Vehicle lateral motion management with preview road information

Abstract

Systems and methods for controlling a vehicle are provided. In one embodiment, a method includes: receiving a first surface value associated with a first road surface area in an upcoming environment of a vehicle; receiving a second surface value associated with a second road surface area in the upcoming environment of the vehicle; determining a change in surface value based on the first surface value and the second surface value; and in response to the change in surface value being greater than a threshold value, adjusting at least one of a vehicle collision warning message, a vehicle braking control, a vehicle steering control, and a path plan based on the second surface value.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to vehicle control, and more particularly to systems and methods for managing vehicle lateral motion based on road preview information. BACKGROUND

[0002] Sensor data and / or models can be used to determine in real-time the road surface conditions ahead of the vehicle. In some cases, for example due to snow or ice, the surface area of the upcoming road is estimated to be a low-friction surface area. In such cases, the autonomous or semi-autonomous control of the vehicle needs to be modified taking into account the low-friction conditions.

[0003] For example, control / driver interface methods for autonomous or semi-autonomous features, such as lane centering, lane changing, steering to avoid an impending collision, braking to avoid an impending collision, can result in loss of tracking capability when the vehicle encounters a change in surface conditions, for example from high to low friction. These controller / driver interface methods need to be modified to address such changes. Furthermore, other desirable features and characteristics of the present disclosure will become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and the foregoing technical field and background. SUMMARY

[0004] Systems and methods for controlling a vehicle are provided. In one embodiment, a method includes receiving a first surface value associated with a first road surface area in an upcoming environment of the vehicle; receiving a second surface value associated with a second road surface area in the upcoming environment of the vehicle; determining a change in surface values based on the first surface value and the second surface value; and in response to the change in surface values being greater than a threshold, adjusting at least one of a vehicle collision warning message, a vehicle braking control, a vehicle steering control, and a path plan based on the second surface value.

[0005] In various embodiments, the adjustment of the vehicle collision warning message includes adjusting a timing of generating the vehicle collision warning message based on the change in surface values.

[0006] In various embodiments, the adjustment of the vehicle braking control includes adjusting a timing of generating a vehicle braking command based on the change in surface values.

[0007] In various embodiments, the adjustment of the vehicle steering control includes adjusting a timing of generating a vehicle steering command based on the change in surface values.

[0008] In various embodiments, the method further includes projecting the second surface value to a waypoint of the upcoming path, and wherein the adjustment is based on the waypoint and the projected surface value.

[0009] In various embodiments, the adjustment of the vehicle braking control comprises adjusting a vehicle braking command based on a model predictive control based on the projected surface value.

[0010] In various embodiments, the adjustment of the vehicle steering control comprises adjusting a steering control command based on a model predictive control based on the projected surface value.

[0011] In various embodiments, the adjustment of the path planning comprises comparing a path curvature to a maximum path curvature of the surface mu.

[0012] In various embodiments, the adjustment of the path planning comprises modifying the path to position the vehicle in a new lane in response to the comparison result.

[0013] In various embodiments, the method further comprises calculating an error, and wherein the adjustment of the path planning is performed in response to the error being greater than a threshold.

[0014] In another embodiment, a system for controlling a vehicle comprises: at least one sensor that senses a road surface in an environment of the vehicle; and a control module configured to receive, by a processor, a first surface value associated with a first road surface region in a forthcoming environment of the vehicle, receive a second surface value associated with a second road surface region in the forthcoming environment of the vehicle, determine a change in surface value based on the first surface value and the second surface value, and in response to the change in surface value being greater than a threshold, adjust at least one of a vehicle collision warning message, a vehicle braking control, a vehicle steering control, and path planning based on the second surface value.

[0015] In various embodiments, the control module adjusts the vehicle collision warning message by adjusting a timing of generating the vehicle collision warning message based on the change in surface value.

[0016] In various embodiments, the control module adjusts the vehicle braking control by adjusting a timing of generating a vehicle braking command based on the change in surface value.

[0017] In various embodiments, the control module adjusts the vehicle steering control by adjusting a timing of generating a vehicle steering command based on the change in surface value.

[0018] In various embodiments, the control module is further configured to project the second surface value to a waypoint of the forthcoming path, and make the adjustment based on the waypoint and the projected surface value.

[0019] In various embodiments, the control module adjusts the vehicle braking control by adjusting a vehicle braking command based on a model predictive control based on the projected surface value.

[0020] In various embodiments, the control module adjusts vehicle steering control by adjusting steering control commands based on model predictive control based on the projected surface value.

[0021] In various embodiments, the control module adjusts path planning by comparing path curvature to a maximum path curvature of the surface mu.

[0022] In various embodiments, the control module adjusts path planning by modifying the path to position the vehicle in a new lane in response to the comparison result.

[0023] In various embodiments, the control module is further configured to calculate an error and adjust path planning in response to the error being greater than a threshold. BRIEF DESCRIPTION OF DRAWINGS

[0024] Exemplary embodiments will be described below with reference to the following drawings, in which like elements are referred to with like reference numerals, and in which:

[0025] Figure 1 is a functional block diagram illustrating an autonomous vehicle having a surface-based control system, in accordance with various embodiments;

[0026] Figure 2 is a dataflow diagram illustrating a control module of a surface-based control system, in accordance with various embodiments;

[0027] Figure 3 is a timing diagram illustrating control and timing of a surface-based control system, in accordance with various embodiments; and

[0028] Figure 4 is a flowchart illustrating a control method for controlling a vehicle based on a surface-based control system, in accordance with various embodiments. DETAILED DESCRIPTION

[0029] The following detailed description is merely exemplary in nature and is not intended to limit the application and use. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. As used herein, the term module refers to any hardware, software, firmware, electronic control component, processing logic, and / or processor device, individually or in any combination, including but not limited to: application specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or group) and memories that execute one or more software or firmware programs, combinational logic circuits, and / or other suitable components that provide the described functionality.

[0030] Embodiments of the present disclosure can be described herein in terms of functional and / or logical block components and various processing steps. It should be appreciated that such block components can be realized by any number of hardware, software, and / or firmware components configured to perform the specified functions. For example, an embodiment of the present disclosure can employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which can carry out a variety of functions under the control of one or more microprocessors or other control devices. Furthermore, those skilled in the art will appreciate that embodiments of the present disclosure can be practiced with one or more systems including a variety of

[0031] For the sake of brevity, conventional techniques related to signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) can not be described in detail herein. Furthermore, the connecting lines shown in the various figures contained herein are intended to represent example functional relationships and / or physical couplings between the various elements. It should be noted that many alternatives or additional functional relationships or physical connections can be present in an embodiment of the present disclosure.

[0032] Referring to Figure 1 , a surface-based control system, generally shown at 100, is associated with the vehicle 10 in accordance with various embodiments. As will be discussed in greater detail below, the surface-based control system 100 continuously monitors the environment of the vehicle 10 and determines surface values associated with upcoming roadways. The surface-based control system 100 performs automatic maneuvers or assists the driver in performing complex maneuvers while providing warnings or preparing the vehicle for control transfer in the presence of determined upcoming road surface condition changes, e.g., upcoming slippery road conditions.

[0033] As shown in Figure 1 , the vehicle 10 generally includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is disposed on the chassis 12 and substantially encloses the components of the vehicle 10. The body 14 and the chassis 12 can collectively form a frame. The wheels 16-18 are each rotatably connected to the chassis 12 near a respective corner of the body 14.

[0034] In various embodiments, vehicle 10 is an autonomous vehicle, and system 100 is incorporated into autonomous vehicle 10 (hereinafter referred to as autonomous vehicle 10). Autonomous vehicle 10 is, for example, a vehicle that is automatically controlled to transport a passenger from one location to another. In the illustrated embodiment, vehicle 10 is depicted as a passenger car, but it should be understood that any other vehicle, including motorcycles, trucks, sport utility vehicles (SUVs), recreational vehicles (RVs), watercraft, aircraft, etc., can also be used. In the example embodiment, autonomous vehicle 10 is autonomous in that it provides partial or full automatic assistance to a driver operating vehicle 10. As used herein, the term "operator" includes a driver of vehicle 10 and / or an autonomous driving system of vehicle 10.

[0035] As shown, autonomous vehicle 10 generally includes a propulsion system 20, a transmission system 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In various embodiments, propulsion system 20 can include an internal combustion engine, an electric machine such as a traction motor, and / or a fuel cell propulsion system. Transmission system 22 is configured to transfer power from propulsion system 20 to wheels 16-18 according to a selectable speed ratio. According to various embodiments, transmission system 22 can include a stepped automatic transmission, a continuously variable transmission, or other suitable transmission. Braking system 26 is configured to provide braking torque to wheels 16-18. In various embodiments, braking system 26 can include friction brakes, regenerative brakes, such as electric machines, and / or other suitable braking systems. Steering system 24 affects the position of wheels 16-18. Although depicted as including a steering wheel for purposes of illustration, steering system 24 can not include a steering wheel in some embodiments contemplated within the scope of the present disclosure.

[0036] Sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of an external environment and / or an internal environment of autonomous vehicle 10. Sensing devices 40a-40n can include, but are not limited to, radar, lidar, global positioning systems, optical cameras, thermal cameras, ultrasonic sensors, inertial measurement units, and / or other sensors.

[0037] Actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, propulsion system 20, transmission system 22, steering system 24, and braking system 26. In various embodiments, vehicle features can further include internal and / or external vehicle features, such as, but not limited to, doors, trunk, and cabin features, such as air, music, lighting, etc. (not numbered).

[0038] The communication system 36 is configured to wirelessly communicate information to and from other entities 48, such as but not limited to other vehicles ("V2V" communications), infrastructure ("V2I" communications), remote systems, and / or personal devices (see Figure 2 More detail is described below. In example embodiments, the communication system 36 is a wireless communication system configured to communicate via a wireless local area network (WLAN) using the IEEE 802.11 standard or through the use of cellular data communication. However, additional or alternative communication methods, such as a dedicated short-range communications (DSRC) channel, are also contemplated to be within the scope of the present disclosure. A DSRC channel refers to a one-way or two-way short-to-medium range wireless communication channel specifically designed for automotive applications, as well as a corresponding set of protocols and standards.

[0039] The data storage device 32 stores data used to automatically control the autonomous vehicle 10. In various embodiments, the data storage device 32 stores a defined map of a navigable environment. In various embodiments, the defined map can be pre-defined by a remote system and obtained from the remote system (see Figure 2 Further detail is described below. For example, the defined map can be assembled by the remote system and communicated to the autonomous vehicle 10 (wirelessly and / or in a wired manner) and stored in the data storage device 32. It can be appreciated that the data storage device 32 can be part of the controller 34, separate from the controller 34, or part of both the controller 34 and a separate system.

[0040] The controller 34 includes at least one processor 44 and a computer readable storage device or medium 46. The processor 44 can be any custom made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), a co-processor in a plurality of processors associated with the controller 34, a semiconductor-based microprocessor (in the form of a microchip or chip set), a macroprocessor, any combination thereof, or generally any device for executing instructions. The computer readable storage device or medium 46 can include volatile and nonvolatile storage in, for example, read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a nonvolatile memory that is used to store various operational variables when the processor 44 is powered down. The computer readable storage device or medium 46 can be implemented using any of a number of known memory devices, such as PROMs (programmable read-only memory), EPROMs (erasable PROM), EEPROMs (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combination memory that is capable of storing data, some of which represents executable instructions that the controller 34 uses when controlling the autonomous vehicle 10.

[0041] The instructions may include one or more separate programs, each comprising an ordered list of executable instructions for implementing logical functions. When executed by processor 44, these instructions receive and process signals from sensor system 28, execute logic, calculations, methods, and / or algorithms for automatically controlling components of autonomous vehicle 10, and generate control signals to actuator system 30 to automatically control components of autonomous vehicle 10 based on that logic, calculations, methods, and / or algorithms. Although in Figure 1 Only one controller 34 is shown, but embodiments of the autonomous vehicle 10 may include any number of controllers 34 that communicate via communication messages over any suitable communication medium or combination of communication media and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control the features of the autonomous vehicle 10.

[0042] In various embodiments, one or more instructions of controller 34 are embodied in system 100. When executed by processor 44, the instructions predict upcoming road surface conditions and control one or more features of vehicle 10 based on these conditions.

[0043] It is understood that the subject matter disclosed herein provides certain enhanced features and functions for autonomous or semi-autonomous vehicles 10 that can be considered as a standard or benchmark. Therefore, autonomous and semi-autonomous vehicles can be modified, enhanced, or supplemented to provide additional features as described in more detail below.

[0044] Now for reference Figure 2 And continue to refer to Figure 1 The data flow diagram shows Figure 1 Various embodiments of the control module 34 according to the present invention. Various embodiments of the control module 34 according to the present invention may include any number of sub-modules. It is understood that... Figure 2 The submodules shown can be combined and / or further divided to similarly determine surface conditions and control vehicle 10 based on them. Inputs to control module 34 can be received from sensing devices 40a-40n, or from other control modules (not shown) of vehicle 10, and / or determined by other submodules (not shown) of control module 34. In various embodiments, control module 34 includes a surface change determination module 102, a collision determination line adjustment module 104, a waypoint adjustment module 106, a control adjustment module 108, a path adjustment module 110, and a latitude / longitude control module 112.

[0045] In various embodiments, the surface change determination module 102 receives as input surface data 114 sensed from one or more sensing devices 40a-40n of the vehicle 10. The surface data 114 includes a first surface value associated with a second road surface in an upcoming environment of the vehicle 10 and a first road surface in the upcoming environment. The surface change determination module 102 determines a change in surface value between the two surface areas in the upcoming environment of the vehicle 10. The surface change determination module 102 generates surface change data 116 including the two surface values and a flag indicating when the calculated change is greater than a threshold value.

[0046] In various embodiments, the collision judgment line adjustment module 104 receives as input the surface change data 116, vehicle data 118 including a speed and acceleration of the vehicle 10, and vehicle data 120 associated with another vehicle identified as a potential collision vehicle. The collision judgment line adjustment module 104 adjusts a time of automatic control and / or warning feature activation based on the surface change data 116. For example, as shown in more detail in Figure 3 The collision timeline 200 shows a time (in seconds) of warning and / or control activity for a default surface mu (e.g., μΐ = 1); the second collision timeline 202 shows a time (in seconds) of warning and / or control activity for a second surface mu (e.g., μ2 = 0.5).

[0047] In various embodiments, as shown, the collision judgment line adjustment module 104 adjusts the timing from time 204 to time 206 indicating when a warning should be issued to the driver. The collision judgment line adjustment module 104 generates timing data 122 used by the collision system based on this.

[0048] In various embodiments, as shown, the collision judgment line adjustment module 104 adjusts the timing from time 208 to time 210 indicating when full braking should occur. The collision judgment line adjustment module 104 generates timing data 122 based on the following relationship;

[0049]

[0050] In various embodiments, the collision judgment line adjustment module 104 adjusts the timing from time 212 to time 214 indicating when full steering should occur. The collision judgment line adjustment module 104 generates timing data 122 based on the following relationship:

[0051]

[0052] In various embodiments, the collision judgment line adjustment module 104 adjusts the timing from time 216 to time 218, indicating when the optimal combination of braking and steering should occur, e.g., an emergency lane change. The collision judgment line adjustment module 104 generates timing data 122 based on the default values and adjustments to the timing of the steering.

[0053] It can be appreciated that other timing can be adjusted in various embodiments, as the present disclosure is not limited to the current example.

[0054] Referring back to Figure 2 The waypoint adjustment module 106 receives the surface variation data 116 and waypoint data 124 defining points of the upcoming path as inputs. The waypoint adjustment module 106 re-formulates the waypoints of the path to include future surface values. For example, the current surface mu at time t is determined to be μ(t); and the current curvature value at time t is defined as h(t). The waypoint adjustment module 106 projects future friction values: μ(t + nτ). The waypoint adjustment module 106 then correlates the future friction values with the curvature values of the waypoints over time. The waypoint adjustment module 106 provides reformulated waypoint data 126, which includes waypoints defined by curvature and surface values.

[0055] In various embodiments, the control adjustment module 108 receives the reformulated waypoint data 126, braking command data 128, and / or steering command data 130 as inputs. When lateral control is activated in the vehicle 10, the control adjustment module 108 adjusts the steering command and / or braking command based on the reformulated waypoint data 126. For example, the control adjustment module 108 uses model predictive control to adjust the steering command and / or braking command. In one example, a nonlinear bicycle model can be modified to account for surface values:

[0056]

[0057]

[0058]

[0059] The control adjustment module 108 then calculates error tracking data 134. For example, lateral heading and offset errors can be calculated according to the following equations:

[0060]

[0061] In various embodiments, the path adjustment module 110 receives reconstructed waypoint data 126 and error tracking data 134 as input. The path adjustment module 110 adjusts the upcoming path by reallocating path curvature to meet friction constraints. For example, the path curvature (x) is adjusted between each waypoint to a value less than the maximum permissible path curvature of surface mu at x. The path adjustment module 110 generates adjusted path data 136 based on the adjusted values.

[0062] The lateral and longitudinal control module 112 receives adjusted command data 132 and adjusted path data 136 as inputs. The lateral and longitudinal control module 112 generates control signals 138 to the actuators of the vehicle 10 to control the steering and / or braking of the vehicle 10, so that the vehicle follows a path indicated by the received data for wheel angles, braking, and / or adjustments.

[0063] Now for reference Figure 4 And continue to refer to Figures 1-3 The flowchart illustrates the process that can be performed according to this disclosure by Figures 1-3 The system 100 executes control method 400. As can be understood from this disclosure, the order of operations within the method is not limited to... Figure 4 The method 400 may not be executed in the order shown, but may be executed in one or more different orders as applicable and in accordance with this disclosure. In various embodiments, method 400 may be scheduled to run based on one or more predetermined events, and / or may run continuously during the operation of the autonomous vehicle 10.

[0064] In one example, method 400 may begin at 405. At 410, the current wheel angle, vehicle state, and desired path (waypoints to be followed) are received. Subsequently, at 420, surface value data is received, and at 430, it is determined whether a change towards lower friction is anticipated in the upcoming path. At 430, if lower friction is not anticipated in the upcoming path, method 400 continues to receive control data at 410. If lower friction is anticipated in the upcoming path at 430, it is determined at 440 whether automatic lateral control is activated. If automatic lateral control is not activated at 440, method 400 continues at 450 to generate a warning message to the driver, indicating that a low-friction area is ahead. The method may then terminate at 460.

[0065] For example, as described above, when automatic lateral control is activated at 440, friction values ​​are projected onto each waypoint ahead at 470. For example, as described above, at 480, model predictive control is used to determine the optimal wheel angle and braking command.

[0066] Thereafter, at 490, a predicted tracking error is computed, and at 500, it is compared to a threshold. When the predicted tracking error is not greater than the threshold at 500, at 510, the lateral and longitudinal motion controllers transmit control input commands to the vehicle’s actuator system to control the vehicle based on the updated waypoints. Thereafter, the method can end at 460.

[0067] When the predicted tracking error is greater than the threshold at 500, the path planner reassigns path curvatures to satisfy the friction limit at 520, and transmits the new path to the lateral and longitudinal motion controllers at 530. Thereafter, at 510, the lateral and longitudinal motion controllers transmit control input commands to the vehicle’s actuator system to control the vehicle based on the updated waypoints. Thereafter, the method can end at 460.

[0068] While at least one example embodiment has been described herein, it will be understood by those within the art that various changes can be made and equivalents can be used without departing from the scope of the present disclosure. For example, the order of the steps described herein can be changed, or additional steps can be added, or some of the steps described herein can be eliminated, without departing from the scope of the present disclosure. Also, some of the steps described herein can be performed simultaneously, or in an overlapping manner, without departing from the scope of the present disclosure. Furthermore, the steps described herein can be performed by different entities, or by the same entity, without departing from the scope of the present disclosure. Also, the steps described herein can be performed by hardware, software, or a combination thereof, without departing from the scope of the present disclosure. Additionally, some of the steps described herein can be performed automatically, or by a user, without departing from the scope of the present disclosure. Furthermore, the steps described herein can be performed on a single device, or on multiple devices, without departing from the scope of the present disclosure.

Claims

1. A method for controlling a vehicle, comprising: Receive a first surface value associated with a first road surface region in the environment in which the vehicle is approaching; Receive a second surface value associated with a second road surface region in the environment in which the vehicle is approaching; The change in surface value is determined based on the first surface value and the second surface value; and In response to a change in surface value exceeding a threshold, the timing of automatic control and / or warning feature activation is adjusted based on the change in surface value as input, according to the default collision timeline for the default surface mu and the second collision timeline for the surface mu for the second surface.

2. The method according to claim 1, wherein, The warning features include adjusting the timing of generating vehicle collision warning messages based on changes in surface values.

3. The method according to claim 1, wherein, Adjusting automatic control includes adjusting the timing of generating vehicle braking commands based on changes in surface values.

4. The method according to claim 1, characterized in that, Adjusting automatic control includes adjusting the timing of generating vehicle steering commands based on changes in surface values.

5. The method of claim 1, further comprising projecting the second surface value onto a waypoint of an upcoming path, wherein the adjustment is based on the waypoint and the projected surface value.

6. The method according to claim 5, wherein, Adjusting automatic control includes adjusting vehicle braking commands based on model predictive control, which is based on projected surface values.

7. The method according to claim 5, wherein, Adjusting automatic control includes adjusting steering control commands based on model predictive control, whereby the model predictive control is based on projected surface values.

8. The method according to claim 5, wherein, Adjusting the automatic control involves comparing the path curvature with the maximum path curvature of the surface mμ.

9. The method according to claim 8, wherein, Adjusting automatic control includes modifying the path in response to the result of the comparison to position the vehicle in a new lane.

10. A system for controlling a vehicle, comprising: At least one sensor that senses the road surface in the vehicle environment; and The control module is configured to receive, via a processor, a first surface value associated with a first road surface in the environment in which the vehicle is about to arrive, a second surface value associated with a second road surface in the environment in which the vehicle is about to arrive, determine a change in the surface value based on the first and second surface values, and, in response to a change in the surface value being greater than a threshold, adjust the timing of the activation of automatic control and / or warning features based on a default collision timeline for a default surface mu and a second collision timeline for a surface mu for the second surface, using the change in the surface value as input.

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