DRIVER ACCELERATION LEARNING SYSTEM FOR A VEHICLE

Abstract

A computer-implemented procedure, when executed by data processing hardware, causes the data processing hardware to perform operations. These operations include receiving one or more vehicle parameters and environmental parameters in a driver acceleration learning application, estimating a specified vehicle speed based on at least one of the vehicle parameters and the environmental parameters, generating a speed differential based on the estimated specified vehicle speed, and estimating a requested torque based on at least one of the vehicle parameters and the environmental parameters.Furthermore, the operations include determining the longitudinal acceleration of the vehicle based on the estimated requested torque, generating tables of learned accelerations based on the speed difference and / or the requested torque and / or the longitudinal acceleration of the vehicle via the driver acceleration learning application, and replacing calibration tables with the generated tables of learned accelerations via the driver acceleration learning application.

Description

INTRODUCTION

[0001] The information given in this section serves to provide a general overview of the context of the disclosure. The work of the inventors mentioned herein, to the extent described in this section, as well as aspects of the description that cannot otherwise be considered prior art at the time of filing, are neither explicitly nor implicitly recognized as prior art with respect to the present disclosure.

[0002] The present disclosure relates generally to an acceleration system for a vehicle and in particular to a driver acceleration learning system for a vehicle.

[0003] Many vehicles are equipped with adaptive or active cruise control functions. For example, active cruise control can be used to set and maintain a vehicle speed with minimal input from the driver. The driver can set an active cruise control speed through manual manipulation or override it by braking or accelerating. Active cruise control is calibrated based on an average comfort setting established during manufacturing, meaning it is generally not individually tailored to the driver. The driver can only personalize the active cruise control by setting a specific vehicle speed.However, there is a need for an improved system that learns and adapts to a driver's preferences regarding acceleration.

[0004] DE 11 2017 001 948 T5 discloses a vehicle control device capable of improving driving behavior in a driving mode where a vehicle follows a vehicle ahead. Therefore, a vehicle control device according to the present invention corrects an acceleration determination parameter, used at the time when determining whether a vehicle accelerates or decelerates in a following driving mode, according to an acceleration / deceleration operation by a driver, and executes the following driving mode using the corrected acceleration determination parameter.

[0005] DE 10 2012 207 458 A1 discloses a system and a method which collect acceleration data for a driver for a vehicle, compare the acceleration data for the driver with a set of acceleration data representing several sample drivers driving in the same type of vehicle as the vehicle, and determine a driving style rating for the driver based on the comparison.

[0006] DE 10 2016 222 484 A1 discloses a method for the automated control of a vehicle. The method comprises: capturing vehicle motion data while a driver is controlling the vehicle or at least intervening in the automatic control of a vehicle control system of the vehicle, wherein the vehicle control system is configured to control the vehicle based on parameters that define the vehicle's driving behavior during automatic control; adjusting the parameters for the vehicle control system based on the motion data so that the vehicle's driving behavior during automatic control corresponds to the vehicle's driving behavior when controlled by the driver; and controlling the vehicle using the adjusted parameters via the vehicle control system. SUMMARY

[0007] According to some aspects, a computer-implemented procedure, when executed by data processing hardware, causes the data processing hardware to perform operations. These operations include receiving one or more vehicle parameters and environmental parameters in a driver acceleration learning application, estimating a specified vehicle speed based on at least one of the vehicle parameters and the environmental parameter, generating a speed differential based on the estimated specified vehicle speed, and estimating a requested torque based on at least one of the vehicle parameters and the environmental parameter.Furthermore, the operations include determining the vehicle's longitudinal acceleration based on the estimated requested torque, generating tables of learned accelerations based on the speed difference and / or the requested torque and / or the vehicle's longitudinal acceleration via the driver acceleration learning application, and replacing calibration tables with the generated tables of learned accelerations via the driver acceleration learning application. The tables of learned accelerations contain a table of learned average accelerations and a table of learned maximum accelerations. The calibration tables contain an acceleration request table and a table of calibrated maximum accelerations. The operations also include determining an average ratio based on the tables of learned accelerations and the calibration tables.Furthermore, the operations include generating a table of scaled accelerations based on the determined average ratio.

[0008] Replacing the calibration tables with the learned acceleration tables involves determining, via the driver acceleration learning application, whether to use the learned average acceleration table or the scaled acceleration table.

[0009] Optionally, replacing the calibration tables with the learned acceleration tables may involve replacing the calibrated maximum acceleration table with the learned maximum acceleration table. In some cases, the vehicle parameters may include speed parameters. According to some examples, generating the speed difference may involve generating the speed difference based on the speed parameters. Furthermore, the operations may include generating the learned acceleration tables based on the speed difference via the driver acceleration learning application.

[0010] In other respects, a vehicle includes a driver acceleration learning system. The driver acceleration learning system comprises data processing hardware and memory hardware that communicates with the data processing hardware. The memory hardware stores instructions which, when executed by the data processing hardware, cause the data processing hardware to perform operations. These operations include receiving one or more vehicle parameters and environmental parameters in a driver acceleration learning application, estimating a predetermined vehicle speed based on at least one of the vehicle parameters and the environmental parameter, and generating a speed differential based on the estimated predetermined vehicle speed.Furthermore, the operations include estimating a requested torque based on at least one of the vehicle parameters and the environmental parameters, and determining the vehicle's longitudinal acceleration based on the estimated requested torque. The operations also include generating tables of learned accelerations based on the speed difference and / or the requested torque and / or the vehicle's longitudinal acceleration via the driver acceleration learning application, and replacing calibration tables with the generated tables of learned accelerations via the driver acceleration learning application. The tables of learned accelerations contain a table of learned average accelerations and a table of learned maximum accelerations. The calibration tables contain an acceleration request table and a table of calibrated maximum accelerations.The operations further include determining an average ratio based on the tables of learned accelerations and the calibration tables. They also include generating a table of scaled accelerations based on the determined average ratio. Replacing the calibration tables with the tables of learned accelerations involves determining, via the driver acceleration learning application, whether to use the table of learned average accelerations or the table of scaled accelerations.

[0011] Optionally, replacing the calibration tables with the learned acceleration tables may involve replacing the calibrated maximum acceleration table with the learned maximum acceleration table. In some cases, the vehicle parameters may include speed parameters.

[0012] In other respects, a driver acceleration learning system for a vehicle includes data processing hardware and memory hardware that communicates with the data processing hardware. The memory hardware stores instructions which, when executed by the data processing hardware, cause the data processing hardware to perform operations. These operations include receiving one or more vehicle parameters and environmental parameters in a driver acceleration learning application, estimating a predetermined vehicle speed based on at least one of the vehicle parameters and the environmental parameter, and generating a speed difference based on the estimated predetermined vehicle speed.Furthermore, the operations include estimating a requested torque based on at least one of the vehicle parameters and the environment parameters, determining a longitudinal acceleration of the vehicle based on the estimated requested torque, and generating tables of learned accelerations based on the velocity difference and / or the requested torque and / or the longitudinal acceleration of the vehicle via the driver acceleration learning application, wherein the tables of learned accelerations include a table of learned average accelerations and a table of learned maximum accelerations.Furthermore, the operations include replacing a table of calibrated maxima from the calibration tables of a cruise control system with the generated table of learned maximum accelerations via the driver acceleration learning application, determining an average ratio based on the tables of learned accelerations and the calibration tables, and generating a table of scaled accelerations based on the determined average ratio.

[0013] According to some examples, the operations can include determining whether to use the learned average acceleration table or the scaled table via the driver acceleration learning application. Optionally, the vehicle parameters can include speed parameters, and generating the speed difference can include generating the speed difference based on the speed parameters. Furthermore, the operations can include generating the learned acceleration tables based on the speed difference via the driver acceleration learning application. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings described here serve only to illustrate selected configurations and are not intended to limit the scope of protection of the present disclosure; they show: Fig. 1 a schematic representation of a vehicle equipped with a driver acceleration learning system according to the present disclosure; Fig. 2 an exemplary block diagram of a driver acceleration learning system according to the present disclosure; Fig. 3 another block diagram of the driver acceleration learning system Fig. 2; Fig. 4 another block diagram of the driver acceleration learning system Fig. 2; Fig. 5 an exemplary flowchart of a driver acceleration learning system according to the present disclosure; Fig. 6 another exemplary flowchart of the driver acceleration learning system Fig. 5; Fig. 7 a further exemplary flowchart of a driver acceleration learning system according to the present disclosure; and Fig. 8 an exemplary flowchart of a procedure for carrying out a driver acceleration learning system in accordance with the present disclosure.

[0015] Corresponding reference symbols throughout the drawings denote corresponding parts. DETAILED DESCRIPTION

[0016] Exemplary configurations are now described in more detail with reference to the accompanying drawings. Exemplary configurations are given to ensure that this disclosure is thorough and fully conveys the scope of protection of the disclosure to the person skilled in the art. To provide a thorough understanding of the configurations of this disclosure, specific details such as examples of specific components, devices, and processes are set forth. It is clear to the person skilled in the art that specific details need not be used, that exemplary configurations can be embodied in many different forms, and that the specific details and the exemplary configurations are not to be understood as limiting the scope of protection of the disclosure.

[0017] The terminology used here serves only to describe certain exemplary configurations and is not intended to be restrictive. Unless the context clearly indicates otherwise, the singular articles "a," "an," and "that," as used here, are intended to include the plural forms. The terms "includes," "comprehensive," "containing," and "exhibiting" are inclusive and thus specify the presence of features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more further features, steps, operations, elements, components, and / or groups thereof. Unless a specific order of execution is given, the procedural steps, processes, and operations described herein should not be understood as requiring their execution in the particular order discussed or presented.Additional or alternative steps can be used.

[0018] When an element or layer is described as "on," "interacting with," "connected with," "attached to," or "coupled with" another element or layer, it may be directly on, interacting with, connected with, attached to, or coupled with the other element or layer, or there may be intermediate elements or layers. Conversely, no intermediate elements or layers may be present when an element is described as "directly on," "directly interacting with," "directly connected with," "directly attached to," or "directly coupled with" another element or layer. Other words used to describe the relationship between elements (e.g., "between" versus "directly between," "adjacent to" versus "directly adjacent to," etc.) are to be interpreted in the same way.As the term “and / or” is used here, it includes any combination of one or more of the associated listed objects.

[0019] The terms "first," "second," "third," etc., may be used here to describe different elements, components, areas, layers, and / or sections. These elements, components, areas, layers, and / or sections are not intended to be limited by these terms. These terms may only be used to distinguish one element, component, area, layer, or section from another. Unless clearly indicated by the context, terms such as "first," "second," and other numerical terms do not imply a sequence or order.Thus, a first element, a first component, a first area, a first layer or a first section discussed below could be referred to as a second element, a second component, a second area, a second layer or a second section without deviating from the lessons of the exemplary configurations.

[0020] In this application, including in the following definitions, the term "module" may be replaced by the term "circuit". The term "module" may refer to, be a part of, or include an application-specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combination logic circuit; a free programmable logic array (FPGA); a processor (shared, dedicated, or group) that executes code; memory (shared, dedicated, or group) that stores code executed by a processor; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as in a system-on-a-chip.

[0021] The term "code," as used above, can include software, firmware, and / or microcode, and can refer to programs, routines, functions, classes, and / or objects. The term "shared processor" includes a single processor that executes some or all of the code from multiple modules. The term "group processor" includes a processor that executes some or all of the code from one or more modules along with additional processors. The term "shared memory" includes a single memory that stores some or all of the code from multiple modules. The term "group memory" includes memory that stores some or all of the code from one or more modules along with additional memory. The term "memory" can be a subset of the term "computer-readable medium."The term "computer-readable medium" excludes transitory electrical and electromagnetic signals propagating through a medium and can therefore be considered a concrete and non-transient storage medium. Non-restrictive examples of non-transient storage include a concrete computer-readable medium that encompasses non-volatile storage, magnetic storage, and optical storage.

[0022] The devices and methods described in this application can be implemented in whole or in part by one or more computer programs executed by one or more processors. The computer programs contain instructions executable by a processor, stored on at least one non-transitory, concrete, computer-readable medium. Furthermore, the computer programs can contain and / or rely on stored data.

[0023] A software application (i.e., a software resource) can refer to computer software that causes a computer device to perform a task. Depending on the context, a software application may be called an "application," an "app," or a "program." Examples of applications include, but are not limited to, system diagnostics applications, system management applications, system maintenance applications, word processing applications, spreadsheet applications, messaging applications, media streaming applications, social networking applications, and gaming applications.

[0024] Non-transitory memory can be physical devices used to store programs (e.g., sequences of instructions) or data (e.g., program state information) on a temporary or permanent basis for use by a computer device. Non-transitory memory can be volatile and / or non-volatile addressable semiconductor memory. Examples of non-volatile memory include, but are not limited to, flash memory and read-only memory (ROM) / programmable read-only memory (PROM) / erasable programmable read-only memory (EPROM) / electronically erasable programmable read-only memory (EEPROM) (which is commonly used, for example, for firmware such as boot programs).Examples of volatile memory include, but are not limited to, read / write memory (RAM), dynamic read / write memory (DRAM), static read / write memory (SRAM), phase change memory (PCM), disks or tapes.

[0025] These computer programs (also known as programs, software, software applications, or code) contain machine instructions for a programmable processor and can be implemented in a higher-level procedural and / or object-oriented programming language and / or in assembly language / machine language. As used here, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, any non-transitory computer-readable medium, any device, and / or any apparatus (e.g., magnetic disks, optical disks, memory, programmable logic devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including machine-readable medium that receives machine instructions as a machine-readable signal.The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0026] Various implementations of the systems and techniques described herein can be realized in a digital electronic and / or optical circuit arrangement, in an integrated circuit arrangement, in specially designed ASICs (application-specific integrated circuits), in computer hardware, in computer firmware, in computer software, and / or in combinations thereof. These various implementations may include implementations in one or more computer programs that are executable and / or interpretable in a programmable system that contains at least one programmable processor, which may be a special-purpose or general-purpose processor, coupled to a storage system, at least one input device, and at least one output device for receiving data and instructions from and sending data and instructions to a storage system.

[0027] The processes and logic sequences described in this description can be executed by one or more programmable processors, also known as data processing hardware, which run one or more computer programs to perform functions by processing input data and generating output. Alternatively, the processes and logic sequences can be executed by a specialized logic circuit arrangement, such as an FPGA (free programmable logic assembly) or an ASIC (application-specific integrated circuit). Processors suitable for executing a computer program include, for example, general-purpose and specialized microprocessors, as well as any type of digital computer processor(s). Generally, a processor receives instructions and data from read-only memory, read / write memory, or both.The essential elements of a computer are a processor for executing instructions and one or more storage devices for storing instructions and data. Generally, a computer also includes one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or is functionally coupled to them to receive data from them, send data to them, or both. However, a computer need not have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices; magnetic disks such as internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.The processor and memory can be supplemented by or integrated into a special logic circuit arrangement.

[0028] To provide interaction with a user, one or more aspects of the disclosure can be implemented in a computer that has a display device, such as a CRT (cathode ray tube) monitor, an LCD (liquid crystal display) monitor, or a touchscreen, for displaying information to the user, and optionally a keyboard and pointing device, such as a mouse or trackball, by which the user can provide input to the computer. Other types of devices can also be used to provide interaction with a user; for example, feedback provided to the user can be any form of sensory feedback, such as...This feedback can be visual, audible, or tactile; and input can be received from the user in any form, including acoustic, speech, or keystroke input. Furthermore, a computer can interact with a user by sending documents to and receiving documents from a device used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received by the web browser.

[0029] Based on Fig. 1-4 includes a driver acceleration learning system 10 and a controller 12 configured with a cruise control system 14. The cruise control system 14 is configured for a vehicle 100 to control the acceleration of the vehicle 100 and to maintain a speed set by a driver of the vehicle 100. The cruise control system 14 includes a driver learning application 16 configured to automatically adjust the acceleration of the vehicle 100 using a sensor system 200 and / or a navigation system 300 with which the vehicle 100 is equipped. The sensor system 200 may, for example, include a speed sensor 202 and an image sensor 204, which can provide speed data 206 and image data 208 to the cruise control system 14 for use by the driver acceleration learning application 16.

[0030] In some cases, the speed sensor 202 may be a torque sensor and / or an accelerometer integrated into the vehicle 100, so that the speed data 206 may include torque data 206a and / or acceleration data 206b. The image data 208 may include the vehicle 100's environment, including, but not limited to, road gradient, road surface, road type, traffic conditions, and any other environmental data captured by the image sensor 204. The image sensor 204 may include, but is not limited to, a camera, light detection and distance measurement (LiDAR) device, radar, or any other practical imaging device. The vehicle 100 may be equipped with one or more of both the speed sensor 202 and the image sensor 204.

[0031] Furthermore, the vehicle 100 can be equipped with the navigation system 300, which can provide navigation data 302 to the controller 12. The navigation data 302 can also provide information regarding environmental conditions surrounding the vehicle 100 or areas through which the vehicle 100 can travel. The controller 12 can receive and use the image data 208 and the navigation data 302 to inform the driver acceleration learning application 16, which is described in more detail below. The controller 12 is also configured with data processing hardware 18 and storage hardware 20. The data processing hardware 18 is configured to run the cruise control system 14, including the driver acceleration learning application 16.The storage hardware 20 communicates with the data processing hardware 18 and stores instructions which, when executed in the data processing hardware 18, cause the data processing hardware 18 to perform the operations described herein. Furthermore, the storage hardware 20 is configured to store vehicle parameters 22 and to communicate these vehicle parameters 22 with the data processing hardware 18.

[0032] Some of the vehicle parameters 22 are stored in the memory hardware 20, while others are obtained from the sensor data 206 and / or the image data 208 and processed by the data processing hardware 18. For example, the memory hardware 20 stores an acceleration limit 24, a vehicle mass 26, and a tire radius 28. In some cases, the acceleration limit 24 can be set or otherwise modified by a user of the vehicle 100. In other cases, the acceleration limit 24 can be preset. The controller 12 can estimate the vehicle mass 26 and / or the tire radius 28, or it can have preconfigured the vehicle mass 26 and / or the tire radius 28 in the memory hardware 20.Furthermore, the vehicle parameters 22 include speed parameters 30, which can be determined by the data processing hardware 18 based on other vehicle parameters 22 and / or the speed data 206 from the sensor system 200. The speed parameters 30 are used to generate a speed difference 32. The speed parameters 30 can include, for example, a speed range 30a, a longitudinal acceleration 30b of the vehicle 100, an estimated target speed 30c, and an estimated fixed speed 30d. Additionally, the vehicle parameters 22 include a requested torque 34, which is detected from the speed data 206 and / or from the speed sensors 202 mentioned above.

[0033] The speed range 30a is determined based on the speed data 206 as a range of speeds of the vehicle 100 over a predetermined time period, which can be communicated at least partially by the longitudinal acceleration 30b of the vehicle 100. The driver acceleration learning application 16 can be used by the data processing hardware 18 to estimate the target speed 30c based on other vehicle parameters 22 and environmental parameters 36. The environmental parameters 36 are configured based on the image data 208 and / or the navigation data 302.

[0034] The estimated set speed 30d is also a function of the environmental parameters 36. The estimated set speed 30d can vary, for example, depending on the road type, the speed limit, and / or the traffic, which are detected by the sensor system 200 and the navigation system 300 and stored as the environmental parameters 36. Furthermore, the driver acceleration learning application 16 monitors, for example, the requested torque 34 of the vehicle parameters 22 to estimate the set speed 30d. The requested torque 34 is a function of the acceleration applied by a driver of the vehicle 100 and is detected by the speed sensor 202 as part of the speed data 206.The driver acceleration learning application 16 records the requested torque 34 and uses the vehicle mass 26, the tire radius 28 and the environmental parameters 36 to capture the requested torque 34 at different speeds within the speed range 30a.

[0035] The driver acceleration learning application 16 receives the vehicle parameters 22 and the environmental parameters 36 and estimates the set speed 30d, which is used to generate the speed difference 32 from the vehicle parameters 22. The speed difference 32 can be generated based on the set speed 30d and the estimated target speed 30c relative to the speed range 30a. Furthermore, the vehicle parameters 22 and the environmental parameters 36 can be used to estimate the requested torque 34, which can be used by the cruise control system 14 to determine the longitudinal acceleration 30b. The vehicle parameters 22 and the environmental parameters 36 are also used by the driver acceleration learning application 16 to generate tables 40 of learned accelerations. The tables 40 of learned accelerations can, for example,at least partially based on the speed difference 32, the requested torque 34 and the longitudinal acceleration 30b of the vehicle 100.

[0036] The tables 40 of learned accelerations contain a table 40a of learned average accelerations and a table 40b of learned maximum accelerations. The driver learning application also contains 16 driver acceleration profiles 50, which include a driver identification (driver ID) 52 and acceleration preferences 54. The driver acceleration profiles 50 can be used by the driver acceleration learning application 16 to estimate the target speed 30c and the set speed 30d based on the acceleration preferences 54 assigned to the driver ID 52. The cruise control system also contains 14 calibration parameters 60, which include calibration tables 62. The calibration tables 62 are used by the driver acceleration learning application 16 when comparing the learned accelerations with the tables 40. The calibration tables 60 contain, for example,An acceleration request table 62a and a table 62b of calibrated maximum accelerations are provided. Additionally, the calibration parameters 60 contain a vehicle acceleration 64, which is derived from the velocity parameters 30 of the vehicle parameters 22. Furthermore, the driver acceleration learning application 16 uses both the tables 40 of learned accelerations and the calibration tables 62 to generate tables 70 of scaled accelerations based on an average ratio 72. The average ratio 72 is used by the driver acceleration learning application 16 to generate the tables 70 of scaled accelerations.

[0037] Further based on Fig. 1-4 The driver acceleration learning system 10 can be operated during the operation of the vehicle 100 in such a way that the driver acceleration learning application 16 continuously monitors and learns the acceleration preferences 54 of a respective driver ID 52 based on the speed data 206 received from the speed sensor 202. For example, each driver ID 52 is assigned a respective driver acceleration profile 50, so that the driver acceleration learning application 16 knows, based on the driver ID 52, which acceleration preferences 54 to implement. The driver ID 52 can be assigned to a key fob, an online profile, or any other means of identifying a specific driver. In some cases, the driver ID 52 can be determined by the image data 208 from an interior image sensor 204, which is capable of capturing image data 208 of a driver of the vehicle 100.According to some examples, a user can enter some acceleration preferences 54 into the driver acceleration profile 50.

[0038] The driver acceleration learning application 16 automatically monitors the vehicle parameters 22 and the environmental data 36 to continuously estimate the set speed 30d and the target speed 30c. The estimated set speed 30d and the target speed 30c can be used by the driver acceleration learning application 16 to establish the acceleration preferences 54 of the driver acceleration profile 50. The driver acceleration learning application 16 records the requested torque 34 and the speed difference 32 over the speed range 30a during manual operation (i.e., driving) of the vehicle 100 to record the acceleration preferences 54 for the driver acceleration profile 50. The cruise control system 14 can, for example, record a maximum requested torque 34 at each speed range 30a during manual operation of the vehicle 100.Therefore, the driver acceleration learning application 16 is only operable during manual operation of the vehicle 100 and is inoperative during an active cruise control function of the vehicle 100. Furthermore, the driver acceleration learning application 16 can be operated if a driver overrides the longitudinal acceleration 30b of the vehicle 100, allowing the vehicle 100 to exceed a speed set as part of an active cruise control function of the vehicle 100.

[0039] The driver acceleration learning application 16 is configured to monitor a driver's behavior by monitoring the requested torque 34 and the speed difference 32 at various speed ranges 30a in order to determine the driver's acceleration preferences 54. The driver acceleration learning application 16 is configured to convert the requested torque 34 into an acceleration for compatibility with the cruise control system 14. For example, the longitudinal acceleration 30b of the vehicle 100 is based on a fraction of the requested torque 34, the tire radius 28, and the vehicle mass 26 when a road gradient 36 is removed. The driver acceleration learning application 16 populates Table 40b with learned maximum accelerations based on the maximum requested torque 34a.Table 40a of learned average accelerations is based on the velocity difference 32 and the average requested torque 34a.

[0040] Once the tables of learned accelerations (40) have been populated, the Driver Acceleration Learning Application 16 can replace the calibration tables (60) with the tables of learned accelerations (40). For example, the Driver Acceleration Learning Application 16 can replace the table of calibrated maximum accelerations (62b) with the table of learned maximum accelerations (40b), and the acceleration request table (62a) with the table of learned average accelerations (40a). As mentioned above, the Driver Acceleration Learning Application 16 can create an average ratio (72) by generating a ratio between the tables of learned accelerations (40) and the calibration tables (62). For example, before replacing the acceleration request table (62a), the Driver Acceleration Learning Application 16 can determine whether to use the table of learned average accelerations (40a) or the scaled table (70).In some cases, the driver acceleration learning application 16 can use the Tables 70 of scaled accelerations. The Tables 70 of scaled accelerations are the calibration tables 62, which are scaled over the average ratio 72 relative to the Tables 40 of learned accelerations.

[0041] Further based on Fig. 1-4 uses the driver acceleration learning application 16 and the acceleration limit 24 to enforce a maximum acceleration 24a for the vehicle 100 such that the acceleration preferences 54 cannot exceed the maximum acceleration 24a and / or the acceleration limit 24 of the vehicle 100. The acceleration limit 24 can also be communicated by limits of a power unit of the vehicle 100. The vehicle 100 can be configured as an electric vehicle, a hybrid vehicle, and / or a vehicle equipped with an internal combustion engine (ICE). The power unit components can set an acceleration limit 24 that can affect the acceleration capability of the vehicle 100.

[0042] Now based on Fig. Figure 5 shows an exemplary flowchart of the driver acceleration learning system 10. At 500, the driver acceleration learning application 16 is initiated. At 502, the driver acceleration learning application 16 determines whether active cruise control of the cruise control system 14 is active. If active cruise control is active, the controller 12 monitors at 504 for the selection of a learned driver acceleration profile 50. Based on the selection of a driver acceleration profile 50, the controller 12 implements and uses the acceleration preferences 54 of the driver acceleration profile 50.

[0043] If active cruise control is inactive, the driver acceleration learning application 16 monitors the driving of vehicle 100 and, at 510, quantifies the acceleration of vehicle 100 based on vehicle parameters 22 and environmental parameters 36. At 512, the driver acceleration learning application 16 synthesizes the vehicle and environmental parameters 22 and 36, and at 514, it builds the tables 40 of learned accelerations.

[0044] Now in Fig. Figure 6 shows an exemplary flowchart of the driver acceleration learning system 10. At 600, the controller 12 determines whether the cruise control system 14 is inactive. If the cruise control system 14 is active, the controller 12 continues to monitor whether the cruise control system 14 is inactive or whether the driver overrides the cruise control system 14. If the cruise control system 14 is inactive or the driver overrides the cruise control system 14, at 602, the driver acceleration learning application 16 estimates the set speed 30d of the vehicle 100 based on the vehicle parameters 22 and the environmental parameters 36. At 604, the driver acceleration learning application 16 estimates a speed difference 32 based on the estimated set speed 30d and the estimated target speed 30c, and at 606, it estimates the requested acceleration.The requested acceleration corresponds to the requested torque 34 and is estimated based on the vehicle parameters 22 and the environmental parameters 36. At 608, the driver acceleration learning application 16 records the estimated longitudinal acceleration 30b, determined on the basis of the estimated velocity difference 32 and the estimated requested acceleration. At 610, the driver acceleration learning application 16 generates the tables 40 of learned accelerations, which include table 40a of learned average accelerations and table 40b of learned maximum accelerations.

[0045] Now in Fig. Figure 7 shows a continuation flow diagram of the driver learning acceleration 10. At 700, the controller 12 determines whether the driver acceleration learning application 16 is enabled. If the driver acceleration learning application 16 is enabled, the controller 12 receives the tables of learned accelerations at 702 and determines at 704 whether the table data is stable. If the table data is not stable, the controller 12 returns to monitoring the driver acceleration learning application 16. If the table data is stable, the controller 12 replaces the table of calibrated maximum accelerations (Table 62b) with the learned maximum accelerations (Table 40b) at 706. At 708, the driver acceleration learning application 16 determines the average ratio (APR 72) for the table of scaled accelerations. At 710, the driver acceleration learning application 16 determines whether Table 70 of scaled accelerations should be used.If the Driver Acceleration Learning Application 16 determines that Table 70 of scaled accelerations should not be used, the Driver Acceleration Learning Application 16 replaces Table 62a of calibrated average accelerations at 712 with Table 40a of learned average accelerations. If the Driver Acceleration Learning Application 16 determines that Table 70 of scaled accelerations should be used, the Driver Acceleration Learning Application 16 replaces Table 62a of calibrated average accelerations at 714 with Table 70 of scaled accelerations.

[0046] Now based on Fig.Figure 8 shows an exemplary procedure 800 for the driver acceleration learning system 10. In Figure 802, the driver acceleration learning application 16 receives one or more vehicle parameters 22 and environmental parameters 36, and in Figure 804, it estimates a fixed speed 30d of a vehicle 100 based on at least one of the vehicle parameters 22 and the environmental parameters 36. In Figure 806, the driver acceleration learning application 16 generates a speed difference 32 based on the estimated fixed speed 30d of the vehicle 100, and in Figure 808, it estimates a requested torque 34 based on at least one of the vehicle parameters 22 and the environmental parameters 36. In Figure 810, the driver acceleration learning application 16 determines a longitudinal acceleration 30b of the vehicle 100 based on the estimated requested torque 34.In 812, the driver acceleration learning application 16 generates 100 tables of learned accelerations based on the speed difference 32 and / or the requested torque 34 and / or the longitudinal acceleration 30b of the vehicle. The tables of learned accelerations contain a table 40a of learned average accelerations and a table 40b of learned maximum accelerations. In 814, the driver acceleration learning application replaces a table 62b of calibrated maxima from the calibration tables 62 of a cruise control system 14 with the generated table 62b of learned maximum accelerations, and in 816, it determines an average ratio 72 based on the tables of learned accelerations and the calibration tables 62. In 818, the driver acceleration learning application 16 generates a table 70 of scaled accelerations based on the determined average ratio 72.

[0047] Several implementations have been described. However, it should be understood that various modifications can be made without deviating from the inventive concept and scope of protection as disclosed. Accordingly, other implementations fall within the scope of protection of the following claims.

[0048] The foregoing description is given for illustrative and descriptive purposes only. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular configuration are generally not restricted to that particular configuration, but are, where applicable, interchangeable and may be used in a selected configuration, even if it is not specifically shown or described. Furthermore, it may be modified in many ways. Such modifications are not considered a derogation from the disclosure, and all such modifications are intended to be included within the scope of protection of the disclosure.

Claims

[1] Computer-implemented method which, when executed by data processing hardware (18), causes the data processing hardware (18) to perform operations which include: Receiving one or more vehicle parameters and environmental parameters in a driver acceleration learning application (16); Estimating a specified speed of a vehicle (100) based on at least one of the vehicle parameters and the environmental parameters; Generating a speed difference based on the estimated fixed speed of the vehicle (100); Estimating a requested torque based on at least one of the vehicle parameters and the environmental parameters; Determining the longitudinal acceleration of the vehicle (100) based on the estimated requested torque; Generating tables of learned accelerations based on the velocity difference and / or the requested torque and / or the longitudinal acceleration of the vehicle (100) via the driver acceleration learning application (16); and Replacing calibration tables with the generated tables of learned accelerations via the driver acceleration learning application (16); wherein the tables of learned accelerations include a table of learned average accelerations and a table of learned maximum accelerations, and wherein the calibration tables include an acceleration request table and a table of calibrated maximum accelerations; Determining an average ratio based on the tables of learned accelerations and the calibration tables; Generating a table of scaled accelerations based on the determined average ratio; and where replacing the calibration tables with the learned acceleration tables includes determining whether to use the learned average acceleration table or the scaled acceleration table via the driver acceleration learning application (16). [2] Method according to claim 1, wherein replacing the calibration tables with the learned acceleration tables includes replacing the calibrated maximum acceleration table with the learned maximum acceleration table. [3] Method according to claim 1, wherein the vehicle parameters include speed parameters. [4] Method according to claim 3, wherein generating the velocity difference includes generating the velocity difference based on the velocity parameters. [5] Method according to claim 4, further comprising generating the tables of learned accelerations based on the speed difference via the driver acceleration learning application (16). [6] Vehicle (100) equipped with a driver acceleration learning system (10) configured to perform the method according to claim 1.

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