Modal Control Method for an Autonomous Driving Vehicle with Self-Diagnosis Function and Chassis Control Module

Abstract

A modal control method for an autonomous vehicle estimates a plurality of tire force margin data sets respectively corresponding to a plurality of wheels and a plurality of dynamic chassis margin data sets respectively corresponding to the wheels, and determines whether the autonomous vehicle can travel according to a decision instruction. When it is determined that the autonomous vehicle cannot travel according to the decision instruction, a request signal indicating adjustment of the decision instruction is output to a decision module. When the chassis control module does not receive an adjusted decision instruction, according to the tire force margin data set, the dynamic chassis margin data set, a plurality of required sideslip angles, and an actuator margin signal group calculated from the lever center of mass of the autonomous vehicle, an actuator controller module causes a dynamic drive system to drive the autonomous vehicle to move according to the actuator margin signal group.

Description

Technical Field

[0001] The present invention relates to a vehicle mode control method, and particularly to a mode control method for an autonomous vehicle and a chassis control module. Background Art

[0002] With the development of autonomous vehicle technology, many methods have been designed to plan autonomous driving routes. According to the definition of the Society of Automotive Engineers (SAE International), commercial vehicles can be classified from level 0 to level 5, ranging from fully manual (i.e., no automation) at level 0 and requiring manual control to provide dynamic driving, to fully automatic without manual control at level 5. In more advanced vehicles (such as levels 3 to 5, which can also be referred to as autonomous vehicles), planning the route of an autonomous vehicle may require multiple considerations (such as objects around the vehicle, road conditions, etc.). The planned route is converted into multiple instructions and sent to the vehicle controller unit of the autonomous vehicle for execution.

[0003] However, the chassis of an autonomous vehicle may not have the ability to execute certain instructions (such as sharp turns, sudden braking, etc.). Therefore, the actual mode of an autonomous vehicle may be different from the planned route. In addition, some instructions may cause discomfort to the people sitting in the vehicle (such as the driver, passengers, etc.). Summary of the Invention

[0004] The object of the present invention is to provide a method that can alleviate at least one shortcoming of the prior art.

[0005] The mode control method for an autonomous vehicle of the present invention, the autonomous vehicle includes a chassis, a dynamic drive system, an information platform, a decision-making module, a chassis control module, and an actuator controller module. The dynamic drive system includes multiple wheels, motors, a steering subsystem, a power system, and a braking subsystem installed on the chassis of the autonomous vehicle. The information platform includes multiple sensor groups arranged on the wheels and multiple parts of the autonomous vehicle. The sensor groups are used to obtain multiple vehicle dynamic data sets when the autonomous vehicle is driving. The decision-making module is used to calculate and generate decision instructions for controlling the autonomous vehicle. The chassis control module is coupled with the information platform and the decision-making module. The actuator controller module is coupled with the chassis control module and the dynamic drive system. The method includes the following steps:

[0006] (A) The chassis control module performs vehicle operation margin estimation based on the vehicle dynamic data set and multiple executable performance values of the dynamic drive system, so as to estimate a tire force margin data set corresponding to each of the wheels and a dynamic chassis margin data set corresponding to each of the wheels;

[0007] (B) After receiving a decision instruction from the decision module, the chassis control module calculates the required performance values of the multiple decision instructions, and determines whether the autonomous vehicle can travel according to the decision instruction based on the tire force margin data set and the dynamic chassis margin data set;

[0008] (C) When it is determined that the autonomous vehicle can travel, the chassis control module calculates an actuation signal group according to the decision instruction, and outputs the actuation signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the decision instruction to move the autonomous vehicle;

[0009] (D) When it is determined that the autonomous vehicle cannot travel, the chassis control module outputs a request signal indicating adjustment of the decision instruction to the decision module; and

[0010] (E) After step (D), when the chassis control module does not receive an adjusted decision instruction within a predetermined time period, the chassis control module estimates the required sideslip angles corresponding to each of the wheels, and calculates an actuation margin signal group according to the tire force margin data set, the dynamic chassis margin data set, the required sideslip angles, and the lever center of mass of the autonomous vehicle, and outputs the actuation margin signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the actuation margin signal group to move the autonomous vehicle.

[0011] The modal control method of the autonomous vehicle of the present invention further includes the following steps:

[0012] (F) The chassis control module performs a preliminary inspection on the dynamic drive system and the chassis control module; and

[0013] (G) When the preliminary inspection indicates that either the dynamic drive system or the chassis control module is in either a disabled state or an abnormal state, the chassis control module generates a stop actuation signal and outputs the stop actuation signal to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system to stop the autonomous vehicle.

[0014] For the modal control method of the autonomous driving vehicle of the present invention, after step (D), when the chassis control module receives the adjusted decision instruction within the predetermined time period, the chassis control module repeats step (C) for the adjusted decision instruction.

[0015] For the modal control method of the autonomous driving vehicle of the present invention, in step (A),

[0016] The vehicle dynamic data set used to estimate the tire force margin data set includes a plurality of forces applied to the tire along the vertical axis, longitudinal axis, and lateral axis respectively;

[0017] The chassis control module also calculates the aligning torque calculated from the vehicle dynamic data set and the road adhesion coefficient calculated according to the aligning torque; and

[0018] The chassis control module also estimates the tire force margin data set according to the aligning torque and the road adhesion coefficient.

[0019] For the modal control method of the autonomous driving vehicle of the present invention, in step (A), the dynamic chassis margin data set includes:

[0020] A steering margin sub-data set having an angular velocity margin and a current-torque margin;

[0021] A motor power margin sub-data set having a speed margin and an acceleration margin; and

[0022] A brake torque margin sub-data set having a pressure margin and a brake pressure.

[0023] For the modal control method of the autonomous driving vehicle of the present invention, the decision instruction includes a plurality of waypoints and a specified speed, and step (B) includes the following sub-steps:

[0024] (B-1) By means of the chassis control module, perform polynomial curve fitting on the waypoints to obtain a specified trajectory curve;

[0025] (B-2) By means of the chassis control module, determine the curvature of the specified trajectory curve; and

[0026] (B-3) By means of the chassis control module, calculate the required performance value at least according to the specified speed and the curvature of the specified trajectory curve.

[0027] For the modal control method of the autonomous driving vehicle of the present invention, the required performance value includes a longitudinal speed, and determining whether the autonomous driving vehicle can travel according to the decision instruction in step (B) includes the following sub-steps:

[0028] (B-4) Determine the longitudinal speed from the specified speed by means of the chassis control module;

[0029] (B-5) Calculate the lateral acceleration margin by means of the chassis control module according to the steering margin sub-dataset and the motor power margin sub-dataset;

[0030] (B-6) Calculate the longitudinal speed threshold according to the lateral acceleration margin calculated in (B-5) and the curvature of the specified trajectory curve; and

[0031] (B-7) Compare the longitudinal speed threshold and the longitudinal speed. When the longitudinal speed threshold is greater than the longitudinal speed, determine that the autonomous vehicle can travel.

[0032] For the modal control method of the autonomous vehicle of the present invention, the required performance value includes the applied steering angle, and step (C) includes the following sub-steps:

[0033] (C-1) Generate a steering command by means of the chassis control module according to the applied steering angle and the specified speed;

[0034] (C-2) Input the specified speed into a proportional-integral-derivative controller by means of the chassis control module to obtain an applied acceleration related to one of the power system and the braking subsystem, and generate an acceleration command indicating the applied acceleration; and (C-3) Generate the actuation signal group including the steering command and the acceleration command by means of the chassis control module.

[0035] For the modal control method of the autonomous vehicle of the present invention, step (E) includes the following sub-steps:

[0036] (E-1) Estimate the required sideslip angle of each wheel by means of the chassis control module, at least according to the current speed of the autonomous vehicle and the force applied to the wheel along the vertical axis; and

[0037] (E-2) Calculate the actuation margin signal group by means of the chassis control module. Step (E-2) includes the following sub-steps:

[0038] (E-2-1) Calculate the applied lateral force of the autonomous vehicle by means of the chassis control module, at least according to the required sideslip angle of the wheel, and generate a lateral force command indicating the applied lateral force,

[0039] (E-2-2) Calculate the distance between the lever center of mass of the autonomous vehicle and the specified trajectory curve by means of the chassis control module,

[0040] (E-2-3)Via the chassis control module, calculate the required longitudinal acceleration of the autonomous vehicle according to the distance, the applied lateral force, and the curvature of the specified trajectory curve, and generate a longitudinal acceleration command corresponding to the required longitudinal acceleration, and

[0041] (E-2-4)Via the chassis control module, generate the actuation margin signal group including the lateral force command and the longitudinal acceleration command.

[0042] In the modal control method of the autonomous vehicle of the present invention, calculating the actuation signal group in step (C) includes performing driving comfort adjustment on the actuation signal group according to an expected vibration data set generated from a plurality of expected actions generated by the autonomous vehicle according to the actuation signal group.

[0043] Another object of the present invention is to provide a chassis control module for implementing the modal control method of the autonomous vehicle.

[0044] The chassis control module of the present invention is applicable to controlling the mode of an autonomous vehicle, the autonomous vehicle including a chassis, a dynamic drive system, an information platform, a decision module, and an actuator controller module, the dynamic drive system including a plurality of wheels, motors, a steering subsystem, a power system, and a braking subsystem mounted on the chassis of the autonomous vehicle, the information platform including a plurality of sensor groups disposed on the wheels and a plurality of parts of the autonomous vehicle, the sensor groups being used to obtain a plurality of vehicle dynamic data sets when the autonomous vehicle is traveling, the decision module being used to calculate and generate decision commands for maneuvering the autonomous vehicle, the chassis control module being coupled to the information platform and the decision module, the actuator controller module being coupled to the chassis control module and the dynamic drive system, the chassis control module comprising:

[0045] A dynamic margin identifier, designed to receive the vehicle dynamic data sets obtained by the information platform, perform vehicle operation margin estimation according to the vehicle dynamic data sets and a plurality of executable performance values related to the dynamic drive system, so as to estimate a plurality of tire force margin data sets respectively corresponding to the wheels and a plurality of dynamic chassis margin data sets respectively corresponding to the wheels;

[0046] An integrated controller connected to the dynamic margin identifier is configured to receive the decision instructions, calculate the required performance values of the plurality of decision instructions, and determine whether the autonomous vehicle can travel according to the decision instructions based on the tire force margin data set and the dynamic chassis margin data set estimated by the dynamic margin identifier. When it is determined that the autonomous vehicle can travel according to the decision instructions, the integrated controller calculates an actuation signal group according to the decision instructions and outputs the actuation signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the decision instructions to move the autonomous vehicle;

[0047] When it is determined that the autonomous vehicle cannot travel according to the decision instructions, the integrated controller outputs a request signal indicating an adjustment to the decision instructions to the decision module; and

[0048] After a predetermined period after the output of the request signal, when the integrated controller does not receive an adjusted decision instruction within the predetermined period, the integrated controller estimates the required sideslip angles corresponding to the wheels respectively, and calculates an actuation margin signal group based on the tire force margin data set, the dynamic chassis margin data set, the required sideslip angles, and the lever center of mass related to the autonomous vehicle, and outputs the actuation margin signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the actuation margin signal group to move the autonomous vehicle.

[0049] For the chassis control module of the present invention, the autonomous vehicle further includes a chassis self-check module, and the chassis control module further includes a self-diagnosis unit. The self-diagnosis unit is connected to the chassis self-check module and performs a preliminary check on the dynamic drive system and the chassis control module.

[0050] Wherein, when the preliminary check indicates that either the dynamic drive system or the chassis control module is in either a non-enabled state or an abnormal state, the self-diagnosis unit controls the integrated controller to generate a stop actuation signal and outputs the stop actuation signal to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system to stop the autonomous vehicle.

[0051] For the chassis control module of the present invention, when the integrated controller receives the adjusted decision instruction within the predetermined period after the output of the request signal, the integrated controller calculates the actuation signal group according to the adjusted decision instruction and outputs the actuation signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the decision instructions to move the autonomous vehicle.

[0052] For the chassis control module of the present invention, the dynamic margin identifier is used to estimate the vehicle dynamic data set of the tire force margin data set. The vehicle dynamic data set includes a plurality of forces applied to the tire along the vertical axis, longitudinal axis, and lateral axis respectively;

[0053] The dynamic margin identifier also calculates the aligning torque calculated from the vehicle dynamic data set, and calculates the road adhesion coefficient according to the aligning torque; and

[0054] The dynamic margin identifier also estimates the tire force margin data set according to the aligning torque and the road adhesion coefficient.

[0055] For the chassis control module of the present invention, the dynamic chassis margin data set calculated by the dynamic margin identifier includes:

[0056] A steering margin sub-data set having an angular velocity margin and a current-torque margin;

[0057] A motor power margin sub-data set having a speed margin and an acceleration margin; and

[0058] A brake torque margin sub-data set having a pressure margin and a brake pressure.

[0059] For the chassis control module of the present invention, the decision instruction includes a plurality of waypoints and a specified speed. The integrated controller:

[0060] Performs polynomial curve fitting on the waypoints to obtain a specified trajectory curve;

[0061] Determines the curvature of the specified trajectory curve; and

[0062] Calculates the required performance value at least according to the specified speed and the curvature of the specified trajectory curve.

[0063] For the chassis control module of the present invention, the required performance value includes a longitudinal speed. The integrated controller is designed to determine whether the autonomous vehicle can travel according to the decision instruction in the following manner:

[0064] Determines the longitudinal speed from the specified speed;

[0065] Calculates a lateral acceleration margin according to the steering margin sub-data set and the motor power margin sub-data set;

[0066] Calculates a longitudinal speed threshold according to the lateral acceleration margin and the curvature of the specified trajectory curve; and

[0067] Compare the longitudinal speed threshold and the longitudinal speed. When the longitudinal speed threshold is greater than the longitudinal speed, it is determined that the autonomous vehicle can travel.

[0068] For the chassis control module of the present invention, the required performance value includes the applied steering angle, and the integrated controller calculates the actuation signal group in the following manner:

[0069] Generate a steering command based on the applied steering angle and the specified speed;

[0070] Input the specified speed into a proportional-integral-derivative controller to obtain the applied acceleration related to one of the power system and the brake subsystem, and generate an acceleration command indicating the applied acceleration; and

[0071] Generate the actuation signal group including the steering command and the acceleration command.

[0072] For the chassis control module of the present invention, for each wheel, the integrated controller estimates the required sideslip angle of the wheel based on at least the current speed of the autonomous vehicle and the force applied to the wheel along the vertical axis;

[0073] The integrated controller calculates the actuation margin signal group in the following manner:

[0074] Calculate the applied lateral force of the autonomous vehicle based on at least the required sideslip angle of the wheel, and generate a lateral force command indicating the applied lateral force,

[0075] Calculate the distance between the lever center of mass of the autonomous vehicle and the specified trajectory curve,

[0076] Calculate the required longitudinal acceleration of the autonomous vehicle based on the distance, the applied lateral force, and the curvature of the specified trajectory curve, and generate a longitudinal acceleration command corresponding to the required longitudinal acceleration, and

[0077] Generate the actuation margin signal group including the lateral force command and the longitudinal acceleration command.

[0078] The chassis control module of the present invention further includes a comfort adjustment unit connecting the integrated controller and the actuator controller module. The comfort adjustment unit responds to the actuation signal group received from the integrated controller, performs driving comfort adjustment on the actuation signal group according to the expected vibration data set generated by the actuation signal group to generate an adjusted actuation signal group, and outputs the adjusted actuation signal group to the actuator controller module.

[0079] The beneficial effects of the present invention are as follows: The embodiments of the present invention can be used in more advanced autonomous vehicles (e.g., supporting SAE levels 4 and 5 operations) to ensure that the modes of the advanced autonomous vehicles are based not only on the environment in which the advanced autonomous vehicles travel but also on the capabilities of the advanced autonomous vehicles themselves, particularly with respect to the capabilities of the chassis modules of the advanced autonomous vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 is a block diagram illustrating the components of an autonomous vehicle according to an embodiment of the present invention;

[0081] Figures 2A to 2C is a flowchart illustrating an embodiment of a method for controlling the mode of an autonomous vehicle according to the present invention;

[0082] Figure 3 is a schematic diagram illustrating a plurality of tire forces applied to a tire in different directions;

[0083] Figure 4 is a schematic diagram illustrating a plurality of waypoints including decision instructions and a specified trajectory curve fitted according to the waypoints;

[0084] Figure 5 is a flowchart illustrating the operation of estimating a tire force margin data set according to the present invention;

[0085] Figure 6 is a flowchart illustrating the operation of estimating a plurality of dynamic chassis margin data sets according to the present invention;

[0086] Figure 7 is a schematic diagram illustrating a vehicle traveling on the specified trajectory curve planned and the curvature of the specified trajectory curve according to the present invention;

[0087] Figure 8 is a block diagram illustrating the operation of controlling the mode of the autonomous vehicle in a normal mode according to the present invention;

[0088] Figure 9 is a block diagram illustrating the operation of controlling the mode of the autonomous vehicle in an extreme mode according to the present invention; and

[0089] Figures 10 to 13 is a block diagram illustrating the control connections and interfaces between components of an actuator controller module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0090] Before describing the present invention in detail, it should be noted that in the following description, similar components are denoted by the same reference numerals.

[0091] In the present invention, the term "coupling" may refer to a direct connection between multiple electrical devices / apparatus via a conductive material (e.g., a wire), or an indirect connection between two electrical devices / apparatus through various wired connection technologies of at least one other device / apparatus, such as a Campus Area Network (CAN), Local Area Networks (LAN), or Ethernet, or wireless communication.

[0092] Refer to Figure 1 , which shows a block diagram of an autonomous vehicle 100 according to an embodiment of the present invention. In this embodiment, the autonomous vehicle 100 operates in an autonomous driving mode (e.g., SAE levels 3 to 5), where the SAE international classification is shown in Table 1 below.

[0093] Table 1

[0094]

[0095]

[0096] In this embodiment, the autonomous vehicle 100 includes an information platform 200, a decision-making module 300, a chassis control module 400, a dynamic drive system 500, and a chassis self-check module 700.

[0097] The dynamic drive system 500 includes multiple wheels (not shown in the figure) mounted on the chassis of the autonomous vehicle, a motor (not shown in the figure), a steering subsystem (not shown in the figure), a power system (not shown in the figure), and a braking subsystem (not shown in the figure). Each wheel includes a circular frame (not shown in the figure) and a tire mounted on the circular frame (not shown in the figure). The subsystems and components of the dynamic drive system 500 are mounted on a chassis (not shown in the figure) of the autonomous vehicle 100, collectively referred to as a chassis module. In some embodiments, the motor is included in the steering subsystem and the power system.

[0098] The steering subsystem may include a steering wheel (not shown in the figure), a rack for guiding the front wheels (not shown in the figure), a steering column (not shown in the figure) and a steering shaft (not shown in the figure) connecting the steering wheel and the rack, and a steering motor (not shown in the figure) for driving the autonomous vehicle 100 to operate in an autonomous driving mode. The steering motor includes an Electric Power Steering (EPS) module. In the case where the autonomous vehicle 100 supports SAE level 4 or 5 operation, the steering wheel, the steering column, and the steering shaft can be omitted.

[0099] The steering subsystem may further include a steering controller (not shown in the figure), which is used to control the steering motor and can output a set of logic instructions through a microprocessor and executable software and store them in a machine or computer-readable memory (such as random access memory (RAM), electronic control unit (ECU), read-only memory (ROM), programmable read-only memory, firmware, and flash memory, etc.); programmable logic (such as programmable logic arrays (PLA), field programmable gate arrays (FPGA), complex programmable logic devices (CPLD), etc.); fixed-function logic hardware transmitted through circuits (such as application specific integrated circuit (ASIC), complementary metal oxide semiconductor (CMOS), transistor-transistor logic (TTL), etc. technology); or any combination of other related functions.

[0100] For example, the microprocessor may include, but is not limited to, a single-core processor, a multi-core processor, a dual-core mobile processor, a microprocessor, a microcontroller, a digital signal processor (DSP), a programmable gate array, an application specific integrated circuit, and / or a radio-frequency integrated circuit (RFIC), etc.

[0101] The power system may utilize a motor controller (not shown in the figure) that includes a control motor and an accelerator pedal (not shown in the figure) connected to the motor controller. The accelerator pedal is used to transmit an acceleration request signal (i.e., the power demand of the electric motor) to the power system in response to the operation of the user. In some embodiments, the motor may be incorporated into the power system. In the case where the autonomous vehicle 100 supports SAE level 4 or 5 operation, the accelerator pedal may be omitted. As described above, a set of logic instructions can be output through a microprocessor and executable software and stored in a machine or computer-readable memory to implement the power system.

[0102] The brake subsystem may include a plurality of disc brakes (disc brakes) (not shown in the figure) respectively and tightly connected to the wheels, a plurality of caliper assemblies (not shown in the figure) respectively clamping on the disc brakes, a brake pedal (not shown in the figure) for driving the caliper assemblies to brake the disc brakes, and a brake controller (not shown in the figure) for automatically actuating the caliper assemblies. When the autonomous vehicle 100 supports SAE level 4 or 5 operations, the brake pedal can be omitted. As described above, the brake controller can be implemented as a set of logical instructions stored in a machine or computer-readable storage medium of a memory by a microprocessor and executable software.

[0103] The information platform 200 is used to obtain a plurality of vehicle dynamic data sets when the autonomous vehicle is driving, and it includes a positioning module 210 and a vehicle state sensor module 220. The positioning module 210 can be implemented in a similar manner to the above-mentioned steering subsystem as a set of logical instructions stored in a machine or computer-readable storage medium of a memory by a microprocessor and executable software.

[0104] The positioning module 210 may further include a positioning component, such as a global positioning system (GPS) component. The memory of the positioning module 210 stores an electronic map and a plurality of software instructions. When the microprocessor executes the software instructions, the software instructions cause the microprocessor to perform simultaneous localization and mapping (SLAM) on the electronic map. During use, the positioning module 210 provides information about the global position and status of the autonomous vehicle 100, such as the direction angle of the autonomous vehicle 100.

[0105] The vehicle state sensor module 220 includes a plurality of sensor groups disposed on the wheels, and the sensor groups are used to obtain the wheel information of the autonomous vehicle 100. In this embodiment, the vehicle state sensor module 220 further includes an inertial measurement unit (IMU) and a real-time kinematic (RTK) unit for obtaining the dynamic information of the autonomous vehicle 100 when the autonomous vehicle 100 is driving. During use, the inertial measurement unit and the real-time kinematic unit can complement each other and cooperate to provide the dynamic information under various conditions. For example, when the autonomous vehicle 100 is driving in an area where external communication may be restricted (for example, driving in a tunnel), the real-time kinematic unit cannot provide dynamic information, and the inertial measurement unit provides the dynamic information of the autonomous vehicle 100.

[0106] In one embodiment, the vehicle status sensor module 220 may include additional sensors (not shown in the figure) disposed on other parts of the autonomous vehicle 100 (such as the chassis, the motor, the steering subsystem, the braking subsystem, etc.) to obtain other data, such as a speed of the autonomous vehicle 100 (e.g., a tachometer), a gear ratio, etc.

[0107] The vehicle dynamic data set obtained by the information platform 200 may include various categories of data.

[0108] For each wheel, a sensor group on the wheel is used to sense a plurality of specific data sets, and a plurality of tire forces applied to the wheel in different directions are estimated according to the specific data sets. As Figure 3 shown, the tire forces may include a radial force, a lateral force, a forward / backward force, etc. In addition, by estimating the specific data sets sensed by the sensor group, a frictional force between the wheel and the ground, a torque generated by the wheel, a steering angle of the wheel (when the wheel is a front wheel), a wheel speed, etc. are obtained.

[0109] The sensor on the motor can be used to sense the output power of the motor (i.e., the EPS motor torque).

[0110] The sensors on the steering subsystem can be used to sense an angular velocity of the steering wheel, a steering angle of the steering wheel, etc.

[0111] The sensors on the power system can be used to sense a speed, an acceleration, etc. of the autonomous vehicle 100.

[0112] The sensors on the braking subsystem can be used to sense a brake pedal (if the autonomous vehicle is equipped with the brake pedal), a braking pressure applied to the disc brake (disc brake), etc.

[0113] The decision-making module 300 may use a microprocessor and a physical memory (such as a flash memory) storing software instructions. When the software instructions are executed by the microprocessor, the software instructions cause the microprocessor to calculate a plurality of speed plans and trajectory plans for the autonomous vehicle 100. Specifically, when the autonomous vehicle 100 is in an autonomous driving mode, the decision-making module 300 generates a decision instruction including a plurality of consecutive target path points and a target speed related to the travel of the autonomous vehicle 100. The consecutive target path points may form a path for the autonomous vehicle 100 to move along. The generation of the decision instruction may be based on a predetermined path, a plurality of objects detected by the information platform 200, or other aspects related to the autonomous vehicle 100 and / or the surrounding environment.

[0114] The chassis control module 400 is coupled to the information platform 200 and the decision-making module 300 to receive data from the information platform 200 and the decision-making module 300. And the chassis control module 400 is coupled to an actuator controller module 600 to transmit signals to the actuator controller module 600.

[0115] It should be noted in particular that, as described above, each component included in the chassis control module 400 can be implemented using a microprocessor and executable software as a set of logical instructions stored in a machine or computer-readable storage medium of a memory.

[0116] In this embodiment, the chassis control module 400 includes a dynamic margin identifier 410, an integrated controller 420, a reference signal generator 430, a module communication unit 440, and a comfort adjustment unit 450. It should be noted that each component included in the chassis control module 400 can be implemented using a microprocessor and a physical memory (such as a flash memory) storing software instructions. When the software instructions are executed by the microprocessor, the software instructions cause the microprocessor to perform various operations as described in the following paragraphs.

[0117] The dynamic margin identifier 410 is coupled to the vehicle state sensor module 220 and includes a chassis margin estimator 412 and a tire force margin estimator 414. The dynamic margin identifier 410 is coupled to the decision-making module 300 to transmit information thereto.

[0118] The integrated controller 420 includes a self-diagnosis unit 422 and a modal controller 424.

[0119] The reference signal generator 430 is coupled to the positioning module 210 and the decision-making module 300 to receive and process the data. The reference signal generator 430 is coupled to the integrated controller 420 and transmits the processed data to the integrated controller 420. It should be noted that the reference signal generator 430 can be used to transmit other data (such as an emergency flag) known in the prior art, and its details are omitted here for the sake of brevity.

[0120] The module communication unit 440 is coupled to the chassis self-check module 700, the integrated controller 420, and the decision-making module 300, and the module communication unit 440 is used for data transmission among the chassis self-check module 700, the integrated controller 420, and the decision-making module 300.

[0121] Generally speaking, the chassis control module 400 is used to determine whether the decision instruction generated by the decision-making module 300 can be executed by the dynamic drive system 500 of the autonomous vehicle 100, and make a response according to the judgment result.

[0122] The actuator controller module 600 is connected to the chassis control module 400 to receive signals therefrom, and the actuator controller module 600 is connected to the dynamic drive system 500 to actuate the dynamic drive system 500 according to the signals to drive the autonomous vehicle 100.

[0123] The chassis self-check module 700 is coupled to the dynamic drive system 500 and the sensor set of the vehicle status sensor module 220 to perform an inspection operation on various parts of the autonomous vehicle 100, and determine whether the data of various parts of the autonomous vehicle 100 is operating normally according to the sensor set. For example, the chassis self-check module 700 can determine whether the chassis is operating normally.

[0124] Refer to Figures 2A to 2C , which is a flowchart illustrating the steps of a method for controlling a mode of an autonomous vehicle according to an embodiment of the present invention. It should be noted that the method is implemented using the chassis control module 400 connecting the components of the autonomous vehicle 100.

[0125] In this embodiment, when the autonomous vehicle 100 is in an autonomous driving mode, the information platform 200 is used to detect the vehicle dynamic data set, and the decision module 300 is used to generate the decision instruction.

[0126] In step 800, the chassis control module 400 performs a preliminary inspection on various components of the dynamic drive system 500 and the chassis control module 400 of the autonomous vehicle 100.

[0127] Specifically, in this embodiment, as described in the following sub-steps, the preliminary inspection includes many operations. It should be particularly noted that in this embodiment, each component of the information platform 200, the decision module 300, the chassis control module 400, and the dynamic drive system 500 can be equipped with a self-diagnostic component (not shown in the figure) to determine whether the component is operating, and send a status signal and / or flag to the module communication unit 440 to indicate whether the component is operating normally.

[0128] In sub-step 802, the module communication unit 440 of the chassis control module 400 determines whether the decision module 300 is operating normally. It can be determined whether the decision module 300 is operating normally by determining whether the status signal and / or flag has been received from the decision module 300 through the module communication unit 440.

[0129] When it is determined that the decision module 300 is operating normally, the process proceeds to sub-step 804. Otherwise, the module communication unit 440 generates a message indicating that the autonomous vehicle 100 cannot operate in the autonomous driving mode to notify the driver.

[0130] In sub-step 804, the self-diagnosis unit 422 determines whether the components of the chassis control module 400 are enabled and operating properly. It can be determined whether the components of the chassis control module 400 are enabled and operating properly by whether the module communication unit 440 obtains a status signal from each component of the chassis control module 400. When it is determined that the chassis control module 400 is enabled and operating properly, the process proceeds to sub-step 806. Otherwise (i.e., the chassis control module 400 is not enabled (non-enabled state) or cannot operate properly (abnormal state)), the integrated controller 420 generates a message indicating that the autonomous vehicle 100 cannot drive in the autonomous driving mode to notify the driver, and the integrated controller 420 initiates a troubleshooting operation.

[0131] In sub-step 806, the integrated controller 420 determines whether the dynamic drive system 500 is operating normally. When it is determined based on the data from the chassis self-check module 700 that the dynamic drive system 500 is operating normally, the process proceeds to sub-step 808. Otherwise (for example, the dynamic drive system 500 is in a non-enabled state or an abnormal state), the integrated controller 420 generates a message indicating that it may be unsafe for the autonomous vehicle 100 to continue driving to notify the driver, and the mode controller 424 generates a stop actuation signal and outputs the stop actuation signal to the actuator controller module 600 to control the actuator controller module 600 to stop the autonomous vehicle 100, for example, during deceleration to a stop and moving towards the roadside.

[0132] In sub-step 808, the integrated controller 420 determines whether the dynamic margin identifier 410 of the chassis control module 400 is enabled and operating properly. When it is determined based on the data from the chassis control module 400 that the dynamic margin identifier 410 is enabled and operating properly, the process proceeds to step 810. Otherwise, the integrated controller 420 generates a message indicating that it may be unsafe for the autonomous vehicle 100 to continue driving to notify the driver, and the mode controller 424 generates a stop actuation signal and outputs the stop actuation signal to the actuator controller module 600 to control the actuator controller module 600 to stop the autonomous vehicle 100, for example, during deceleration to a stop and moving towards the roadside.

[0133] In step 810, the dynamic margin identifier 410 collects the vehicle dynamic data set from the information platform 200, and the integrated controller 420 obtains the vehicle dynamic data set and performs a vehicle operation margin estimation according to the vehicle dynamic data set. Specifically, for the vehicle operation margin estimation, multiple executable performance values in the dynamic drive system 500 are first estimated, and then according to the vehicle dynamic data set and the executable performance values, multiple tire force margin data sets corresponding to the wheels respectively, and multiple dynamic chassis margin data sets corresponding to the wheels respectively are estimated.

[0134] As Figure 5 For each wheel as shown, the estimation of the tire force margin data set corresponding to the wheel can be divided into three stages. In the first stage, according to the vehicle dynamic data set, the tire force margin estimator 414 of the dynamic margin identifier 410 obtains a wheel vertical force (i.e., a radial force applied to the wheel along a vertical axis) and estimates a load distribution (i.e., the distribution of the wheel vertical force applied to the wheel), obtains a wheel lateral force (i.e., a lateral force applied to the wheel along a lateral axis), obtains a speed of the autonomous vehicle 100 and the rotational speed of each wheel, and estimates a slip angle of the wheel, and obtains a wheel longitudinal force (i.e., a forward / backward force applied to the wheel along a longitudinal axis), and estimates a slip ratio of the wheel (also referred to as "longitudinal slip").

[0135] Then, in the second stage, the above vehicle dynamic data set and the parameters obtained in the first stage can be used to calculate a self-aligning torque of the wheel, and a road adhesion coefficient is estimated according to the self-aligning torque of the wheel. In addition, the executable performance value of the wheel can be expressed as a maximum force value that the wheel (tire) can generate, and the maximum force value is calculated according to a rotational angle of the wheel and a contact length between the wheel and the ground.

[0136] Then, in the third stage, the tire force margin estimator 414 calculates the tire force margin data set of the wheel according to the parameters obtained in the first stage, the self-aligning torque, and the road adhesion coefficient. The tire force margin data set represents the forces that can be applied to the wheel (on the longitudinal axis and the lateral axis) before reaching the executable performance value.

[0137] In this embodiment, the tire force margin estimator 414 can use the following formula to calculate the tire force margin data set.

[0138]

[0139] Where and represent the components of the tire force margin data set, represents the road adhesion coefficient calculated using multiple parameters included in the vehicle dynamic data set and the self-aligning torque of the wheel. and respectively represent the squares of the forces applied to the tire in different directions.

[0140] Then, the tire force margin estimator 414 transmits the tire force margin data set of the wheel to the integrated controller 420, and transmits the road adhesion coefficient and the self-aligning torque to the chassis margin estimator 412.

[0141] As Figure 6 shown in the dynamic chassis margin data set, the chassis margin estimator 412 of the dynamic margin identifier 410 is used to obtain the vehicle dynamic data set from the vehicle state sensor module 220 and estimate the dynamic chassis margin data set according to the vehicle dynamic data set. Specifically, the vehicle dynamic data set may include a pressure applied to the brake pedal (or brake pressure), a power output by the motor, a speed of the autonomous vehicle 100, a gear ratio, etc.

[0142] In the first stage, the chassis margin estimator 412 obtains the vehicle dynamic data set from the vehicle state sensor module 220 and performs a preprocessing operation to filter out potential noise.

[0143] In the second stage of this embodiment, the chassis margin estimator 412 estimates the dynamic chassis margin data set, which includes a steering margin sub-data set, a motor power margin sub-data set, and a brake torque margin sub-data set. The steering margin sub-data set has an angular velocity margin and a current-torque margin, the motor power margin sub-data set has a speed margin and an acceleration margin, and the brake torque margin sub-data set has a pressure margin and a brake pressure. The current-torque margin can indicate an additional amount of current or torque that the dynamic drive system 500 can output. The chassis margin estimator 412 can calculate the components of the dynamic chassis margin data set according to the vehicle dynamic data set obtained by the vehicle state sensor module 220 of the information platform 200, the current road conditions of the ground (for example, wet or dry, represented by the friction between the wheel and the ground), the physical properties of the wheel (for example, an effective rotation radius of the wheel circular frame of each wheel), etc.

[0144] Note that the calculation of the generally described dynamic chassis margin data set involves calculating multiple current values of multiple related components (current speed, angular velocity of the steering wheel, etc.), and using the current value and the corresponding executable performance value to calculate the dynamic chassis margin data set. Among them, other parameters can also be considered, such as the self-aligning torque, a friction torque between the ground and the tire, an effective rolling radius of the wheel, etc.

[0145] In this embodiment, the chassis margin estimator 412 estimates the steering margin sub-data set according to the power output by the motor, the gear ratio, the road adhesion coefficient, and the self-aligning torque, estimates the motor power margin sub-data set according to the power output by the motor, the gear ratio, and the road adhesion coefficient, and estimates the brake torque margin sub-data set according to the power output by the motor, the gear ratio, and the pressure applied to the brake pedal.

[0146] Note that for each of the steering subsystem, the motor, and the brake subsystem, the executable performance value can be derived from the T-N curve of the motor, and the relevant margin can be obtained by subtracting the current output from the executable performance value. Other parameters, such as a gear ratio of the motor, a traction force of the motor, a back electromotive force coefficient, a rotational speed of the motor, an efficiency of the motor, etc., can be used to derive the executable performance value.

[0147] Therefore, the dynamic margin identifier 410 is used to calculate a longitudinal acceleration margin according to the motor power margin sub-data set and the brake torque margin sub-data set, and calculate a lateral acceleration margin according to the steering margin sub-data set.

[0148] It should be specifically noted that the information platform 200 is used to continuously obtain the vehicle dynamic data set, and the dynamic margin identifier 410 is used to continuously perform the vehicle operation margin estimation, so that the tire force margin data set and the dynamic chassis margin data set can be updated in real time.

[0149] In step 812, the integrated controller 420 receives the data set from the information platform 200, and determines whether the values of the tire force margin data set and the dynamic chassis margin data set estimated in step 810 converge over time. When the sensor is first activated and gradually stabilizes over time, the output of the sensor may deviate from the output of the sensor in the initial state. Therefore, the values of the tire force margin data set and the dynamic chassis margin data set estimated in step 810 may not be accurate at first, but converge over time and become more accurate. When it is determined that the values of the tire force margin data set and the dynamic chassis margin data set have converged, the process proceeds to step 814. Otherwise, the process proceeds to step 808.

[0150] In step 814, the reference signal generator 430 receives a decision instruction from the decision module 300. In this embodiment, the decision instruction includes a plurality of waypoints and a specified speed (such as Figure 4 where the marker X represents a waypoint). Each waypoint may be in the form of a set of geographic coordinates in a geographic coordinate system (GCS) to indicate a specific geographic location, and in response to receiving the waypoint, the reference signal generator 430 performs a transformation operation to transform each geographic coordinate in the set of geographic coordinates into a set of local coordinates related to the autonomous vehicle 100 for subsequent calculations.

[0151] The reference signal generator 430 performs a polynomial curve fitting on the waypoints to obtain a specified trajectory curve. Specifically, in this embodiment, the polynomial curve fitting may be a least squares fitting, and the specified trajectory curve can be represented in the form of the following third-order polynomial:

[0152] y = a0 + a1x + a2x 2 + a3x 3 ,

[0153] where x and y represent the geographic coordinates of a waypoint in the GCS, and a0, a1, a2, and a3 represent the coefficients obtained through the polynomial curve fitting.

[0154] Next, the reference signal generator 430 transmits the specified trajectory curve to the integrated controller 420.

[0155] Then, the integrated controller 420 determines a longitudinal speed for traveling on the specified trajectory curve and calculates a plurality of required performance values of the decision instruction based at least on the estimated maximum lateral acceleration, the specified speed, and a curvature of the specified trajectory curve. Calculating the specified trajectory curve can obtain a lateral movement amount and a front wheel steering angle required by the autonomous vehicle 100.

[0156] In this embodiment, the required performance values include the longitudinal speed (i.e., the speed at which the autonomous vehicle 100 travels forward) and an applied steering angle of the front wheel, and the applied steering angle of the front wheel can be further calculated through an angular displacement of the steering wheel.

[0157] In cases where the decision instruction requires the autonomous vehicle 100 to turn at a relatively high speed, quantitatively increase the output of the steering motor, quantitatively increase the brake pressure to the disc brake, etc., the required performance may indicate a certain amount of power. The power may be a current supplied to the steering motor to drive the rack to rotate the front wheel at the applied steering angle.

[0158] In step 816, the self-diagnosis unit 422 determines whether the autonomous vehicle 100 can travel according to the decision instruction based on the tire force margin data set and the dynamic chassis margin data set estimated by the dynamic margin identifier 410.

[0159] Specifically, in step 816, a determination is made in a longitudinal direction and a lateral direction as to whether the autonomous vehicle 100 can travel according to the decision instruction. Step 816 includes the following operations.

[0160] The self-diagnosis unit 422 calculates a longitudinal speed threshold based on the currently calculated lateral acceleration margin and the curvature of the specified trajectory curve. It should be noted that in this embodiment, other parameters may also be used in the calculation of the longitudinal speed threshold, such as the friction between the wheel and the ground, the effective rolling radius of the wheel, etc.

[0161] In this embodiment, when turning according to the specified trajectory curve, a curvature (as Figure 7 shown) will appear, and a radius of curvature of the curvature can be derived. In this case, the longitudinal speed threshold can be expressed as:

[0162]

[0163] where V x represents the longitudinal speed threshold, a y represents the lateral acceleration margin, and R represents the radius of curvature.

[0164] Then, the self-diagnosis unit 422 compares the longitudinal speed threshold and the longitudinal speed. When it is determined that the longitudinal speed threshold is greater than the longitudinal speed, the autonomous vehicle 100 can travel according to the decision instruction, and the process proceeds to step 820. Otherwise, when it is determined that the longitudinal speed threshold is not greater than the longitudinal speed, the autonomous vehicle 100 cannot travel according to the decision instruction, and the process proceeds to step 830.

[0165] In step 820, the chassis control module 400 generates an actuation signal group according to the decision instruction and outputs the actuation signal group to the actuator controller module 600, so that the actuator controller module 600 actuates the dynamic drive system 500 according to the decision instruction to move the autonomous vehicle 100. In some embodiments where the comfort adjustment unit 450 is omitted, the mode controller 424 directly transmits the actuation signal group to the actuator controller module 600. In some embodiments where the comfort adjustment unit 450 exists, the mode controller 424 uses the comfort adjustment unit 450 to generate the actuation signal group.

[0166] Refer back toFigure 8 , in this embodiment, the operation of step 820 includes the following sub-steps.

[0167] In sub-step 822, the reference signal generator 430 of the chassis control module 400 performs the polynomial curve fitting at the path points to obtain the specified trajectory curve.

[0168] In sub-step 824a, the mode controller 424 of the chassis control module 400 generates a steering command according to the applied steering angle and the specified speed. It should be noted that, in this embodiment, the mode controller 424 calculates the applied steering angle according to the specified trajectory curve, the specified speed, and other parameters (such as a preview deviation, a yaw rate, an understeer coefficient, a control gain of the steering subsystem, etc.).

[0169] In sub-step 824b, according to the relationship between the specified speed and the current speed of the autonomous vehicle, the mode controller 424 inputs the specified speed into a proportional–integral–derivative (PID) controller (not shown in the figure) to obtain an applied acceleration or deceleration related to one of the power system and the braking subsystem (θ throttle or θ brake ). Then, the mode controller 424 outputs an acceleration command for the applied acceleration. In addition, the mode controller 424 can calculate a center of percussion (COP) of the autonomous vehicle 100 according to the moment of inertia of the autonomous vehicle 100, the mass of the autonomous vehicle 100, and the length from a rear wheel to the center of mass of the autonomous vehicle 100.

[0170] In sub-step 826, the mode controller 424 generates the actuation signal group including the steering command and the acceleration command.

[0171] It should be noted that, in the Figure 8 operation, other operations known in the related art (such as look-ahead distance calculation, lateral error incorporating the look-ahead distance, applying operation limitations, etc.) and multiple parameters related to the speed and acceleration of the autonomous vehicle 100 (such as Δx cmd , ΔVx,) can be used to generate the actuation signal group. For the sake of brevity, the details thereof are omitted here.

[0172] Note that, in embodiments where the comfort adjustment unit 450 exists, in step 820, the comfort adjustment unit 450 performs a driving comfort adjustment on the actuation signal group according to an expected vibration data set generated by a plurality of expected actions of the autonomous vehicle 100 according to the actuation signal group, and transmits the expected vibration data set to the passengers in the autonomous vehicle 100. In this embodiment, the comfort adjustment unit 450 may determine whether the expected vibration data set constitutes whole body vibration (WBV) that may be uncomfortable for the passengers in the autonomous vehicle 100, as defined in International Organization for Standardization (ISO) 2631-1. In the case where the comfort adjustment unit 450 determines that the expected vibration data set may cause discomfort to the passengers in the autonomous vehicle 100, the comfort adjustment unit 450 may accordingly adjust the actuation signal group to generate an adjusted actuation signal group, which helps to reduce the expected vibration data set.

[0173] Then, in step 829, the actuation signal group (or the adjusted actuation signal group) is output to the actuator controller module 600, and the actuator controller module 600 controls the component operations of the dynamic drive system 500 according to the (adjusted) actuation signal group, so as to move the autonomous vehicle 100 according to the decision instruction.

[0174] Note that, since the autonomous vehicle 100 can drive according to the decision instruction, the operation in step 820 can be referred to as the "normal mode".

[0175] On the other hand, in step 830, the mode controller 424 outputs a request signal for adjusting the decision instruction to the decision module 300 via the module communication unit 440. Specifically, the request signal for adjusting the decision instruction may include the lateral acceleration margin and the longitudinal speed threshold, to request the decision module 300 to generate the plurality of adjusted planned path points according to the updated data.

[0176] In step 832, the mode controller 424 determines whether the chassis control module 400 has received an adjusted decision instruction within a predetermined time period (e.g., 0.5 seconds) after outputting the request signal for adjusting the decision instruction.

[0177] When it is determined that the adjusted decision instruction has been received within the predetermined time period, the process proceeds to step 820.

[0178] Otherwise, when the chassis control module 400 does not receive the adjusted decision instruction within the predetermined time period, the process proceeds to step 840.

[0179] In step 840, the chassis control module 400 calculates a consistent actuation margin signal group based on the decision instruction, the dynamic chassis margin data set, the vehicle dynamic data set, and the lever center of mass of the autonomous vehicle.

[0180] Refer again to Figure 9 , in this embodiment, the operation of step 840 includes the following sub-steps.

[0181] In sub-step 842, the reference signal generator 430 of the chassis control module 400 performs the polynomial curve fitting at the path points of the decision instruction to obtain the specified trajectory curve.

[0182] In sub-step 844, the mode controller 424 of the chassis control module 400 calculates the lateral force applied to the autonomous vehicle 100 generated by the current action of the autonomous vehicle 100 and generates a lateral force instruction indicating the applied lateral force.

[0183] Specifically, for each wheel, the mode controller 424 estimates a required slip angle based at least on the current speed of the autonomous vehicle 100 and the wheel vertical force applied to the wheel along the vertical axis. It should be noted that the term "slip angle" is the angle between the direction in which the wheel is pointing and the direction in which the wheel is actually traveling. It should be further noted that the required slip angle represents a slip angle of the vehicle, and this slip angle "attempts" to move the autonomous vehicle 100 according to the specified trajectory curve (but cannot move as precisely as indicated by the specified trajectory curve), and is achieved by adjusting the longitudinal speed of the autonomous vehicle 100.

[0184] When calculating the required slip angle of the wheel, other data included in the vehicle dynamic data set can be used, such as the cornering stiffness of the tire, the road adhesion coefficient, etc. The current speed of the autonomous vehicle 100 can be decomposed into a longitudinal speed component and a lateral speed component.

[0185] Then, the mode controller 424 calculates the lateral force applied to the autonomous vehicle 100 based on the required slip angle of the wheel and the cornering stiffness. The applied lateral force indicates how much lateral force is required for the autonomous vehicle 100 to move along the specified trajectory curve based on the required slip angle of the wheel and the current speed of the autonomous vehicle 100.

[0186] In sub-step 846, the mode controller 424 calculates a required longitudinal acceleration of the autonomous vehicle 100 and generates a longitudinal acceleration command indicative of the required longitudinal acceleration.

[0187] Specifically, the mode controller 424 calculates a distance between the lever center of mass of the autonomous vehicle 100 and the specified trajectory curve. The distance may include a longitudinal component and a lateral component.

[0188] Then, the mode controller 424 calculates the required longitudinal acceleration based on the distance, the applied lateral force, and the curvature of the specified trajectory curve. It should be noted that when calculating the required longitudinal acceleration, other parameters may be considered, such as an arc length of the specified trajectory curve, a damping and a natural frequency of a speed control subsystem (not shown in the figure) in the autonomous vehicle 100.

[0189] In sub-step 848, the mode controller 424 generates the actuation margin signal set and calculates the lateral force command and the longitudinal acceleration command.

[0190] In step 850, the mode controller 424 outputs the actuation margin signal set to the actuator controller module 600 so that the actuator controller module 600 can control components of the dynamic drive system 500 to move the autonomous vehicle 100 according to the actuation margin signal set.

[0191] It should be noted that the operation of step 840 may be referred to as "limit operation mode" because the autonomous vehicle 100 cannot move according to the decision instruction (for example, the decision instruction indicates a sudden turn exceeding the capabilities of the autonomous vehicle 100, the ground is slippery and limits the capabilities of the autonomous vehicle 100, etc.), and must be controlled to operate within the limits of the autonomous vehicle 100. As Figure 9 shown, the actuation margin signal set can cause the autonomous vehicle 100 to "attempt" to travel along the path initially planned by the decision module 300.

[0192] Refer to Figures 10 to 13 In one embodiment, the actuator controller module 600 includes a steering actuator controller 610, a power actuator controller 620, and a brake actuator controller 630. Each of the steering actuator controller 610, the power actuator controller 620, and the brake actuator controller 630 includes a feedback loop control system structure. The steering actuator controller 610, the power actuator controller 620, and the brake actuator controller 630 can be respectively used to control the steering controller of the steering subsystem, the motor controller of the power system, and the brake controller of the brake subsystem.

[0193] The steering actuator controller 610 includes an angle controller 612, an angular velocity controller 614, and a current controller 616. Each of the angle controller 612, the angular velocity controller 614, and the current controller 616 can receive the set of actuation signals (or the adjusted set of actuation signals), and respectively convert these signals into one of an angle of the steering wheel of the steering subsystem, an angular velocity of the steering wheel, and a current output to the steering motor, and respectively output them to drive the steering motor that drives the rack by the set of actuation signals.

[0194] Note that other parameters (e.g., a part of the set of actuation signals obtained from the decision instruction (e.g., δc, ωc, and i c ), multiple feedback signals generated from a part of the set of actuation signals (e.g., δf, ωf, and i f )(e.g., a motor torque coefficient (K t ), a torque loss (T L ), the self-aligning torque, etc.) can be incorporated into the operation of the steering actuator controller 610.

[0195] Since the steering actuator controller 610 includes multiple controllers capable of generating various forms of signals for multiple different vehicles with multiple different steering actuators, the steering actuator controller 610 of the actuator controller module 600 can be applied to various autonomous vehicles 100 using different types of steering subsystems.

[0196] The power actuator controller 620 includes a speed controller 622 and an acceleration controller 624. Each of the speed controller 622 and the acceleration controller 624 can receive the set of actuation signals and respectively convert the set of actuation signals into a speed and an acceleration of the autonomous vehicle 100 represented by the set of actuation signals.

[0197] Note that other parameters (e.g., a part of the set of actuation signals obtained from the decision instruction (e.g., Vx and a x ), multiple feedback signals generated from a part of the set of actuation signals (e.g., Vx,f and a x,f ), a frictional force (e.g., F friction ) and a power output torque (F power ) etc.) can be incorporated into the operation of the power actuator controller 620.

[0198] Since the power actuator controller 620 includes a plurality of controllers capable of generating various forms of signals for a plurality of different vehicles having a plurality of different power actuators, the power actuator controller 620 of the actuator controller module 600 can be applied to various autonomous vehicles 100 employing different types of power actuator controllers 620.

[0199] The brake actuator controller 630 includes an acceleration controller 632, a current controller 634, and a pressure controller 636. Each of the acceleration controller 632, the current controller 634, and the pressure controller 636 can receive the actuation signal set and convert the actuation signal set into one of a powertrain deceleration, an amount of current indicated by the actuation signal set for controlling the deceleration of the brake subsystem, and a pressure applied to the brake pedal, respectively.

[0200] Note that other parameters (e.g., a part of the actuation signal set obtained from the decision instruction (e.g., a x and i c ), a plurality of feedback signals generated from a part of the actuation signal set (e.g., Vx,f and a x,f ), another frictional force (P c ), another power output torque (P f ), a brake torque (T), etc.) can be incorporated into the operation of the brake actuator controller 630.

[0201] The brake actuator controller 630 of the actuator controller module 600 can be applied to various autonomous vehicles employing different types of brake subsystems.

[0202] Note that the actuator controller module 600 can serve as a multi-functional interface between the chassis control module 400 and the dynamic drive system 500, such that the chassis control module 400 and the actuator controller module 600 can be easily installed in existing autonomous vehicles.

[0203] In summary, the embodiments of the present invention provide a method for controlling the mode of an autonomous vehicle and a chassis control module. In this embodiment, the chassis control module 400 is configured to obtain the vehicle dynamic data set from the information platform 200 to perform the margin estimation to determine the tire force margin data set and the dynamic chassis margin data set, and in response to receiving the decision instruction, determine whether the autonomous vehicle 100 can travel according to the decision instruction based on the tire force margin data set and the dynamic chassis margin data set estimated by the dynamic margin identifier 410. When it is determined that the autonomous vehicle 100 can travel according to the decision instruction, the chassis control module 400 operates in a normal mode, wherein the chassis control module 400 generates an actuation signal set according to the decision instruction. When it is determined that the autonomous vehicle 100 cannot travel according to the decision instruction, the chassis control module 400 notifies the decision module 300 to adjust the decision instruction. When the chassis control module 400 does not receive an adjusted decision instruction from the decision module 300, the chassis control module 400 operates in an extreme mode, and the chassis control module 400 generates an actuation margin signal set according to the decision instruction, the tire force margin data set, the dynamic chassis margin data set, the plurality of required sideslip angles of the wheels, and the lever centroid of the autonomous vehicle 100. Then, the chassis control module 400 outputs the actuation signal set or the actuation margin signal set to the actuator controller module 600 to enable the actuator controller module 600 to actuate the dynamic drive system 500 to drive the autonomous vehicle 100 to move. In this configuration, the embodiments of the present invention can be used in more advanced autonomous vehicles (e.g., supporting SAE levels 4 and 5 operations) to ensure that the mode of the advanced autonomous vehicle is based not only on the environment in which the advanced autonomous vehicle travels but also on the capabilities of the advanced autonomous vehicle itself, particularly with respect to the capabilities of the chassis module of the advanced autonomous vehicle.

[0204] The above are only the embodiments of the present invention, and the scope of the embodiments of the present invention cannot be limited thereby. That is, all simple equivalent changes and modifications made according to the claims of the present invention and the content of the specification still fall within the scope of the present invention.

Claims

1. A modal control method for an autonomous vehicle, the autonomous vehicle comprising a chassis, a dynamic drive system, an information platform, a decision-making module, a chassis control module, and an actuator controller module, the dynamic drive system including a plurality of wheels, motors, a steering subsystem, a power system, and a braking subsystem mounted on the chassis of the autonomous vehicle, the information platform including a plurality of sensor groups disposed on a plurality of parts of the autonomous vehicle, the parts including the wheels, the sensor groups being configured to obtain a plurality of vehicle dynamic data sets when the autonomous vehicle is in motion, the decision-making module being configured to calculate and generate decision-making instructions for maneuvering the autonomous vehicle, the chassis control module being coupled to the information platform and the decision-making module, and the actuator controller module being coupled to the chassis control module and the dynamic drive system, characterized in that: The method comprises the following steps: (A) By means of the chassis control module, according to the vehicle dynamic data set and multiple executable performance values of the dynamic drive system, perform vehicle operation margin estimation to estimate multiple tire force margin data sets respectively corresponding to the wheels and multiple dynamic chassis margin data sets respectively corresponding to the wheels; (B) By means of the chassis control module, after receiving a decision instruction from the decision module, calculate the required performance values of the multiple decision instructions, and determine whether the autonomous vehicle can travel according to the decision instruction based on the tire force margin data set and the dynamic chassis margin data set; (C) When it is determined that the autonomous vehicle can travel, by means of the chassis control module, calculate an actuation signal group according to the decision instruction, and output the actuation signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the decision instruction to move the autonomous vehicle; (D) When it is determined that the autonomous vehicle cannot travel, by means of the chassis control module, output a request signal indicating adjustment of the decision instruction to the decision module; and (E) After step (D), when the chassis control module does not receive an adjusted decision instruction within a predetermined time period, by means of the chassis control module, estimate the required sideslip angles respectively corresponding to the wheels, and calculate an actuation margin signal group based on the tire force margin data set, the dynamic chassis margin data set, the required sideslip angles, and the lever center of mass of the autonomous vehicle, and output the actuation margin signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the actuation margin signal group to move the autonomous vehicle.

2. The modal control method of the autonomous vehicle according to claim 1, characterized in that: It further comprises the following steps: (F) By means of the chassis control module, perform a preliminary check on the dynamic drive system and the chassis control module; and (G) When the preliminary check indicates that either the dynamic drive system or the chassis control module is in either a non-enabled state or an abnormal state, by means of the chassis control module, generate a stop actuation signal and output the stop actuation signal to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system to stop the autonomous vehicle.

3. The modal control method of the autonomous vehicle according to claim 1, wherein: After step (D), when the chassis control module receives the adjusted decision instruction within the predetermined time period, the chassis control module repeats step (C) for the adjusted decision instruction.

4. The modal control method for an autonomous vehicle according to claim 1, characterized in that: In step (A), the vehicle dynamic data set for estimating the tire force margin data set includes multiple forces respectively applied to the tire along the vertical axis, the longitudinal axis, and the lateral axis; the chassis control module further calculates the aligning torque calculated from the vehicle dynamic data set, and calculates the road adhesion coefficient based on the aligning torque; and the chassis control module further estimates the tire force margin data set based on the aligning torque and the road adhesion coefficient.

5. The modal control method for an autonomous vehicle according to claim 4, wherein: In step (A), the dynamic chassis margin data set includes: a steering margin sub-data set having an angular velocity margin and a current-torque margin; a motor power margin sub-data set having a speed margin and an acceleration margin; and a brake torque margin sub-data set having a pressure margin and a brake pressure.

6. The modal control method of an autonomous vehicle according to claim 5, characterized in that: The decision instruction includes a plurality of waypoints and a specified speed. Step (B) includes the following sub-steps: (B-1) By means of the chassis control module, performing polynomial curve fitting on the waypoints to obtain a specified trajectory curve; (B-2) By means of the chassis control module, determining the curvature of the specified trajectory curve; and (B-3) By means of the chassis control module, calculating the required performance value based at least on the specified speed and the curvature of the specified trajectory curve.

7. The modal control method of the autonomous vehicle according to claim 6, wherein: The required performance value includes a longitudinal speed. Determining whether the autonomous vehicle can travel according to the decision instruction in step (B) includes the following sub-steps: (B-4) By means of the chassis control module, determining the longitudinal speed from the specified speed; (B-5) By means of the chassis control module, calculating a lateral acceleration margin according to the steering margin sub-data set and the motor power margin sub-data set; (B-6) Calculating a longitudinal speed threshold according to the lateral acceleration margin calculated in (B-5) and the curvature of the specified trajectory curve; and (B-7) Comparing the longitudinal speed threshold and the longitudinal speed. When the longitudinal speed threshold is greater than the longitudinal speed, it is determined that the autonomous vehicle can travel.

8. The modal control method of an autonomous vehicle according to claim 6, characterized in that: The required performance value includes an applied steering angle. Step (C) includes the following sub-steps: (C-1) By means of the chassis control module, generating a steering instruction according to the applied steering angle and the specified speed; (C-2) By means of the chassis control module, inputting the specified speed into a proportional-integral-derivative controller to obtain an applied acceleration related to one of the power system and the brake subsystem, and generating an acceleration instruction indicating the applied acceleration; and (C-3) By means of the chassis control module, generating the actuation signal set including the steering instruction and the acceleration instruction.

9. The modal control method for an autonomous vehicle according to claim 6, wherein: Step (E) includes the following sub-steps: (E-1) By means of the chassis control module, for each wheel, estimating the required sideslip angle of the wheel based at least on the current speed of the autonomous vehicle and the force applied to the wheel along the vertical axis; and (E-2) By means of the chassis control module, calculating the actuation margin signal set, where step (E-2) includes the following sub-steps: (E-2-1) By means of the chassis control module, calculating the applied lateral force of the autonomous vehicle based at least on the required sideslip angle of the wheel, and generating a lateral force instruction indicating the applied lateral force, (E-2-2) By means of the chassis control module, calculating the distance between the lever center of mass of the autonomous vehicle and the specified trajectory curve, (E-2-3)Via the chassis control module, calculate the required longitudinal acceleration of the autonomous vehicle according to the distance, the applied lateral force, and the curvature of the specified trajectory curve, and generate a longitudinal acceleration command corresponding to the required longitudinal acceleration, and (E-2-4)Via the chassis control module, generate the actuation margin signal group including the lateral force command and the longitudinal acceleration command.

10. The modal control method for an autonomous vehicle according to claim 1, characterized in that: Calculating the actuation signal group in step (C) includes performing driving comfort adjustment on the actuation signal group according to an expected vibration data set generated according to a plurality of expected actions generated by the autonomous vehicle according to the actuation signal group.

11. A chassis control module is applicable to controlling the modes of an autonomous vehicle. The autonomous vehicle includes a chassis, a dynamic drive system, an information platform, a decision-making module, and an actuator controller module. The dynamic drive system includes a plurality of wheels, motors, a steering subsystem, a power system, and a braking subsystem mounted on the chassis of the autonomous vehicle. The information platform includes a plurality of sensor groups disposed on a plurality of parts of the autonomous vehicle. The parts include the wheels. The sensor groups are used to obtain a plurality of vehicle dynamic data sets when the autonomous vehicle is traveling. The decision-making module is used to calculate and generate decision-making instructions for maneuvering the autonomous vehicle. The chassis control module is coupled to the information platform and the decision-making module. The actuator controller module is coupled to the chassis control module and the dynamic drive system. It is characterized in that: The chassis control module includes: A dynamic margin identifier, designed to receive the vehicle dynamic data set obtained by the information platform, perform vehicle operation margin estimation according to the vehicle dynamic data set and a plurality of executable performance values related to the dynamic drive system, so as to estimate a plurality of tire force margin data sets corresponding to the wheels respectively and a plurality of dynamic chassis margin data sets corresponding to the wheels respectively; An integrated controller connected to the dynamic margin identifier, For receiving the decision instruction, calculating the required performance values of the plurality of decision instructions, and determining whether the autonomous vehicle can travel according to the decision instruction according to the tire force margin data set and the dynamic chassis margin data set estimated by the dynamic margin identifier, When it is determined that the autonomous vehicle can travel according to the decision instruction, calculate an actuation signal group according to the decision instruction, And output the actuation signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the decision instruction to move the autonomous vehicle; Wherein, when it is determined that the autonomous vehicle cannot travel according to the decision instruction, the integrated controller outputs a request signal indicating adjustment of the decision instruction to the decision module; And After a predetermined time period after outputting the request signal, when the integrated controller does not receive an adjusted decision instruction within the predetermined time period, the integrated controller estimates a plurality of required sideslip angles corresponding to the wheels respectively, and calculates an actuation margin signal group according to the tire force margin data set, the dynamic chassis margin data set, the required sideslip angle, and the lever centroid related to the autonomous vehicle, and outputs the actuation margin signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the actuation margin signal group to move the autonomous vehicle.

12. The chassis control module according to claim 11, wherein: The autonomous vehicle further includes a chassis self-check module, and the chassis control module further includes a self-diagnosis unit. The self-diagnosis unit is connected to the chassis self-check module and performs a preliminary check on the dynamic drive system and the chassis control module. Wherein, when the preliminary inspection indicates that one of the dynamic drive system and the chassis control module is in one of the unenabled state and the abnormal state, the self-diagnosis unit controls the integrated controller to generate a stop actuation signal and output the stop actuation signal to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system to stop the autonomous vehicle.

13. The chassis control module according to claim 11, wherein: When the integrated controller receives the adjusted decision instruction within the predetermined time period after outputting the request signal, the integrated controller calculates the actuation signal group according to the adjusted decision instruction and outputs the actuation signal group to the actuator controller module, so that the actuator controller module can actuate the dynamic drive system according to the decision instruction to move the autonomous vehicle.

14. The chassis control module according to claim 11, wherein: The dynamic margin identifier is used to estimate the vehicle dynamic data set of the tire force margin data set, and the vehicle dynamic data set includes a plurality of forces applied to the tire along the vertical axis, the longitudinal axis, and the lateral axis respectively; The dynamic margin identifier also calculates the aligning torque calculated from the vehicle dynamic data set and calculates the road adhesion coefficient according to the aligning torque; And The dynamic margin identifier also estimates the tire force margin data set according to the aligning torque and the road adhesion coefficient.

15. The chassis control module according to claim 14, characterized in that: The dynamic chassis margin data set calculated by the dynamic margin identifier includes: A steering margin sub-data set having an angular velocity margin and a current-torque margin; A motor power margin sub-data set having a speed margin and an acceleration margin; and A brake torque margin sub-data set having a pressure margin and a brake pressure.

16. The chassis control module according to claim 15, wherein: The decision instruction includes a plurality of waypoints and a specified speed, and the integrated controller: Performs polynomial curve fitting on the waypoints to obtain a specified trajectory curve; Determines the curvature of the specified trajectory curve; And Calculates the required performance value at least according to the specified speed and the curvature of the specified trajectory curve.

17. The chassis control module according to claim 16, characterized in that: The required performance value includes a longitudinal speed, and the integrated controller is designed to determine whether the autonomous vehicle can travel according to the decision instruction in the following manner: Determines the longitudinal speed from the specified speed; Calculates a lateral acceleration margin according to the steering margin sub-data set and the motor power margin sub-data set; Calculates a longitudinal speed threshold according to the lateral acceleration margin and the curvature of the specified trajectory curve; and Compares the longitudinal speed threshold and the longitudinal speed, and when the longitudinal speed threshold is greater than the longitudinal speed, determines that the autonomous vehicle can travel.

18. The chassis control module according to claim 16, characterized in that: The required performance value includes an applied steering angle, and the integrated controller calculates the actuation signal group in the following manner: Generates a steering instruction according to the applied steering angle and the specified speed; Inputs the specified speed into a proportional-integral-derivative controller to obtain an applied acceleration related to one of the power system and the brake subsystem, and generates an acceleration instruction indicating the applied acceleration; And Generates the actuation signal group including the steering instruction and the acceleration instruction.

19. The chassis control module according to claim 16, wherein: For each wheel, the integrated controller estimates the required slip angle of the wheel based at least on the current speed of the autonomous vehicle and the force applied to the wheel along the vertical axis; The integrated controller calculates the actuation margin signal set in the following manner: Calculates the lateral force applied by the autonomous vehicle based at least on the required slip angle of the wheel, and generates a lateral force command indicating the applied lateral force, Calculates the distance between the lever center of mass of the autonomous vehicle and the specified trajectory curve, Calculates the required longitudinal acceleration of the autonomous vehicle based on the distance, the applied lateral force, and the curvature of the specified trajectory curve, and generates a longitudinal acceleration command corresponding to the required longitudinal acceleration, and Generates the actuation margin signal set including the lateral force command and the longitudinal acceleration command.

20. The chassis control module according to claim 16, characterized in that: Further includes a comfort adjustment unit connecting the integrated controller and the actuator controller module. The comfort adjustment unit responds to the actuation signal set received from the integrated controller, performs driving comfort adjustment on the actuation signal set according to the expected vibration data set generated by the actuation signal set, to generate an adjusted actuation signal set, and outputs the adjusted actuation signal set to the actuator controller module.

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