Active trajectory tracking control for autonomous driving during altitude transitions

Abstract

In example embodiments, methods, systems, and vehicles are provided that include one or more sensors disposed on a vehicle configured to at least facilitate obtaining sensor data of the vehicle, one or more position systems configured to at least facilitate obtaining position data related to a position of the vehicle, a computer memory configured to store map data related to a path corresponding to the position, and a processor disposed on the vehicle configured to at least facilitate generating an elevation profile along the path using the sensor data and the map data, and providing instructions for controlling the vehicle using the elevation profile.

Description

Technical Field

[0001] The technical field generally relates to vehicles, and more specifically, to methods and systems for controlling vehicles during changes in road elevation. Background Technology

[0002] Currently, some vehicles include systems for controlling the vehicle based on estimates of the road gradient and tilt angles of the road the vehicle is traveling on. However, such existing vehicle systems typically involve single-point estimates of the vehicle's location at a given time and location, and may not provide optimal estimates in certain situations that lead to suboptimal control performance.

[0003] Therefore, it is desirable to provide improved methods and systems for controlling vehicles during road elevation changes. Summary of the Invention

[0004] According to an exemplary embodiment, a method is provided, comprising: obtaining sensor data from one or more sensors on a vehicle; obtaining location data related to the vehicle's location; obtaining map data related to a path corresponding to the location; using a processor, generating an elevation distribution along the path using the sensor data and the map data; and actively controlling the vehicle using the predicted elevation distribution based on instructions provided by the processor.

[0005] Furthermore, in an exemplary embodiment, the method further includes: receiving user input regarding the vehicle's destination; and generating a planned task to travel along a road associated with the path to the destination based on the user input and location data; wherein the step of generating the elevation distribution includes generating a road elevation distribution on a gradually receding predicted horizon of the road, using sensor data and map data, according to the planned task via a processor; and wherein the step of controlling the vehicle includes controlling the vehicle using the predicted road elevation distribution on the gradually receding predicted horizon, based on instructions provided by the processor.

[0006] Furthermore, in an exemplary embodiment, the road elevation distribution includes the road's slope angle and inclination angle, as well as the distribution of the predicted horizon that gradually recedes away.

[0007] Furthermore, in an exemplary embodiment, the road elevation distribution is generated by the processor based on camera data and lane level map data of the road.

[0008] Furthermore, in an exemplary embodiment, the method further includes performing an elevation distribution transformation from road coordinates to vehicle coordinates via a processor to generate a transformed elevation distribution.

[0009] Furthermore, in an exemplary embodiment, the step of controlling the vehicle includes controlling the lateral dynamics of the vehicle via instructions provided by a processor based on the converted altitude distribution.

[0010] Furthermore, in an exemplary embodiment, the step of controlling the vehicle includes controlling the longitudinal dynamics of the vehicle via instructions provided by a processor based on the converted altitude distribution.

[0011] In another exemplary embodiment, a system is provided comprising: one or more sensors configured to at least contribute to obtaining dynamic measurements and path information of a vehicle; one or more location systems configured to at least contribute to obtaining location data related to the location of the vehicle; a computer memory configured to store map data related to a path corresponding to the location; and a processor configured to at least contribute to: generating an elevation distribution along the path using the sensor data and the map data; and providing instructions to control the vehicle using the elevation distribution.

[0012] Furthermore, in one exemplary embodiment, one or more sensors are configured to at least facilitate receiving user input regarding the vehicle's destination; and the processor is configured to at least facilitate: generating a planned task to travel along a road associated with the path to the destination based on the user input and location data; generating a road elevation distribution on a gradually receding predicted horizon of the road, according to the planned task, using the sensor data and the map data; and providing instructions for controlling the vehicle using the road elevation distribution on the gradually receding predicted horizon.

[0013] Furthermore, in an exemplary embodiment, the road elevation distribution includes the road's slope angle and inclination angle, as well as the distribution of the predicted horizon that gradually recedes away.

[0014] Furthermore, in one exemplary embodiment, the processor is configured to at least contribute to generating a road elevation distribution based on camera data and lane level map data of the road.

[0015] Furthermore, in one exemplary embodiment, the processor is configured to at least facilitate the conversion of the elevation distribution from road coordinates to vehicle coordinates, generating the converted elevation distribution.

[0016] Furthermore, in one exemplary embodiment, the processor is also configured to at least contribute to controlling the lateral movement of the vehicle based on the converted elevation distribution.

[0017] Furthermore, in one exemplary embodiment, the processor is also configured to at least facilitate the control of the vehicle's longitudinal movement based on the converted elevation distribution.

[0018] In another exemplary embodiment, a vehicle is provided, comprising: a vehicle body; a propulsion system configured to generate motion of the vehicle body; one or more sensors disposed on the vehicle and configured to at least contribute to obtaining sensor data of the vehicle; one or more position systems configured to at least contribute to obtaining position data related to the position of the vehicle; a computer memory configured to store map data related to a path corresponding to the position; and a processor disposed on the vehicle, the processor being configured to at least contribute to: generating an elevation distribution along the path using the sensor data and the map data; and providing instructions to control the vehicle using the elevation distribution.

[0019] Furthermore, in one exemplary embodiment, one or more sensors are configured to at least facilitate receiving user input regarding the vehicle's destination; and the processor is configured to at least facilitate: generating a planned task to travel along a road associated with the path to the destination based on the user input and location data; generating a road elevation distribution on a gradually receding predicted horizon of the road, according to the planned task, using the sensor data and the map data; and providing instructions for controlling the vehicle using the road elevation distribution on the gradually receding predicted horizon.

[0020] Furthermore, in an exemplary embodiment, the road elevation distribution includes the road's slope angle and inclination angle, as well as the distribution of the predicted horizon that gradually recedes away.

[0021] Furthermore, in one exemplary embodiment, the processor is configured to at least contribute to generating a road elevation distribution based on camera data and lane level map data of the road.

[0022] Furthermore, in an exemplary embodiment, the processor is configured to at least facilitate the conversion of the elevation distribution from road coordinates to vehicle coordinates, generating the converted elevation distribution.

[0023] Furthermore, in an exemplary embodiment, the processor is also configured to at least facilitate the control of the vehicle's lateral and longitudinal movements based on the converted elevation distribution. Attached Figure Description

[0024] The present disclosure will now be described in conjunction with the following accompanying drawings, wherein like reference numerals denote like elements, and wherein:

[0025] Figure 1 This is a functional block diagram of a vehicle according to an exemplary embodiment, the vehicle including a control system for controlling the vehicle relative to changes in road elevation;

[0026] Figure 2 This is according to an exemplary embodiment. Figure 1 A block diagram of the control system modules;

[0027] Figure 3 This is a process flowchart according to an exemplary embodiment for controlling a vehicle relative to changes in road elevation, the process of which can be combined with Figure 1 vehicles and Figure 1 and 2 This is achieved through a control system; and

[0028] Figure 4-9 An exemplary embodiment is shown. Figure 3 Some implementations of the process. Detailed Implementation

[0029] The following detailed description is merely exemplary in nature and is not intended to limit this disclosure or its application and use. Furthermore, it is not intended to be bound by any theories presented in the foregoing background or the following detailed description.

[0030] Figure 1 A vehicle 100 according to an exemplary embodiment is shown. As described in more detail below, according to an exemplary embodiment, the vehicle 100 includes a control system 102 for controlling road elevation changes of the vehicle 100 using active model control with a predictive time horizon, utilizing sensor, location, and map data.

[0031] In various embodiments, vehicle 100 includes automobiles. In some embodiments, vehicle 100 can be any of a variety of different types of automobiles, such as sedans, vans, trucks, or sports utility vehicles (SUVs), and can be two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), four-wheel drive (4WD), or all-wheel drive (AWD), and / or various other types of vehicles. In some embodiments, vehicle 100 may also include motorcycles or other vehicles, such as aircraft, spacecraft, ships, etc., and / or one or more other types of mobile platforms (e.g., robots and / or other mobile platforms).

[0032] The vehicle 100 includes a body 104 disposed on a chassis 116. The body 104 substantially surrounds the other components of the vehicle 100. The body 104 and the chassis 116 may together form a frame. The vehicle 100 also includes a plurality of wheels 112. Each wheel 112 is rotatably connected to the chassis 116 near a corresponding corner of the body 104 to facilitate movement of the vehicle 100. In one embodiment, the vehicle 100 includes four wheels 112, although this may vary in other embodiments (e.g., for trucks and certain other vehicles).

[0033] The drive system 110 is mounted on the chassis 116 and drives the wheels 112, for example, via axle 114. The drive system 110 preferably includes a propulsion system. In some exemplary embodiments, the drive system 110 includes an internal combustion engine and / or an electric motor / generator, and is connected to its transmission. In some embodiments, the drive system 110 may vary, and / or two or more drive systems 112 may be used. For example, the vehicle 100 may also incorporate any one or a combination of a variety of different types of propulsion systems, such as an internal combustion engine fueled by gasoline or diesel, a "flexible fuel vehicle" (FFV) engine (i.e., using a mixture of gasoline and alcohol), an engine fueled by gaseous compounds (e.g., hydrogen and / or natural gas), a combustion / electric motor hybrid engine, and an electric motor.

[0034] like Figure 1 As shown, in various embodiments, the vehicle also includes a braking system 106 and a steering system 108. In an exemplary embodiment, the braking system 106 uses a braking component to control the braking of the vehicle 100, which is automatically controlled via input provided by the driver (e.g., via the brake pedal in some embodiments) and / or via the control system 102. Furthermore, in an exemplary embodiment, the steering system 108 controls the steering of the vehicle 100 via a steering component (e.g., a steering column coupled to axle 114 and / or wheel 112), which is automatically controlled via input provided by the driver (e.g., via the steering wheel in some embodiments) and / or via the control system 102.

[0035] exist Figure 1 In the illustrated embodiment, the control system 102 is connected to the braking system 106, the steering system 108, and the drive system 110. Similarly, as... Figure 1 As shown, in various embodiments, the control system 102 includes a sensor array 120, a position system 130, and a controller 140.

[0036] In various embodiments, sensor array 120 includes various sensors that acquire sensor data for tracking road height and controlling vehicle 10 based on road height. In the depicted embodiments, sensor array 120 includes an inertial measurement sensor 121, an input sensor 122 (e.g., a brake pedal sensor that measures braking input provided by the driver, and / or a touchscreen sensor, and / or other input sensors configured to receive input from the driver or other user of vehicle 10); a steering sensor 123 (e.g., coupled to the steering wheel and / or wheels of vehicle 10 and configured to measure its steering angle), a torque sensor 124 (e.g., configured to measure the torque of the vehicle), a speed sensor 125 (e.g., a wheel speed sensor and / or other sensor configured to measure the speed and / or rate of the vehicle and / or data for calculating such speed and / or rate), and a camera 126 (e.g., configured to acquire camera images of the road on which the vehicle is traveling).

[0037] Similarly, in various embodiments, the location system 130 is configured to acquire and / or generate data about the location and / or orientation of the vehicle. In some embodiments, the location system 130 includes and / or is connected to a satellite-based network and / or system, such as the Global Positioning System (GPS) and / or other satellite-based systems.

[0038] In various embodiments, controller 140 is coupled to sensor array 120 and position system 130. Also in various embodiments, controller 140 includes a computer system (also referred to herein as computer system 14) and includes processor 142, memory 144, interface 146, storage device 148, and computer bus 150. In various embodiments, controller (or computer system) 140 controls vehicle operation based on road incline and gradient and during changes in road elevation. In various embodiments, controller 140... Figure 3 process steps and Figure 4-9 The implementation provides these and other functionalities.

[0039] In various embodiments, the controller 140 (and in some embodiments, the control system 102 itself) is disposed within the vehicle body 104 of the vehicle 100. In one embodiment, the control system 102 is mounted on the chassis 116. In some embodiments, the controller 140 and / or the control system 102 and / or one or more components thereof may be disposed externally to the vehicle body 104, such as on a remote server, in the cloud, or on other devices that remotely perform image processing.

[0040] It should be understood that controller 140 may be different Figure 1The illustrated embodiment. For example, controller 140 may be coupled to or utilize one or more remote computer systems and / or other control systems, for example as part of one or more of the aforementioned vehicle 100 devices and systems.

[0041] In the depicted embodiment, the computer system of controller 140 includes a processor 142, a memory 144, an interface 146, a storage device 148, and a bus 150. The processor 142 performs the computational and control functions of controller 140 and may include any type of processor or multiple processors, a single integrated circuit such as a microprocessor, or any suitable number of integrated circuit devices and / or circuit boards, which work together to realize the functions of the processing unit. During operation, processor 142 executes one or more programs 152 contained in memory 144 and thus controls the general operation of controller 140 and the computer system of controller 140, typically in the process described herein, for example, in conjunction with the following... Figure 3 , Figure 4-9 The process 300 is further discussed in the embodiments.

[0042] Memory 144 can be any suitable type of memory. For example, memory 144 can include various types of dynamic random access memory (DRAM), such as SDRAM, various types of static RAM, and various types of non-volatile memory (PROM, EPROM, and flash memory). In some examples, memory 144 is located on and / or co-located on the same computer chip as processor 142. In the depicted embodiment, memory 144 stores the aforementioned program 152, as well as map data 154 (e.g., from and / or used in conjunction with location system 130) and one or more stored values ​​156 (e.g., in various embodiments, including road elevation data from upcoming road segments and / or other roads, and / or thresholds for determining and / or implementing vehicle control based on road inclination and / or slope).

[0043] Bus 150 is used to transmit programs, data, status, and other information or signals between various components of the computer system of controller 140. Interface 146 allows communication, for example, from system drives and / or another computer system to the computer system of controller 140, and can be implemented using any suitable methods and means. In one embodiment, interface 146 obtains various data from sensor array 120 and / or position system 130. Interface 146 may include one or more network interfaces for communicating with other systems or components. Interface 146 may also include one or more network interfaces for communicating with technicians, and / or one or more storage interfaces for connecting to storage devices, such as storage device 148.

[0044] Storage device 148 can be any suitable type of storage device, including various types of direct access memory and / or other storage devices. In one exemplary embodiment, storage device 148 includes a program product from which memory 144 can receive program 152, which performs one or more embodiments of one or more processes of this disclosure, such as those described below. Figure 3 , Figure 4-9 The steps of process 300 are further discussed in the embodiments. In another exemplary embodiment, the program product may be directly stored in and / or accessed by memory 144 and / or disk (e.g., disk 157), as described below.

[0045] Bus 150 can be any suitable physical or logical device for connecting computer systems and components. This includes, but is not limited to, direct hardwired connections, fiber optic, infrared, and wireless bus technologies. During operation, program 152 is stored in memory 144 and executed by processor 142.

[0046] It should be understood that although this exemplary embodiment is described in the context of a full-featured computer system, those skilled in the art will recognize that the mechanisms of this disclosure can be distributed as program products having one or more types of non-transitory computer-readable signal-bearing media for storing and executing the program and its instructions, such as a non-transitory computer-readable medium carrying the program and containing computer instructions stored therein for causing a computer processor (e.g., processor 142) to execute and execute the program. Such program products can take many forms, and this disclosure applies equally regardless of the specific type of computer-readable signal-bearing medium used to execute the distribution. Examples of signal-bearing media include recordable media, such as floppy disks, hard disks, memory cards, and optical disks, and transmission media, such as digital and analog communication links. It should be understood that cloud-based storage and / or other technologies may also be utilized in some embodiments. Similarly, it can be understood that the computer system of controller 140 may also differ from... Figure 1 In the illustrated embodiments, for example, the computer system of controller 140 may be connected to or may utilize one or more remote computer systems and / or other control systems.

[0047] Figure 2 Provided according to exemplary embodiments Figure 1 The functional block diagram of the control system 102 modules. (See attached diagram.) Figure 2 As shown, in various embodiments, the control system 102 includes Figure 1 Location system 130 (e.g., GPS), Figure 1 Inertial measurement sensor 121 and camera 126 and Figure 1 Map data 154 (e.g., stored in...) Figure 1 (in memory 144).

[0048] like Figure 1 As shown, in various embodiments, data from location system 130 (e.g., GPS), inertial measurement sensor 121, camera 126, and map data 154 (and, in various embodiments, data from...) Figure 1 Additional data from the additional sensors of the sensor array 120 are provided as input to algorithm 202 to predict the vehicle's speed at each time point (t). k , t k+1 , ..., t k+p Operations and conversions.

[0049] Similarly, in various embodiments, algorithm 202 utilizes a Bayesian filter according to the following equation:

[0050]

[0051] Where p(x) k |z 1：k The probability distribution of state updates based on the predicted state and the measurement likelihood calculated using a Bayesian estimator are used for this purpose. Other estimation algorithms can also be used for this purpose.

[0052] Similarly, in various embodiments, algorithm 202 is via Figure 1 The processor 142 executes and generates predicted disturbances 208 at each time point according to the following equation:

[0053] φ k φ k+1 φ k+2 …φ k+p (Equation 2),

[0054]

[0055] θ k θ k+1 θ k+2 …θ k+p (Equation 4), and

[0056]

[0057] Where φ k θ represents the road slope angle. k Indicates the road slope angle. This represents the vehicle's desired yaw rate, and It is the desired longitudinal acceleration.

[0058] Similarly, in various embodiments, via Figure 1 The processor 144 generates tracking error (e k )206. Tracking error is the difference between the vehicle trajectory and the desired vehicle trajectory.

[0059] As depicted in the various embodiments, prediction disturbance 208 and tracking error 206 are provided for model predictive control (MPC) to control the vehicle 10 in a manner that compensates for road elevation disturbances in the predictions. In the various embodiments, Figure 1 The processor 144 uses adaptive active control 212 at each time point (t k , t k+1 , ..., t k+p Provide instructions for model predictive control to the vehicle along the horizon that is gradually moving away (e.g., by providing instructions for adjusting the acceleration, braking and / or steering of vehicle 10).

[0060] Figure 3 This is a flowchart of a process 300 for controlling a vehicle relative to a road elevation change, according to an exemplary embodiment. In various embodiments, process 300 may be combined with... Figure 1 Vehicle 100 and Figure 1 and 2 This is achieved through the control system 102. Figure 3 The process 300 will also be combined below. Figure 4-9 Further discussion, Figure 4-9 Different implementations of process 300 according to various embodiments are shown.

[0061] like Figure 3 As shown, the process begins at step 301. In one embodiment, process 300 begins when the vehicle is driven or the ignition cycle begins, such as when the driver approaches or enters the vehicle 100, or when the driver turns on the vehicle and / or its ignition device (e.g., by turning a key, engaging a key chain, or pressing the start button). In one embodiment, the steps of process 300 are performed continuously during vehicle operation.

[0062] Generate user input for the vehicle (step 302). In various embodiments, the user input is generated via... Figure 1The input sensor 122 obtains information from the driver or other user of vehicle 100. In various embodiments, user input includes the destination of vehicle 100 for the current vehicle driving. Furthermore, in some embodiments, user input may also include one or more other user requests related to vehicle driving, such as preferences regarding the route or route type, control over one or more automated features of vehicle 100, etc. In some embodiments, user input is provided by the driver or other user of vehicle 100 via one or more buttons, switches, knobs, touchscreens, microphones, and / or other devices of vehicle 100, such as... Figure 1 The location system 130 is part of the location system (e.g., in some embodiments, as part of a navigation system and / or GPS system, etc.). In various embodiments, the user input in step 302 is provided. Figure 1 The processor 142 is used to process, and to determine and implement the remaining steps of process 300, as described below, for example.

[0063] Similarly, in some embodiments, additional sensor data is obtained (step 304). In various embodiments, via... Figure 1 The sensor array 120 uses one or more inertial measurement sensors 121, steering sensors 123, torque sensors 124, speed sensors 125, cameras 126, and / or other sensors to obtain sensor data about the vehicle 100 and / or the road or path traveled by the vehicle 100. In various embodiments, the sensor data in step 304 is provided to... Figure 1 The processor 142 is used to process, and to determine and implement the remaining steps of process 300, as described below, for example.

[0064] Obtain the vehicle's position data (step 306). In various embodiments, this is achieved through... Figure 1 The location system 130 (e.g., GPS) obtains location data related to the location of the vehicle 100. In some embodiments, this location information is obtained using information from one or more satellites and includes the longitudinal and lateral coordinates of the vehicle 100. In various embodiments, the location data of step 306 is provided to... Figure 1 The processor 142 is used to process, and to determine and implement the remaining steps of process 300, as described below, for example.

[0065] Map data for vehicle driving is also obtained (step 308). In various embodiments, lane-level map data of the road or path traveled by vehicle 100 is obtained. In various embodiments, based on the location data from step 306, data is obtained from data stored in... Figure 1 The map data is retrieved from one or more map data 154 in memory 144 corresponding to the lanes and roads or paths traveled by vehicle 100.

[0066] Obtain camera data (step 310). In various embodiments, camera data of the road or path traveled by vehicle 100 is acquired, including information about road inclination and slope angle. In various embodiments, the camera data is obtained relative to the current lane in which vehicle 100 is traveling. In some embodiments, camera data about adjacent and / or other nearby lanes is also acquired. In some embodiments, the camera data, including information about road inclination and slope angle, is obtained in the current and previous iterations of process 300 from map data from step 308 and from camera images obtained from sensor data acquired in step 304.

[0067] Plan a mission for the vehicle (step 312). In various embodiments, based on the user input in step 302, plan a mission (or travel path) for vehicle 100 to reach the destination currently being driven by the vehicle. In various embodiments, the mission is... Figure 1 The processor 142 determines the route to include roads and driving lanes within those roads in order to reach the user-selected destination. In some embodiments, the location data from step 306, the map data from step 308, and / or the camera data from step 310 are also used by the processor 142 that selects the task.

[0068] Furthermore, in various embodiments, a slope distribution is generated (step 314). In various embodiments, by... Figure 1 The processor 142 generates a slope distribution relative to the slope angle of the road and road or path that gradually moves away from the predicted horizon along the road or path associated with the task of step 312. In various embodiments, the slope distribution is generated in this manner using the map data from step 308 and the camera data from step 310.

[0069] The following is combined Figure 4 and Figure 5 The exemplary implementation shown describes the generation of the slope distribution in step 314.

[0070] refer to Figure 4 According to an exemplary embodiment, vehicle 100 is depicted traveling along road 400 having multiple lanes. In various embodiments, vehicle 100 is traveling along a road corresponding to... Figure 4 The path (or mission) of point 402, indicated by a star, proceeds, with point 402 extending across multiple lanes of road 400.

[0071] Similarly, Figure 4As shown, various slope and tilt angle data points (e.g., from camera data and map data) are obtained along different lanes and segments of road 400. Specifically, in the illustrated embodiment, the slope and tilt angle data points include: (i) a first slope and tilt angle data point 410 along a first lane of road 400 where vehicle 100 is currently (or initially) traveling; (ii) a second slope and tilt angle data point 411 corresponding to a second lane of road 400 (e.g., a lane adjacent to the lane of road 400 where vehicle 100 is currently traveling); and (iii) a third slope and tilt angle data point 412 along a third lane of road 400 (e.g., a lane two lanes away from the lane where vehicle 100 is currently traveling). According to an exemplary embodiment, Figure 4 An exemplary implementation is depicted in which vehicle 100 is performing (or will perform) a lane-changing operation across three lanes of road 400.

[0072] refer to Figure 5 According to an exemplary embodiment, Figure 4 Exemplary implantation located at Figure 1 A close-up view of vehicle 100 on road 400 is shown. Figure 5 As shown, each travel point 402 of the vehicle 100 includes the relevant road gradient angle Φx502 of the road 400. Similarly, as... Figure 5 As shown, in various embodiments: (i) each first data point 410 includes the relevant road slope angle Φ of the first lane of road 400. 0 x510; (ii) Each second data point 411 includes the relevant road slope angle Φ of the second lane of road 400. 1 x511; and (iii) each third data point 412 includes the relevant road slope angle Φ of the third lane of road 400. 2 x512.

[0073] As also depicted in the various embodiments, the road slope angle value is determined relative to a coordinate system having an x-axis 520 corresponding to the current direction of travel of the vehicle 100 and a y-axis 530 perpendicular to it.

[0074] Furthermore, in various embodiments, the road slope angle is determined according to the following equation (examples of non-limiting models of exemplary mathematical functions can be used to implement the methods disclosed in this application):

[0075] y(x)=d1x+d2x 2 +d3x 3 +d4x 4 +d5x 5 (Equation 6),

[0076] y i(x)=c0+c1x+c2x 2 +c3x 3 (Equation 7),

[0077]

[0078] For 0≤x k ≤x p (Equation 9),

[0079]

[0080] as well as

[0081]

[0082] Where Φ represents the road slope angle, and y is the lateral offset of the vehicle at a distance x ahead relative to its current position, c0, ..., c3 are the polynomial coefficients of the lane center of each lane i, and d1, ..., d5 are the polynomial coefficients of the desired trajectory or planned task distribution to be determined on multiple lanes.

[0083] Similarly, in various embodiments, similar to Figure 5 For example, each travel point 402 of vehicle 100 similarly includes the relevant road inclination angle θ(x) 502 of road 400. Likewise, as... Figure 5 As shown, in various embodiments: (i) each first data point 410 further includes a relevant road inclination angle θ0(x) of a first lane of road 400; (ii) each second data point 411 includes a relevant road inclination angle θ1(x) of a second lane of road 400; and (iii) each of the third data points 412 includes a relevant road inclination angle θ2(x) of a third lane of road 400, which is determined by... Figure 1 The processor 142 determines the slope angle φ in a manner similar to that described above in conjunction with Formula 6-11 for the road slope angle φ.

[0084] Return to reference Figure 3 In various embodiments, the slope distribution is converted (step 316). Specifically, in various embodiments, Figure 1 The processor 142 converts the road slope angle distribution from the road coordinate system to the vehicle coordinate system in step 314. The following is based on... Figure 6 An exemplary implementation of the transformation in step 316 is described below.

[0085] refer to Figure 6 Vehicle 100 is depicted situated on road 400, which has road path 602. Similarly... Figure 6 As shown, the vehicle 100 travels along the desired path 604. According to an exemplary embodiment, Figure 6The diagram also shows the desired yaw angle e of the desired path 604 relative to the road path 602. 2d 606.

[0086] Continue to refer to Figure 6 In various embodiments, the transformation in step 316 includes a rotation from road coordinates (e.g., road path 602) to the desired path 604 according to the following equation:

[0087] φ=sin(e 2d )Θ+cos(e 2d )Φ (Equation 12) and

[0088] θ=cos(e 2d )Θ-sin(e 2d )Φ (Equation 13),

[0089] Where Φ represents the road slope angle, Θ represents the road inclination angle, and e 2d This represents the desired path 604 relative to road path 602. Figure 6 The expected yaw angle is 606.

[0090] Refer again Figure 3 In various embodiments, the gradient distribution transitions in steps 314 and 316 are used to control the vehicle, both for lateral control (steps 318 and 320). In various embodiments, Figure 1 The processor 142 uses the converted slope distribution to provide adjustment and / or control commands to various vehicle components (e.g., braking system 106, steering system 108, and / or drive system 110) to adjust the lateral and longitudinal control of the vehicle 100 based on the projected slope angle and tilt angle on the predicted horizon that is gradually moving away from the road, for example, to provide a potentially smoother transition when the vehicle 100 travels along different sections of a road with variable slope angles and / or road tilt angles.

[0091] First, during step 318, in an exemplary embodiment, the lateral control of the vehicle 100 is adjusted using a lateral trajectory tracking model in conjunction with the following equation:

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] φ(x k )=[φ k φ k+1 φ k+2 …φ k+p (Equation 24), and

[0103]

[0104] Where: (i)e1 represents the lateral position error relative to the road; (ii)e2 represents the yaw angle error relative to the road; (iii)δ represents the front wheel steering angle; (iv) Indicates consideration of curvature The expected yaw rate point cloud on the predicted horizon; and (v)φ k+i Represents the point cloud of predicted road slope angle on the horizon, where the value of (i)ψ is in Figure 7 The text indicates that, and (ii) the value of φ is in... Figure 8 The middle represents (combined) Figure 5 (x-axis 520° and y-axis 530°). C af C ar These are the forward and backward turning coefficients, where m is the vehicle mass and I is the turning coefficient. z It is the moment of inertia, l f , l r These are the distances from the center of gravity to the front and rear axles, respectively, and δ is the vehicle's road wheel angle.

[0105] Continuing with step 318, in various embodiments, lateral control is based on the processing of multiple inputs, including: (i) the desired trajectory Y(x) = f(x) from the task / path planner; (ii) the vehicle path curvature (ρ); and (iii) the vehicle speed (v). x v y (iv) Inertial Measurement Unit (IMU) data (a x a y ω z (v) Torque applied by the driver (τ) driver (vi) steering angle (δ); (vii) authorization; (viii) driver overtaking; (ix) safety-critical slope descent request; (x) horizontal slope angle φ; and (xi) desired horizon curvature φ. Also in various embodiments, these various inputs (e.g., via...) Figure 1 The sensor array 120 obtained) was Figure 1 The processor 142 is used to generate steering commands for the lateral control of the vehicle 100 based on the following equation:

[0106]

[0107] Make:

[0108] and

[0109]

[0110] Where: (i)Ae+B1δ t It is based on model-driven dynamic error compensation; (ii) (iii) B3sin(φ) is the effect of the expected curvature on error dynamics; (iv) B3sin(φ) is the effect of the slope angle. This represents the uncertainty (to be estimated and compensated) in the error dynamics, and (v)α1e+α2δ≤c. This represents constraints on uncertainty realization and robust control, as well as constraints on performance, feel, comfort, and safety.

[0111] Furthermore, during step 320, in an exemplary embodiment, based on the... Figure 1 The sensor data input provided by the sensor array 120, combined with the following equation, is used to apply the longitudinal trajectory tracking model for longitudinal compensation (via the sensor array 120). Figure 1 The processor 142 provides instructions to adjust the longitudinal control of the vehicle 100:

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120] gθ(x k )=gcos(φ)[θ k θ k+1 θ k+2 …θk+p (Equation 37, and)

[0121]

[0122] Where: (i)T B / E (ii)a represents traction / braking torque; x (ii) v represents longitudinal acceleration; x Represents longitudinal velocity; (iii) (iv) Indicates the desired longitudinal velocity. Represents the desired longitudinal acceleration; and (v)θ k+i This represents a point cloud showing the predicted road tilt angle on the horizon, where the value of the road tilt angle θ is in... Figure 9 The text indicates that, in various embodiments, similar to equations 26, 27, and 28, acceleration and deceleration commands for vehicle longitudinal control can be generated.

[0123] In various embodiments, the method then terminates at step 322.

[0124] Therefore, methods, systems, and vehicles are provided for controlling a vehicle during road elevation changes. In various embodiments, camera data and map data are used to generate road slope angle and road tilt angle distributions along a gradually receding predicted horizon on the road the vehicle is traveling on. Also in various embodiments, converted versions of the road slope angle and road tilt angle distributions are used for lateral and longitudinal control of the vehicle, for example, to facilitate smooth transitions between road segments with different road slopes and / or road tilt angles.

[0125] It should be understood that the systems, vehicles, and methods may differ from those depicted in the accompanying drawings and described herein. For example, Figure 1 Vehicle 100 Figure 1 and Figure 2 The control system 102 and / or Figure 1 and Figure 2 Its components can vary in different embodiments. Similarly, it can be understood that the steps of process 300 can differ. Figure 3 The steps described herein, and / or the various steps of process 300, may occur simultaneously and / or in different ways. Figure 3 The order described in the text occurs. Similarly, it should be understood that... Figure 4-9 The various implementation methods can also differ in different embodiments.

[0126] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be understood that numerous variations exist. It should also be understood that the one or more exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient roadmap for implementing one or more exemplary embodiments. It should be understood that various changes can be made to the function and arrangement of the elements without departing from the scope of this disclosure as set forth in the appended claims and their legal equivalents.

Claims

1. A method for controlling a vehicle during road elevation changes, comprising: Sensor data is obtained from one or more sensors on the vehicle, including camera data from one or more cameras on the vehicle; Obtain location data related to the vehicle's position; Receive user input regarding the vehicle's destination; Obtain map data associated with the path corresponding to the location; Based on the user input and the location data, a planned task is generated to travel along the road associated with the path to the destination; Using a processor, sensor data, and map data, a road elevation distribution is generated on a gradually receding predicted horizon according to a planned task. The steps of controlling the vehicle include controlling the vehicle on the gradually receding predicted horizon using the predicted road elevation distribution based on instructions provided by the processor. and Based on instructions provided by the processor, the vehicle is actively controlled using predicted altitude distribution. The processor uses adaptive active control to provide instructions to the vehicle at various time points along the gradually receding horizon to adjust the vehicle's acceleration, braking, and / or steering. The road elevation distribution includes multiple slope angle and tilt angle data points obtained along different lanes and road segments of the road, including: (i) the first slope angle and tilt angle data point along the first lane of the road currently being traveled by the vehicle; (ii) a second slope angle and tilt angle data point corresponding to a second lane of the road, the second lane including a lane adjacent to the first lane; and (iii) a third slope angle and tilt angle data point along a third lane of the road, the third lane being two lanes away from the first lane.

2. The method according to claim 1, further comprising: The processor performs the conversion of the elevation distribution from road coordinates to vehicle coordinates, generating the converted elevation distribution.

3. The method of claim 2, wherein the step of controlling the vehicle comprises: Based on the converted altitude distribution, the lateral dynamics of the vehicle are controlled by instructions provided by the processor.

4. The method of claim 2, wherein the step of controlling the vehicle comprises: Based on the converted altitude distribution, the longitudinal dynamics of the vehicle are controlled by instructions provided by the processor.

5. A system for controlling a vehicle during road elevation changes, comprising: One or more sensors are configured to at least help obtain dynamic measurements and path information of the vehicle, including sensor data containing camera data, and to receive user input about the vehicle's destination. One or more location systems are configured to at least help obtain location data related to the vehicle's location; A computer memory is configured to store map data associated with a path corresponding to the location; and The processor is configured to at least promote: Based on the user input and the location data, a planned task is generated to travel along the road associated with the path to the destination; Using sensor and map data, a road elevation distribution is generated on a gradually receding predicted horizon according to the planned task. Based on instructions provided by the processor, the vehicle is controlled using this predicted road elevation distribution on the gradually receding predicted horizon. The processor provides instructions for controlling the vehicle using altitude distribution. At each time point, the processor uses adaptive active control to provide instructions to the vehicle at progressively receding horizons to adjust the vehicle's acceleration, braking, and / or steering. The road elevation distribution includes multiple slope angle and tilt angle data points obtained along different lanes and road segments of the road, including: (i) the first slope angle and tilt angle data point along the first lane of the road currently being traveled by the vehicle; (ii) a second slope angle and tilt angle data point corresponding to a second lane of the road, the second lane including a lane adjacent to the first lane; and (iii) a third slope angle and tilt angle data point along a third lane of the road, the third lane being two lanes away from the first lane.

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