road on which the vehicle 100 is operating is essentially straight, whether the vehicle's path is essentially straight). The vehicle 100's speed can be determined, for example, by...Determined using telemetry data received from the vehicle's electronic control system, global positioning system (GPS) data, data received from a wheel speed sensor, and / or the like, including combinations and / or multiples thereof. Lateral acceleration, for example, can be determined using data from the vehicle's inertial measurement unit (IMU). Road straightness can be determined using imaging information extracted from sensor data 212 from one or more sensors 104 (e.g., a camera), from map data from the vehicle's navigation system 100, and / or the like, including combinations and / or multiples thereof. If any of these conditions are not met (e.g.,If the vehicle speed is not greater than the threshold, the lateral acceleration is not substantially zero, or the road is not a straight road, procedure 500 proceeds to block 504 without taking any action. However, if each of these three conditions is true, procedure 500 proceeds to block 506.

[0053] In block 506, the misalignment detection machine 210 performs misalignment detection. For example, the misalignment detection machine determines whether the steering wheel angle of the vehicle 100 is below a threshold value. According to one or more embodiments, the threshold value is essentially zero, such that the misalignment detection machine 210 determines whether the steering wheel angle is essentially zero. If the steering wheel angle is less than the threshold value, the misalignment detection machine 210 determines in block 508 that there is no misalignment for the vehicle 100. However, if the steering wheel angle is less than the threshold value in block 506, the method 500 proceeds to block 510.

[0054] In block 510, the misalignment detection machine 210 compares the vehicle movement angle 310 and the vehicle heading angle 312. That is, the misalignment detection machine 210 determines whether the heading angle of vehicle 100 (e.g., the vehicle heading angle 312) differs from the direction in which vehicle 100 is moving (e.g., the vehicle movement angle 310). Using the results of the comparison, the misalignment detection machine 210 determines whether the misalignment is a front misalignment (block 512) or a rear misalignment (block 514). If a front misalignment (block 512) is present, there is no threshold difference between the vehicle heading angle 312 and the vehicle movement angle 310 while vehicle 100 is essentially traveling in a straight line.If a rear misalignment (block 514) is present, there is a threshold difference between the vehicle heading angle 312 and the vehicle movement angle 310, while the vehicle is essentially traveling in a straight line, causing the dog-searching behavior as described here.

[0055] According to one or more embodiments, if the misalignment detection is classified as originating from a front misalignment (block 512) or a rear misalignment (block 514), an alignment mitigation action can be performed to mitigate the negative effects of the misalignment on the vehicle. For example, the vehicle 100 can undergo a realignment of its wheels 106, and one or more systems of the vehicle 100 can implement a corrective action while driving (e.g., limiting a vehicle speed, adjusting a dynamic suspension system, and / or the like, including combinations and / or multiple such actions).

[0056] Additional processes may also be included, and it should be understood that the processes that are in Fig. The processes shown in Figure 5 are for illustrative purposes only, and it is understood that further processes can be added, or existing processes can be removed, modified, or rearranged without deviating from the scope of this disclosure. Furthermore, it should be understood that the processes shown in Figure 5 are for illustrative purposes only and are not intended to be interpreted in this way. Fig. 5 are shown, can be implemented as programming instructions stored in a non-transient, computer-readable storage medium and then, when executed by a processor (e.g., the processing device 202 of Fig. 2, the one or more processors 721 of Fig. 7 and / or the like, which includes combinations and / or several thereof) of a computing system (e.g., of the processing system 102 of Fig. 1 and Fig. 2, of the processing system 700 of Fig. 7 and / or the like, which includes combinations and / or several thereof) are executed, causing the processor to perform the processes described herein.

[0057] Fig. Figure 6 is a flowchart of a method 600 for detecting a vehicle front or rear misalignment using vehicle sensors according to one or more embodiments. The method 600 can be implemented using any suitable system or device. For example, the method 600 can be implemented using the processing system 102 of Fig. 1 and Fig. 2, through the processing system 700 of Fig. 7 and / or the like, which includes combinations and / or multiples thereof, must be implemented. Procedure 600 is now implemented with reference to Fig. 1 and Fig. The method described in section 2 is not so limited. For example, method 600 is useful for vehicles implementing an ARS, but is not so limited.

[0058] In block 602, the misalignment detection machine 210, using sensor data 212 from one or more sensors 104 (e.g., sensors associated with one or more ADAS), detects that the vehicle 100 is traveling straight ahead. The misalignment detection machine 210 also determines whether the vehicle 100's speed is greater than a threshold value, whether the vehicle's lateral acceleration is essentially zero, and whether the vehicle 100 is traveling straight ahead (e.g., whether the road on which the vehicle 100 is operating is essentially straight, whether the vehicle's path is essentially straight). The vehicle 100's speed can be determined, for example, by...The lateral acceleration can be determined using telemetry data received from the vehicle's electronic control system, global positioning system (GPS) data, data received from a wheel speed sensor, and / or the like, including combinations and / or multiples thereof. For example, lateral acceleration can be determined using data from the vehicle's inertial measurement unit (IMU). Road straightness can be determined using imaging information extracted from sensor data 212 from one or more sensors 104 (e.g., a camera), map data from the vehicle's navigation system 100, and / or the like, including combinations and / or multiples thereof. If any of these conditions are not met (e.g.,If the vehicle speed is not greater than the threshold, the lateral acceleration is not substantially zero, or the road is not a straight road, procedure 600 proceeds to block 604, where no action is taken. However, if each of these three conditions is true, procedure 600 proceeds to block 606.

[0059] In block 606, the misalignment detection machine 210 performs misalignment detection. For example, the misalignment detection machine determines whether the steering wheel angle of the vehicle 100's steering wheel is below a threshold value. According to one or more embodiments, the threshold value is essentially zero, such that the misalignment detection machine 210 determines whether the steering wheel angle is essentially zero. If the steering wheel angle is less than the threshold value, the misalignment detection machine 210 determines in block 608 that there is no misalignment for the vehicle 100. However, if in block 606 the steering wheel angle is not less than the threshold value, the method 600 proceeds to block 609.

[0060] In Block 609, Procedure 600 includes performing an ARS-SAS offset attenuation. Specifically, the ARS system adjusts an SAS offset that is used as a new ARS neutral position. To this end, the misalignment detection machine 210 calculates an SAS difference using a measured rear wheel angle and a front steering ratio (e.g., the measured rear wheel angle multiplied by the front steering ratio) as a basis. Next, the misalignment detection machine 210 calculates the SAS offset as a moving average based on the measured steering wheel angle and the SAS difference (e.g., measured steering wheel angle minus SAS difference). Finally, the misalignment detection machine 210 adjusts the ARS system using the SAS offset. In particular, the ARS system requires a SAS of 0 if the steering wheel angle is equal to the SAS offset (e.g., not zero). Procedure 600 then proceeds to block 610.

[0061] In block 610, the misalignment detection machine 210 compares the vehicle heading angle 310 and the vehicle heading angle 312. That is, the misalignment detection machine 210 determines whether the heading angle of vehicle 100 (e.g., the vehicle heading angle 312) differs from the direction in which vehicle 100 is moving (e.g., the vehicle heading angle 310). Using the results of the comparison, the misalignment detection machine 210 determines whether the misalignment is a front misalignment (block 612) or a rear misalignment (block 614). If a front misalignment is present (block 612), there is no threshold difference between the vehicle heading angle 312 and the vehicle heading angle 310 while vehicle 100 is essentially traveling in a straight line.If a rear misalignment is present (block 614), there is a threshold difference between the vehicle heading angle 312 and the vehicle movement angle 310, while the vehicle is essentially traveling in a straight line, causing the dog-searching behavior as described here.

[0062] In response to the detection of a rear misalignment (Block 614), Procedure 600 proceeds to implement an ARS corrective action, which is an example of an alignment mitigation action. Procedure 600 proceeds to Block 616. In Block 616, the misalignment detection machine 210 measures the SAS during straight-ahead driving. In Block 618, the misalignment detection machine 210 calculates a target rear wheel angle offset for SAS = 0 (e.g., indicating an angle of essentially 0 degrees to the SAS) during straight-ahead driving. Specifically, the misalignment detection machine 210 calculates the target rear wheel angle offset based on the steering wheel angle and the front steering ratio (e.g., the steering wheel angle divided by the front steering ratio). The ARS system is then adjusted using the target wheel angle offset. Specifically, the ARS system subtracts the target rear wheel angle offset from the ARS position calculation.In block 620, the misalignment detection machine 210 applies the target rear wheel angle offset to the ARS system (e.g., one of the one or more vehicle devices 214) using the sensor data 214. In block 622, the dog search behavior is attenuated.

[0063] Additional processes may also be included, and it should be understood that the processes that are in Fig. The processes shown in Figure 6 are for illustrative purposes only, and it is understood that further processes can be added, or existing processes can be removed, modified, or rearranged without deviating from the scope of this disclosure. Furthermore, it should be understood that the processes shown in Figure 6 are for illustrative purposes only and are not intended to be interpreted in this way. Fig. 6 are shown, can be implemented as programming instructions that are stored in a non-transient, computer-readable storage medium and then, when executed by a processor (e.g., the processing device 202 of Fig. 2, the one or more processors 721 of Fig. 7 and / or the like, which includes combinations and / or several thereof) of a computing system (e.g., of the processing system 102 of Fig. 1 and Fig. 2, of the processing system 700 of Fig. 7 and / or the like, which includes combinations and / or several thereof) are executed, causing the processor to perform the processes described here.

[0064] It is understood that one or more of the embodiments described here can be implemented in conjunction with any other type of computing environment, whether known now or developed later. For example, Fig.Figure 7 shows a block diagram of a processing system 700 for implementing the techniques described herein. According to one or more embodiments described herein, the processing system 700 is an example of a cloud computing node in a cloud computing environment. In examples, the processing system 700 includes one or more central processing units (also referred to as "processors" or "processing equipment" and / or "processing devices") 721a, 721b, 721c, etc. (collectively or generically referred to as one or more processors 721 and / or one or more processing devices). In aspects of this disclosure, each processor 721 may include a reduced instruction set computer as a microprocessor (RISC microprocessor). Processors 721 are coupled to a system memory 722 and / or various other components by means of a system bus 733.The system memory 722 can contain one or more temporary and / or persistent memory devices, such as a read / write memory (RAM) 723, a read-only memory (ROM) 724, and / or the like, including combinations and / or multiples thereof. The system bus 733 can contain a basic input / output system (BIOS) that controls certain basic functions of the processing system 700.

[0065] Furthermore, an input / output adapter (I / O adapter) 727 and a network adapter 726, which are connected to the system bus 733, are shown. The input / output adapter 727 can be an interface adapter for small computer systems (SCSI adapter) that communicates with a hard disk 735 and / or a storage device 736 or other similar component. The input / output adapter 727, the hard disk 735, and the storage device 736 are collectively referred to here as mass storage 734. The operating system 740 for execution in the processing system 700 can be stored in the mass storage 734. The network adapter 726 connects a system bus 733 to an external network 738, which enables the processing system 700 to communicate with other such systems.

[0066] A display device (e.g., a display monitor) 739 is connected to the system bus 733 via the display adapter 732, which may include a graphics adapter to improve the performance of graphics-intensive applications and a video controller. In one aspect of this disclosure, adapters 726, 727, and / or 732 may be connected to one or more input / output buses that are connected to the system bus 733 by means of an intervening bus bridge (not shown). Suitable input / output buses for connecting peripheral devices such as disk controllers, network adapters, and graphics adapters typically include common protocols such as the Peripheral Component Connection (PCI). Additional input / output devices are shown as being connected to the system bus 733 via the user interface adapter 728 and the display adapter 732.A keyboard 729, a mouse 730 and a loudspeaker 731 can be connected to the system bus 733 by means of a user interface adapter 728, which may contain, for example, a super input / output chip that integrates several device adapters into a single integrated circuit.

[0067] In certain aspects of the present disclosure, a processing system 700 includes a graphics processing unit (GPU) 737. The graphics processing unit 737 is a specialized electronic circuit designed to operate and modify a working memory to accelerate the generation of images in a frame buffer intended for output to a display. In general, the graphics processing unit 737 is very efficient at manipulating computer graphics and image processing and has a highly parallel structure, which makes it more effective than generally applicable CPUs for algorithms, with the parallel processing of large blocks of data.

[0068] Thus, the processing system 700, as configured here, includes a processing capability in the form of processors 721, a storage capacity comprising the system memory 722 and the mass storage device 734, input means such as a keyboard 729 and a mouse 730, and an output capability comprising a loudspeaker 731 and a display device 739. In certain aspects of the present disclosure, a section of the system memory 722 and the mass storage device 734 jointly store the operating system 740 in order to coordinate the functions of the various components shown in the processing system 700.

[0069] The terms "a" and "an" do not denote a limit on the number of elements, but rather indicate the presence of at least one of the referenced element. The term "or" means "and / or" unless clearly indicated otherwise by context. A reference to "an aspect" in the application text means that a specific element (e.g., a feature, a structure, a step, or a property) described in connection with that aspect is contained in at least one aspect described therein and may or may not be present in other aspects. It should also be understood that the described elements in the various aspects may be combined in any suitable manner.

[0070] When an element, such as a layer, a thin layer, an area, or a substrate, is described as "attached" to another element, it may be located directly adjacent to that element, or there may be intervening elements. Conversely, when an element is described as "directly adjacent" to another element, there are no intervening elements.

[0071] Unless otherwise specified herein, all testing standards shall be the most recent valid standard as of the filing date of this application or, if priority is claimed, as of the filing date of the earliest priority application in which the testing standard appears.

[0072] Unless otherwise defined, technical and scientific terms used herein have the same meaning as would normally be understood by a person skilled in the field to which this disclosure belongs.

Claims

[1] Computer-implemented method (500, 600) for detecting a vehicle front or vehicle rear misalignment (302, 304) using a vehicle sensor (104) of a vehicle (100), the method comprising: Determine (506, 606) whether the vehicle (100) is in a misalignment detection state; in response to determining (506, 606) that the vehicle (100) is in the misalignment detection state, determining (506, 609) whether the vehicle (100) is experiencing a misalignment; in response to determining (506, 609) that the vehicle (100) is experiencing the misalignment, classifying (510, 610) the misalignment as a front misalignment (302) or a rear misalignment (304) by comparing a vehicle heading angle (312) with a vehicle movement angle (310), wherein the vehicle movement angle (310) is determined using sensor data (214) received from the vehicle sensor (104); and Performing (622) an alignment mitigation action to mitigate the negative effects of the misalignment on the vehicle (100); wherein the vehicle (100) uses an active rear steering system, and wherein performing (622) the alignment mitigation action to mitigate adverse effects of the misalignment on the vehicle (100) includes implementing an active rear steering remediation action in response to classifying the misalignment as the rear misalignment (304); characterized by , that the misalignment in response to the vehicle heading angle (312) being within a threshold difference of the vehicle movement angle (310), is classified as the front misalignment (302); and The misalignment in response to the fact that the vehicle heading angle (312) is not within a threshold difference of the vehicle movement angle (310) is classified as the rear misalignment (304). [2] Computer-implemented method (500, 600) according to claim 1, wherein no action is taken in response to the determination that the vehicle (100) is not in the misalignment detection state. [3] Computer-implemented method (500, 600) according to claim 1, wherein in response to the fact that a speed of the vehicle (100) is greater than a threshold value, wherein a lateral acceleration of the vehicle (100) is substantially zero, and in response to the fact that the vehicle (100) is traveling straight ahead, the vehicle (100) is in the misalignment detection state. [4] Computer-implemented method (500, 600) according to claim 1, wherein, in response to the fact that a steering wheel angle of a steering wheel of the vehicle (100) is greater than a threshold value, it is determined that the vehicle (100) experiences the misalignment. [5] Computer-implemented method (500, 600) according to claim 4, wherein the threshold is essentially zero. [6] Computer-implemented method (500, 600) according to claim 1, wherein implementing the active rear steering aid action comprises: Measuring a steering angle while driving straight ahead; Calculating a target rear wheel angle offset during straight-line driving; and Applying the target rear wheel angle offset to the active rear steering system using the sensor data (214) collected by the vehicle sensor (104).

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