CN108415237B - Embedded high-reliability automatic driving controller based on software and hardware redundancy scoring model - Google Patents

Embedded high-reliability automatic driving controller based on software and hardware redundancy scoring model Download PDF

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CN108415237B
CN108415237B CN201810070246.4A CN201810070246A CN108415237B CN 108415237 B CN108415237 B CN 108415237B CN 201810070246 A CN201810070246 A CN 201810070246A CN 108415237 B CN108415237 B CN 108415237B
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automatic driving
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黄凯
杨俊杰
朱笛
李博洋
张文权
崔明月
晏荣杰
刘杰
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Sun Yat Sen University
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract

The invention relates to the technical field of automatic driving, in particular to an embedded high-reliability automatic driving controller based on a software and hardware redundancy scoring model. An embedded high-reliability automatic driving controller based on a software and hardware redundancy scoring model comprises an automatic driving controller, an input module, a processing module and an output module, wherein the input module, the processing module and the output module are respectively connected with the automatic driving controller; the input module is connected with the upper sensor, the processing module is respectively connected with the transverse controller and the longitudinal controller, and the output module is connected with the bottom hardware. The invention improves the performance of the unmanned controller on fault tolerance and power consumption, reduces the probability of downtime or malfunction of the unmanned controller in actual complex road conditions, and improves the safety of the unmanned vehicle; the real-time controllability of the whole system is improved by using the RTOS, and the adaptive capacity of the controller is obviously improved by combining with the perfect control logic. The overall experience is consistently close to or even exceeds the driving level of an excellent driver.

Description

Embedded high-reliability automatic driving controller based on software and hardware redundancy scoring model
Technical Field
The invention relates to the technical field of automatic driving, in particular to an embedded high-reliability automatic driving controller based on a software and hardware redundancy scoring model.
Background
The automatic driving controller obtains the position, the attitude and the speed information of the current vehicle based on a laser radar and a GPS positioning module, and then outputs control instructions of steering, an accelerator and a brake according to a given target track. However, many existing controllers only focus on the implementation of basic functions, neglect non-functional requirements, have weak fault tolerance and comfort, and have the problems of high cost and poor expandability of an operation platform.
Disclosure of Invention
The embedded high-reliability automatic driving controller based on the software and hardware redundancy scoring model has high control precision, operates on embedded hardware with low power consumption, ensures real-time performance by applying an RTOS (real-time operating system) technology, ensures reliability by adopting a perfect error detection mechanism and a redundancy voting design, can dynamically adjust an acceleration and steering strategy according to the actual working condition of a vehicle to ensure comfort, and greatly improves the comprehensive performance of the controller under the actual complex scene.
The technical scheme of the invention is as follows: an embedded high-reliability automatic driving controller based on a software and hardware redundancy scoring model comprises an automatic driving controller, an input module, a processing module and an output module, wherein the input module, the processing module and the output module are respectively connected with the automatic driving controller;
the input module is connected with the upper sensor, the processing module is respectively connected with the transverse controller and the longitudinal controller, and the output module is connected with the bottom hardware.
Furthermore, the upper sensor comprises a position and attitude sensor and a target track sensor. The bottom hardware comprises an accelerator, a brake and a steering angle.
In the aspect of hardware, n blocks (n is an odd number) of low-power-consumption embedded computing platforms, namely, raspberry pi 3Model B, are adopted to realize redundancy design, controllers and corresponding error detection programs are independently operated on the raspberry pi platforms, then, a voter is realized at the tail end of the platform, and the output of the current controller is selected by using the principle of 'majority domination-equal number optimization'.
In the aspect of control algorithm, a set of advanced control algorithm is designed, and vehicle control is decoupled and controlled by transverse turning and longitudinal vehicle speed: the transverse controller calculates feedforward control according to the speed and the track curvature of the vehicle and calculates feedback control according to the position deviation and the course deviation; the longitudinal controller then generally employs PID control while taking into account the effect of vehicle pitch angle. By separating the horizontal control and the vertical control, the efficient operation of each control logic is ensured, and the dependence of the two controls is taken into consideration in the logic processing, so that the route fitting with extremely small error is completed in the actual driving test, and the high-precision performance of the controller is reflected. The dynamic optimization parameters are combined with the actual working conditions of the vehicle, so that the position and the posture of the vehicle are controlled more smoothly, and the running state of the vehicle is more stable.
In the aspect of software implementation, the non-functional requirements are emphasized, and the robustness of the controller is improved by adding detection and processing of various possible code errors. The RTOS is used for ensuring that all processes (and threads) in the controller are in a real-time running state which can be monitored, has low delay and is not overtime, and the controllability of the system is improved. From the perspective of passengers, turning and vehicle speed control of the vehicle are further optimized based on riding experience, the running stability of the vehicle is obviously improved, and good riding comfort is provided for the passengers.
Compared with the prior art, the beneficial effects are: the invention improves the performance of the unmanned controller on fault tolerance and power consumption, reduces the probability of downtime or malfunction of the unmanned controller in actual complex road conditions, and improves the safety of the unmanned vehicle; the real-time controllability of the whole system is improved by using the RTOS, and the adaptive capacity of the controller is obviously improved by combining with the perfect control logic. The overall experience is consistently close to or even exceeds the driving level of an excellent driver.
Drawings
FIG. 1 is a schematic diagram of the structural framework of the controller of the present invention.
FIG. 2 is a task flow diagram of the control algorithm of the present invention.
Fig. 3 is a schematic diagram of the working principle of the voting machine placed at the output position by the redundant design of the present invention.
FIG. 4 is a schematic diagram of the present invention identifying a predetermined trajectory and an actual trajectory of a vehicle on a lateral control.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent; for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent.
Hardware aspect:
the sensor sends the relevant data to the controller for processing. The controller hardware is composed of n raspberry pi low-power-consumption platforms, each raspberry pi runs an independent controller processing program, and the stable operation of the whole controller system can be ensured as long as the normal operation of one raspberry pi is ensured. The redundant design of the hardware level improves the reliability of the controller in a complex environment. And each low-cost micro-computing module raspberry pie only needs 5V voltage and about 0.5A current, so that the overall power consumption is low, and the feasibility of redundant design is ensured.
Software aspect:
the redundancy design is realized on the software level, m (m is more than or equal to n) independent controller processes can be operated on n raspberry groups, and the reliability of the whole system is also improved. Each controller process corresponds to a set of independent error detection programs, errors which may occur in the controller programs are monitored, and the output of the error detection programs comprises: SUC (no errors), DUF (detectable errors), and SDC (undetectable errors). A voter is realized at the output end of the controller, and a final control instruction is selected through reasonable voting logic and sent to bottom hardware by summarizing the output of each independent controller program and the result of error detection, so that the influence of random software and hardware errors on the safety and reliability of the whole system is reduced.
The transverse control and the longitudinal control of the vehicle are separated, and the specific control logic is as follows:
Figure RE-GDA0001646306660000021
the above formula is a vehicle lateral control logic, and is explained as follows: δ represents a steering angle output, L represents a wheel base,K、Kp、Ki、
Figure RE-GDA0001646306660000022
respectively representing a feedforward weight coefficient, a lane keeping weight coefficient, an integral weight coefficient, a damping weight coefficient, UxRepresenting the current speed of the vehicle, R(s) representing the curvature of the trajectory curve at the current point, CfRepresenting the front axle tire angular stiffness, a representing the distance of the front axle to the vehicle's center of mass, e (t) representing the distance between the current position and the nearest point on the trajectory, xlaIndicating the distance to look ahead, # indicates the heading angle, and Δ # indicates the difference between the current heading angle and the heading angle of the nearest point on the track.
Figure RE-GDA0001646306660000031
The above formula is a vehicle longitudinal control logic, and is explained as follows: kp、KiAnd K1All represent a weight coefficient, UdModule, U, representing target vehicle speednThe modulus represents the current vehicle speed, g represents the gravitational acceleration, and theta represents the current pitch angle of the vehicle. Because the riding experience is seriously influenced by the frequent change of the brake, the brake output is also processed in a grading way and is divided into 6 grades, thereby ensuring that the position of the brake pedal in a certain output range is unchanged.
The controller program runs on the RTOS, programming is carried out by using the related technology of a real-time system, each thread of the controller process is ensured to run in the environment with low system delay and artificially controllable, and experiments prove that the method obviously reduces the probability of overtime processing of the system thread and improves the safety of the whole driving system.
Through carrying out comprehensive monitoring to the vehicle operating mode, accomplish the high accuracy control to the vehicle in combination with overall control logic, further promoted the reliability of controller, reduce the potential safety hazard that coarse control brought to from the angle of experience by bus, with turning and vehicle control such as acceleration further level and smooth, improved the travelling comfort that brings by bus of controller.
Fig. 1 is a controller overall structure frame. The input module acquires data sent by an upper sensor; the processing module analyzes the sensor data, wherein the transverse controller is responsible for turning logic (a steering wheel), and the longitudinal controller is responsible for vehicle speed control (an accelerator and a brake); the output module is responsible for sending the control instruction obtained by processing to bottom hardware, and respectively controls accelerator treading, brake treading and steering of a steering wheel.
Fig. 2 is a task flow introduction of the control algorithm. The acquisition of the position and the posture of the vehicle is completed by the upper sensor and is sent to the controller for processing through a communication mechanism. Since the horizontal control and the vertical control have a certain logical dependency relationship, the actual processing is performed by serial logic. And acquiring a target track, finishing operations such as coordinate and data domain transformation and the like in a preprocessing stage, and then recalculating partial parameters through feedback control to finish optimization of control logic. The controller part which is communicated with the sensor receives and processes data at the frequency of 100 Hz; the part of the controller that fits the preset path is processed at a frequency of 10 Hz.
Figure 3 demonstrates the working principle of a voter placed in an output position by a redundant design. At a specific time point, the voter collects signals from all the controller instances and the corresponding error detectors, judges whether the current system state is SUC, DUF or SDC according to the statistical condition of the errors detected by the error detectors, and outputs a corresponding control instruction according to the state.
Fig. 4 identifies the predetermined trajectory and the actual trajectory of the vehicle on the lateral control. As can be seen from the upper half part of the upper graph, the fitting degree of the actual track of the vehicle to the preset track is very high, which shows that the controller has very high precision and excellent control performance.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (3)

1. An embedded high-reliability automatic driving controller based on a software and hardware redundancy scoring model is characterized by comprising an automatic driving controller, an input module, a processing module and an output module, wherein the input module, the processing module and the output module are respectively connected with the automatic driving controller;
the input module is connected with the upper sensor, the processing module is respectively connected with the transverse controller and the longitudinal controller, and the output module is connected with the bottom hardware;
hardware aspect:
the sensor sends the relevant data to the controller for processing; the controller hardware consists of n raspberry pi low-power-consumption platforms, each raspberry pi runs an independent controller processing program, and the stable operation of the whole controller system can be ensured as long as the normal operation of one raspberry pi is ensured;
software aspect:
the redundancy design is also realized on the software level, m (m is more than or equal to n) independent controller processes can be operated on n raspberry blocks, each controller process corresponds to a set of independent error detection program, errors possibly occurring in the controller programs are monitored, and the output of the error detection programs comprises: SUC, DUF and SDC; a voter is realized at the output end of the controller, and a final control instruction is selected through reasonable voting logic and sent to bottom hardware by summarizing the output of each independent controller program and the result of error detection;
the transverse control and the longitudinal control of the vehicle are separated, and the specific control logic is as follows:
Figure FDA0002932388550000011
the above formula is a vehicle lateral control logic, and is explained as follows: δ represents steering angle output, δfeedforwardRepresenting steering angle feed-forward output, δfeedbackIndicating steering angle feedbackOut, lambdadampingRepresenting rate of reduction, L representing wheel base, K, Kp、Ki、
Figure FDA0002932388550000012
Respectively representing a feedforward weight coefficient, a lane keeping weight coefficient, an integral weight coefficient, a damping weight coefficient, UxRepresenting the current speed of the vehicle, R(s) representing the curvature of the trajectory curve at the current point, CfRepresenting the front axle tire angular stiffness, a representing the distance of the front axle to the vehicle's center of mass, e (t) representing the distance between the current position and the nearest point on the trajectory, xlaIndicating the distance to look ahead, # denotes the heading angle, and Δ # denotes the difference between the current heading angle and the heading angle of the nearest point on the track;
Figure FDA0002932388550000013
the above formula is a vehicle longitudinal control logic, and is explained as follows: kp、KiAnd K1All represent weight coefficients, U (t) represents braking distance, UdModule, U, representing target vehicle speednRepresenting the current speed of the vehicle, g representing the acceleration of gravity, and theta representing the current pitch angle of the vehicle; because the riding experience is seriously influenced by the frequent change of the brake, the brake output is also processed in a grading way and is divided into 6 grades, thereby ensuring that the position of the brake pedal in a certain output range is unchanged.
2. The embedded high-reliability automatic driving controller based on the software and hardware redundancy scoring model as claimed in claim 1, wherein: the upper sensor comprises a position and attitude sensor and a target track sensor.
3. The embedded high-reliability automatic driving controller based on the software and hardware redundancy scoring model as claimed in claim 1, wherein: the bottom hardware comprises an accelerator, a brake and a steering angle.
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