Human-machine collaborative steering control system test bench based on hardware-in-the-loop

Abstract

The present invention relates to the field of human-machine collaborative steering control technology, and in particular to a human-machine collaborative steering control system test bench based on hardware-in-the-loop. A steering wheel, a steering wheel controller, a hardware-in-the-loop device, a front-wheel steering module, a sensor module, a driver biological collection module, and a human-machine collaborative control evaluation module. The human-machine collaborative steering system test bench of the present invention can perform hardware-in-the-loop simulation tests on the designed human-machine collaborative steering system under different working conditions to improve the research and development efficiency and reliability of the human-machine co-driving steering system and reduce research and development costs; the collaborative steering evaluation system not only considers the vehicle operating status, but also proposes to use multiple indicators such as driver biological information indicators to comprehensively evaluate the human-machine collaborative steering control effect.

Description

Technical Field

[0001] The present invention relates to the technical field of human-machine collaborative steering control, and in particular to a human-machine collaborative steering control system test bench based on hardware-in-the-loop. Background Art

[0002] Smart cars are complex systems integrating environmental perception, planning and decision-making, and control execution. The development of smart car technology is bound to progress from partially autonomous driving to fully autonomous driving. To achieve fully autonomous driving, there are four stages: driver assistance, partially autonomous driving, highly autonomous driving, and finally fully autonomous driving.

[0003] Human-machine collaboration, or co-driving control, can be seen as the transition from manned to unmanned driving. Control of the vehicle is shared between the driver and the vehicle, with shared control of the steering system being particularly crucial. In this stage, in addition to the vehicle itself controlling steering, the driver also participates in steering control. This requires not only controlling steering wheel rotation based on signals from the steering angle sensor, but also the driver's input of steering wheel torque and angle. Human and vehicle steering control must work in tandem. Imperfect coordination can inevitably lead to fluctuations in steering wheel angle or speed, resulting in steering wheel judder, which can seriously impact driving safety.

[0004] Currently, steering test systems are all developed for electric power steering systems. They are unable to verify controller algorithms for various operating conditions such as human-machine collaborative driving, fully autonomous driving, and driver-only driving, and they also lack an evaluation of the degree of human-machine collaboration. Summary of the Invention

[0005] The purpose of the present invention is to provide a human-machine collaborative steering control system test bench based on hardware in the loop, which is used in the vehicle development stage. This platform can be used to verify the steering system control algorithm under various working conditions, especially under extreme working conditions that are difficult to repeat. It can greatly reduce the number of actual vehicle tests, shorten the development cycle, and realize the testing of the actual control effect of the developed intelligent car steering control algorithm, and evaluate the human-machine collaborative steering coordination. It provides a human-machine collaborative steering control system test bench based on hardware in the loop, which can effectively solve the following existing technical problems: Currently, steering test systems are all developed for electric power steering systems, and cannot verify controller algorithms for various working conditions such as human-machine collaborative driving, fully automatic driving, and driver driving alone, and lack evaluation of the degree of human-machine collaboration.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The human-machine collaborative steering control system based on hardware in the loop includes:

[0008] Hub, hub controller, hardware-in-the-loop equipment, front-wheel steering module, sensor module, driver biometric collection module, and human-machine collaborative control evaluation module;

[0009] The hardware-in-the-loop device stores a vehicle dynamics model and an intelligent vehicle steering control algorithm, and outputs a PWM signal to drive the front wheel steering module to steer;

[0010] The hardware-in-the-loop device is connected to the hub controller signal to control the hub speed according to the vehicle dynamics model;

[0011] The human-machine collaborative control evaluation module is used to provide evaluation information based on the vehicle operating status and the driver status.

[0012] Furthermore, the front wheel steering module includes a steering wheel, a steering column, a steering motor, a rack and pinion reduction mechanism, a steering tie rod, and a steering wheel;

[0013] The steering wheel is connected to the torque input end of the rack and pinion reduction mechanism through the steering column. The other torque input end of the rack and pinion reduction mechanism is connected to the torque output end of the steering motor. The torque output end of the rack and pinion reduction mechanism is connected to the steering wheel through the steering tie rod.

[0014] Furthermore, the sensor module includes a pressure sensor arranged on the steering wheel, a torque sensor arranged between the steering column and the input end of the rack and pinion reduction mechanism; and a speed sensor arranged on the wheel hub.

[0015] A torque sensor is provided between the steering column and the input end of the rack and pinion reduction mechanism, and the steering motor is controlled by a hardware-in-the-loop device.

[0016] Furthermore, the driver's biological collection module includes an EMG surface electromyography instrument and a biofeedback instrument.

[0017] Furthermore, the control method of the intelligent vehicle steering control algorithm includes:

[0018] There are three modes: automatic driving, human-machine co-driving and driver-only driving;

[0019] If the driver does not select the driver-only mode, then:

[0020] Detecting the pressure input by the driver through the pressure sensor on the steering wheel;

[0021] If the pressure input by the driver is detected, it is determined to be human-machine co-driving mode;

[0022] If no pressure from the driver's input is detected, it is determined to be in autonomous driving mode.

[0023] Furthermore, the evaluation method of the human-machine collaborative control evaluation module is as follows:

[0024] The driver's electromyographic signals are collected through the EMG surface electromyography instrument, and the driver's blood flow pulse BVP and skin conductance SC are collected through the biofeedback instrument;

[0025] Then the collected values are normalized and multiplied by different weight coefficients α;

[0026] Then add them together to form the evaluation index β;

[0027] Let μ be the blood flow pulse value, let ν be the skin conductance value, and let β be the sum of these two values after adding weights. The evaluation index is expressed as:

[0028] β=α·1μ+α2ν

[0029] During the human-machine collaborative steering process, the driver's satisfaction was evaluated as follows:

[0030]

[0031] E d is the driver satisfaction, the integral of the square of the driver torque in the time interval [t1, t2], which is the energy required by the driver to turn;

[0032] The degree of human-machine coordination needs to be described during the steering process between the driver and the steering system. The degree of human-machine coordination is expressed as:

[0033]

[0034] W d The degree of coordination between human and machine operation, where y cg is the distance the vehicle deviates from the lane line, W d The lower the value, the worse the human-machine collaboration;

[0035] During the human-machine collaborative steering process, there is a deviation between the driver's intention and the auxiliary steering system's intention. To characterize the degree of this deviation, the concept of conflict level is proposed. The conflict level is expressed as:

[0036]

[0037] T d is the driver torque vector, T c is the auxiliary torque vector.

[0038] The present invention has at least the following beneficial effects:

[0039] 1. The human-machine collaborative steering system test bench of this invention can perform hardware-in-the-loop simulation testing of the designed human-machine collaborative steering system under different operating conditions, thereby improving the efficiency and reliability of the research and development of human-machine collaborative steering systems while reducing R&D costs. During the vehicle development phase, the robustness of the intelligent vehicle steering control algorithm can be evaluated and tested under various operating conditions, especially under difficult-to-repeat extreme conditions, thereby reducing the number of actual vehicle tests and saving development costs.

[0040] 2. The human-machine collaborative steering evaluation system of the present invention not only takes into account the vehicle operating status, but also proposes to use multiple indicators such as driver bio-information indicators to comprehensively evaluate the human-machine collaborative steering control effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 is a schematic diagram of the system;

[0043] Figure 2 This is a framework diagram of the evaluation process. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0045] See Figure 1 and Figure 2 :

[0046] The entire human-machine collaborative steering system test bench of the present invention includes: a road hub, hardware-in-the-loop equipment, a front-wheel steering module, a sensor module, a driver's biological data collection module and a human-machine collaborative control evaluation module.

[0047] The front-wheel steering module includes a steering wheel, a steering column, a steering motor, a gear rack reduction mechanism, a steering tie rod, a steering wheel, etc.; the sensors include a pressure sensor on the steering wheel, a torque sensor on the steering column, and a speed sensor on the wheel hub; the driver's biological collection module includes an EMG surface electromyography instrument and a biofeedback instrument.

[0048] The vehicle dynamics model and intelligent vehicle steering control algorithm run within a hardware-in-the-loop (HIL) device, which outputs a PWM signal to drive the steering motor. The PWM pulse width depends on the torque distribution coefficient λ, which is calculated by the vehicle dynamics model. The HIL device is connected to the hub controller via Ethernet to control the hub speed based on the virtual road scenario in the vehicle control model.

[0049] Vehicle dynamics models are a common technology, and applications such as CarSim, CarMaker, MATLAB / Simulink, and Panosim can all create dynamic models. Vehicle steering algorithms are also relatively mature, with the simplest being pure tracking algorithms. There are also methods based on visual recognition, radar, and other methods for steering. This article will not elaborate on this topic.

[0050] In the above scheme, the steering wheel is connected to the torque input of the rack and pinion reduction mechanism via the steering column. The other torque input of the rack and pinion reduction mechanism is connected to the torque output of the steering motor. The torque output of the rack and pinion reduction mechanism is connected to the steering wheel via a steering tie rod. A torque sensor is installed between the steering column and the input of the rack and pinion reduction mechanism. The steering motor is controlled by a hardware-in-the-loop device.

[0051] In the above scheme, the human-machine collaborative steering system test bench includes three modes: automatic driving, human-machine co-driving and driver-only driving. In the human-machine co-driving mode, the driver and the automatic control system jointly control the steering. After the person inputs the torque on the steering wheel (obtained by the speed torque sensor on the steering column), the steering control algorithm will calculate the torque that should be applied to the steering motor based on the person's steering torque. The steering motor is controlled by the output PWM signal of the hardware-in-the-loop device, thereby cooperating to make the person's steering control smooth, easy and smooth. When the pressure sensor on the steering wheel does not detect the pressure input by the driver, it enters the fully automatic driving mode and the vehicle steering is controlled by the automatic driving algorithm.

[0052] Regarding the torque that should be applied to the steering motor, the driver torque T h , Steering motor torque T m and the total steering torque T req The relationship between them is:

[0053] λT h +(1-λ)T m =T req

[0054] It is understood that autonomous driving algorithms are a type of existing technology, primarily encompassing pure vision solutions and fusion perception (vision and lidar) solutions. Relevant R&D companies include: Internationally, Waymo, Velodyne, Uber, Mobileye, Bosch, Apple, Google, Tesla, Minieye0, AutoX1, Voyage2, and MaxiEye; and domestically, TuSimple, pony.ai, Xijing Technology, UISEE Technology, Zongmu Technology, Baidu, WeRide, Alibaba, Tencent, Xiantu Intelligent, Didi Chuxing, Geely, Xpeng Motors, NIO, Horizon Robotics, NavInfo, Monmenta, Cambricon, HoloMatic, Fabu Technology, PlusAI, Jingchi Technology, Singularity Motors, HiRain, Pathfinder Vision, DeepBlue Technology, Magic Vision 8, OFI, SAIC, WM Motor, and Nullmax. The specifics of autonomous driving algorithms are not part of this application and will not be elaborated upon.

[0055] Driver torque T h , Steering motor torque T m and the total steering torque T req The relationship between them is:

[0056] λT h +(1-λ)T m =T req

[0057] In this solution, the sensor module includes a steering wheel pressure sensor, a steering torque sensor, and a vehicle speed sensor. The hardware-in-the-loop device collects the actual deflection angle between the tire centerline and the hub as feedback. This feedback is then used to calculate the vehicle's trajectory within the dynamics model, providing an average value of the vehicle's deviation from the center road in the virtual scene.

[0058] The driver information collection system uses an EMG acquisition device to collect electromyographic signals and a biofeedback device to collect the driver's blood flow pulse (BVP) and skin conductance (SC). These values are then normalized, multiplied by different weighting coefficients α, and summed to form the evaluation index β. Let μ be the blood flow pulse value, ν be the skin conductance value, and β be the weighted sum of these two values. Therefore, the evaluation index β = α·1μ + α2ν.

[0059] The human-machine collaborative control evaluation system analyzes the steering control algorithm and the degree of human-machine collaboration by collecting driver and vehicle operating status information. Specifically, the actual deflection angle between the wheel hub and the wheel centerline is collected to calculate the average distance the vehicle deviates from the road center in virtual road conditions, simulating the vehicle's actual operating trajectory. This evaluation is then combined with the driver's blood flow pulse (BVP) and skin conductance (SC) β value, along with EMG signals, to objectively assess the driver's operating load. A subjective evaluation of satisfaction, human-machine coordination, and conflict-prone human-machine steering collaboration is then conducted to evaluate the steering algorithm under test.

[0060] Specific definition of indicators:

[0061] Electromyographic signal: Electromyographic signal is the temporal and spatial superposition of action potentials of motor units in skin muscle fibers. It is the electrical signal accompanying muscle contraction, and its size represents the degree of muscle contraction.

[0062] Blood flow pulse: refers to the amount of blood flowing through a certain interface of the blood vessels per unit time. The driver's blood flow pulse value is recorded within three seconds.

[0063] Skin conductance: Skin conductance is referred to as skin conductance. It is a physiological indicator for emotion research. It refers to the phenomenon that the electric current acting on the skin causes changes in skin resistance or skin potential. It is used to characterize the driver's emotional fluctuations.

[0064] Satisfaction: During the human-machine collaborative steering process, the driver evaluates the collaborative steering and expresses it in terms of satisfaction.

[0065]

[0066] E d is the driver satisfaction, and the integration of the square of the driver torque in the time interval [t1, t2] is the energy required by the driver for steering.

[0067] Human-machine coordination degree: During the steering process between the driver and the steering system, it is necessary to describe the human-machine coordination degree and propose the concept of human-machine degree.

[0068]

[0069] E d is the driver satisfaction, W d The degree of coordination between human and machine operation, where y cg is the distance the vehicle deviates from the lane line, W d The lower the value, the worse the human-machine collaboration.

[0070] Conflict level: During the human-machine collaborative steering process, there is a deviation between the driver's intention and the auxiliary steering system's intention. In order to characterize the degree of this deviation, the concept of conflict level is proposed.

[0071]

[0072] T d is the driver torque vector, T c To assist the torque vector, the cosine angle of the dot product of the two is used to describe the degree of conflict.

[0073] Evaluation indicator threshold range:

[0074]

[0075]

[0076] From the above, it can be seen that the present invention has the following technical advancements:

[0077] 1. The human-machine collaborative steering system test bench of this invention can perform hardware-in-the-loop simulation testing of the designed human-machine collaborative steering system under different operating conditions, thereby improving the efficiency and reliability of the research and development of human-machine collaborative steering systems while reducing R&D costs. During the vehicle development phase, the robustness of the intelligent vehicle steering control algorithm can be evaluated and tested under various operating conditions, especially under difficult-to-repeat extreme conditions, thereby reducing the number of actual vehicle tests and saving development costs.

[0078] 2. The human-machine collaborative steering evaluation system of the present invention not only takes into account the vehicle operating status, but also proposes to use multiple indicators such as driver bio-information indicators to comprehensively evaluate the human-machine collaborative steering control effect.

[0079] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. The human-machine collaborative steering control system based on hardware in the loop is characterized by: include: Road hub, road hub controller, hardware-in-the-loop equipment, front wheel steering module, sensor module, driver biometric collection module and human-machine collaborative control evaluation module; The front wheel steering module includes a steering wheel; The front wheel steering module also includes a steering wheel, a steering column, a steering motor, a rack and pinion reduction mechanism, and a steering tie rod; the steering wheel is connected to the torque input end of the rack and pinion reduction mechanism through the steering column, the other torque input end of the rack and pinion reduction mechanism is connected to the torque output end of the steering motor, and the torque output end of the rack and pinion reduction mechanism is connected to the steering wheel through the steering tie rod; The sensor module includes a pressure sensor installed on the steering wheel, a torque sensor installed between the steering column and the input end of the rack and pinion reduction mechanism; a speed sensor installed on the wheel hub; a torque sensor is provided between the steering column and the input end of the rack and pinion reduction mechanism, and the steering motor is controlled by a hardware-in-the-loop device; By collecting the actual deflection angle between the wheel hub and the wheel centerline, the average distance of the vehicle's deviation from the road center in the virtual road condition is calculated, and the actual running trajectory of the vehicle is simulated; The hardware-in-the-loop device stores a vehicle dynamics model and an intelligent vehicle steering control algorithm, and outputs a PWM signal to drive the front wheel steering module to steer; The hardware-in-the-loop device is connected to the road hub controller signal to control the road hub speed according to the vehicle dynamics model; The human-machine collaborative control evaluation module is used to provide evaluation information based on the vehicle operation status and the driver status; The evaluation method of the human-machine collaborative control evaluation module is as follows: The driver's electromyographic signals are collected through the EMG surface electromyography instrument, and the driver's blood flow pulse BVP and skin conductance SC are collected through the biofeedback instrument; Then the collected values are normalized and multiplied by different weight coefficients α; Then add them together to form the evaluation index β; Let μ be the blood flow pulse value, let ν be the skin conductance value, and let β be the sum of these two values after adding weights. The evaluation index is expressed as: β=α1·μ+α2·ν During the human-machine collaborative steering process, the driver's satisfaction was evaluated as follows: Ed is the driver satisfaction, which is the integration of the square of the driver torque in the time interval [t1, t2], i.e., the energy required for the driver to turn; The degree of human-machine coordination needs to be described during the steering process between the driver and the steering system. The degree of human-machine coordination is expressed as: Wd is the degree of coordination between human and machine operation, where ycg is the distance the vehicle deviates from the lane line. The lower the Wd value, the worse the degree of human-machine coordination. During the human-machine collaborative steering process, there is a deviation between the driver's intention and the auxiliary steering system's intention. To characterize the degree of this deviation, the concept of conflict level is proposed. The conflict level is expressed as: Td is the driver torque vector and Tc is the assist torque vector.

2. The human-machine collaborative steering control system test bench based on hardware-in-the-loop according to claim 1 is characterized in that: The intelligent vehicle steering control algorithm and its control method include: There are three modes: automatic driving, human-machine co-driving and driver-only driving; If the driver does not select the driver-only driving mode: Detecting the pressure input by the driver through a pressure sensor on the steering wheel; If the pressure input by the driver is detected, it is determined to be human-machine co-driving mode; If no pressure from the driver's input is detected, it is determined to be in autonomous driving mode.

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