CN116923405A - Method and system for judging curve trafficability of automatic driving vehicle and electronic equipment - Google Patents

Method and system for judging curve trafficability of automatic driving vehicle and electronic equipment Download PDF

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Publication number
CN116923405A
CN116923405A CN202210346292.9A CN202210346292A CN116923405A CN 116923405 A CN116923405 A CN 116923405A CN 202210346292 A CN202210346292 A CN 202210346292A CN 116923405 A CN116923405 A CN 116923405A
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China
Prior art keywords
vehicle
curve
center line
steering angle
parameters
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Pending
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CN202210346292.9A
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Chinese (zh)
Inventor
汪伟
刘光伟
朱田
彭之川
刘修扬
张智腾
张勇
朱泽敏
吴炳瑶
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Changsha CRRC Zhiyu New Energy Technology Co Ltd
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Changsha CRRC Zhiyu New Energy Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by Changsha CRRC Zhiyu New Energy Technology Co Ltd filed Critical Changsha CRRC Zhiyu New Energy Technology Co Ltd
Priority to CN202210346292.9A priority Critical patent/CN116923405A/en
Publication of CN116923405A publication Critical patent/CN116923405A/en
Pending legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18145—Cornering
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0015—Planning or execution of driving tasks specially adapted for safety
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/53—Road markings, e.g. lane marker or crosswalk
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/40—High definition maps

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Traffic Control Systems (AREA)

Abstract

A method, a system and an electronic device for judging the curve trafficability of an automatic driving vehicle are provided, wherein the method comprises the steps of obtaining a lane central line, running parameters and vehicle inherent parameters including a maximum steering angle; acquiring a transverse tracking error of the vehicle and the lane center line according to the lane center line and the running parameters; calculating a vehicle steering angle by applying a preset vehicle steering geometric model according to the inherent parameters, the running parameters and the transverse tracking errors of the vehicle; comparing the steering angle of the vehicle with the maximum steering angle, and acquiring a vehicle curve trafficability judging result according to the comparing result, wherein the method has simple related parameters, and prejudging the vehicle curve trafficability by adopting a presighting algorithm based on two dynamic parameters of the longitudinal speed and the transverse tracking error of the vehicle; the vehicle steering geometric model, the lane line recognition technology and the high-precision map technology are combined, the trafficability of the vehicle curve is prejudged based on the running track of the vehicle, the problem of intelligent driving of the vehicle curve is solved, and the intellectualization and safety of a driving system are improved.

Description

Method and system for judging curve trafficability of automatic driving vehicle and electronic equipment
Technical Field
The application belongs to the technical field of automatic driving, and particularly relates to a method and a system for judging the curve trafficability of an automatic driving vehicle and electronic equipment.
Background
The automatic driving technology relies on cooperation of artificial intelligence, visual computing, radar, monitoring device and global positioning system, so that the computer can automatically and safely operate the motor vehicle without any active operation of human beings. At present, the automatic driving technology of vehicles is rapidly developed. Unlike intelligent driving trolleys, in the field of commercial vehicles or special vehicles such as buses and sanitation vehicles, the size of the vehicle is large, the length and the width of the vehicle are often larger than those of passenger vehicles, and the vehicle tends to run on the center line of a lane due to the addition of an intelligent system for vehicles such as lane keeping. Thus, the passability of a bus in a curve is a problem. If the curve passing performance is determined inaccurately, the vehicle can be driven onto a sidewalk or collide with a road guardrail when the curve passing performance is serious. In the prior art, aiming at the problem of automatic driving over-curve, the tracking performance of the vehicle is controlled and regulated in the over-curve process, but the problem of curve passing performance is not judged in advance, and particularly for a large-sized vehicle, the risk of accident caused by curve failing to pass exists.
Disclosure of Invention
The application aims to solve the technical problem of accident risk caused by inaccurate curve passing performance judgment of a large-sized vehicle by providing a method and a system for judging the curve passing performance of an automatic driving vehicle and electronic equipment.
To achieve the above object, according to an aspect of the present application, there is provided a curve trafficability determination method of an autonomous vehicle, the method including: acquiring a lane center line, running parameters and vehicle inherent parameters including a maximum steering angle; acquiring a transverse tracking error of a vehicle and the lane center line according to the lane center line and the running parameters; calculating a vehicle steering angle by applying a preset vehicle steering geometric model according to the vehicle inherent parameters, the running parameters and the transverse tracking errors; and comparing the steering angle of the vehicle with the maximum steering angle, and acquiring a vehicle curve trafficability judging result according to a comparison result.
In one embodiment, the acquiring the lane center line includes: identifying the lane centerline in front of a vehicle using lane line identification techniques; or alternatively, the process may be performed,
and acquiring vehicle positioning information, and acquiring the lane center line in front of the vehicle by taking the vehicle positioning information as an origin of a vehicle body coordinate system and combining road information given by a high-precision map.
In one embodiment, the operation parameter includes a pre-aiming distance, and the acquiring the lateral tracking error between the vehicle and the lane center line according to the lane center line and the operation parameter includes: determining a pretightening point according to the mass center of the vehicle, the pretightening distance and the lane center line; and calculating the transverse tracking error according to the mass center of the vehicle, the pre-aiming point, the pre-aiming distance and the extending line of the front direction of the vehicle.
In one embodiment, the determining the pre-aiming point according to the vehicle centroid, the pre-aiming distance and the lane center line includes: determining a circle by taking the mass center of the vehicle as an origin and the pretightening distance as a radius; and determining an intersection point of the circle and the lane center line as the pre-aiming point.
In one embodiment, the calculating the lateral tracking error according to the vehicle centroid, the pre-aiming point, the pre-aiming distance and the vehicle straight ahead direction extension line includes: determining an included angle between a connecting line between the pre-aiming point and the mass center of the vehicle and an extension line of the front direction of the vehicle; multiplying the pretightening distance by the sine value of the included angle to obtain the transverse tracking error.
In one embodiment, the operation parameters include a vehicle longitudinal speed and a control parameter, the vehicle intrinsic parameters further include a vehicle wheelbase, and the vehicle steering geometric model is:
wherein L is the wheelbase of the vehicle, and the unit is m and v x For the longitudinal speed of the vehicle, the unit is m/s, e p The unit is m, k is a control parameter, and delta is a vehicle steering angle.
In one embodiment, the method further comprises:
and when the curve passing performance judging result is not passing, switching the automatic driving mode of the vehicle into a human driving mode, or adjusting the longitudinal speed of the vehicle based on the vehicle steering geometric model until the curve passing performance judging result is passing, and when the longitudinal speed is adjusted to 0 and the curve passing performance judging result is still not passing, controlling the vehicle to stop or switching the automatic driving mode of the vehicle into the human driving mode.
In accordance with yet another aspect of the present application, there is provided a curve trafficability determination system for an automatically driven vehicle, including:
the information acquisition module is used for acquiring a lane center line, running parameters and vehicle inherent parameters including a maximum steering angle;
the tracking error calculation module is used for obtaining the transverse tracking error of the vehicle and the lane center line according to the lane center line and the running parameters
The steering angle calculation module is used for calculating a vehicle steering angle by applying a preset vehicle steering geometric model according to the vehicle inherent parameters, the running parameters and the transverse tracking errors;
and the trafficability judging module is used for comparing the steering angle of the vehicle with the maximum steering angle and acquiring a vehicle curve trafficability judging result according to a comparison result.
In one embodiment, the information acquisition module includes a lane line recognition module for recognizing the lane center line in front of the vehicle using a lane line recognition technology and/or a high-precision map module for acquiring vehicle positioning information and acquiring the lane center line in front of the vehicle in combination with road information given by a high-precision map by taking the vehicle positioning information as an origin of a vehicle body coordinate system.
In accordance with yet another aspect of the present application, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the aforementioned method when executing the computer program.
In accordance with yet another aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the aforementioned method.
Compared with the prior art, the method, the system and the electronic equipment for judging the trafficability of the automatic driving vehicle curve have the advantages that the lane center line, the running parameters and the vehicle inherent parameters including the maximum steering angle are obtained, the transverse tracking errors of the vehicle and the lane center line are obtained according to the lane center line and the running parameters, the vehicle steering angle is calculated by applying a preset vehicle steering geometric model according to the vehicle inherent parameters, the running parameters and the transverse tracking errors, the vehicle steering angle is compared with the maximum steering angle, the vehicle curve trafficability judging result is obtained, the method involves simple parameters, and the trafficability of the vehicle curve is prejudged by adopting a pre-aiming algorithm based on the longitudinal vehicle speed and the transverse tracking errors of the vehicle; the vehicle steering geometric model, the lane line recognition technology and the high-precision map technology are combined, the trafficability of the vehicle curve is prejudged based on the running track of the vehicle, the problem of intelligent driving of the vehicle curve is solved, and the intellectualization and safety of a driving system are improved; meanwhile, the lane line recognition system is matched with the high-precision map, so that the lane line recognition system can be used for calculating in real time to judge the passage of the curve, and the curve passage judgment result can be calculated in advance according to the high-precision map, so that the lane line recognition system has higher safety and practicability.
Drawings
In order to more clearly illustrate one or more embodiments of the present application or the prior art solutions, the following description will briefly describe the drawings used in the embodiments or the prior art descriptions, and it should be apparent that the drawings in the following description are only one or more embodiments of the present application and that other drawings can be obtained according to these drawings without inventive effort to those skilled in the art.
FIG. 1 is a schematic flow chart of a method for determining the curve passing performance of an automatically driven vehicle according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a system for determining the curve passing performance of an autonomous vehicle according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The present application will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present application should be taken in a general sense as understood by one of ordinary skill in the art to which the present application belongs. The use of the terms "first," "second," and the like in one or more embodiments of the present application does not denote any order, quantity, or importance, but rather the terms "first," "second," and the like are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, the present application provides a curve trafficability determination method of an autonomous vehicle, the method comprising:
s1: the lane center line, the running parameters, and the vehicle-inherent parameters including the maximum steering angle are acquired.
Specifically, the operation parameters include a pre-aiming distance, a longitudinal vehicle speed and control parameters, the intrinsic vehicle parameters include a vehicle wheelbase and a maximum steering angle, the pre-aiming distance can be directly obtained through external input, the longitudinal vehicle speed can be obtained through a sensor, and the vehicle wheelbase is taken as the intrinsic vehicle parameters and is generally measured by combining a steering form and a calibration condition. The maximum steering angle is a vehicle inherent parameter, different vehicles have different maximum steering angles, and the maximum steering angle of a specific vehicle can be obtained through experimental measurement, and is considered to be directly given from the outside in the embodiment.
In this embodiment, acquiring the lane center line includes: identifying a lane centerline in front of the vehicle using lane line identification techniques; or acquiring vehicle positioning information, taking the vehicle positioning information as an origin of a vehicle body coordinate system, and acquiring a lane center line in front of the vehicle by combining road information given by a high-precision map.
S2: and acquiring a transverse tracking error of the vehicle and the lane center line according to the lane center line and the running parameters.
In this embodiment, acquiring the lateral tracking error between the vehicle and the lane center line according to the lane center line and the running parameters includes: determining a pre-aiming point according to the mass center of the vehicle, the pre-aiming distance and the lane center line; specifically, a circle is determined by taking the center of mass of the vehicle as an origin and the pretightening distance as a radius, and the intersection point of the circle and the center line of the lane is determined as a pretightening point. Then, calculating the transverse tracking error of the vehicle and the lane center line according to the vehicle mass center, the pre-aiming point, the pre-aiming distance and the extending line of the front direction of the vehicle; specifically, an included angle between a connecting line between the pre-aiming point and the mass center of the vehicle and an extension line of the front direction of the vehicle is determined, and the pre-aiming distance is multiplied by a sine value of the included angle to obtain a transverse tracking error.
S3: and calculating the steering angle of the vehicle according to the inherent parameters of the vehicle, the running parameters and the transverse tracking error by applying a preset geometric model of the steering of the vehicle.
In this embodiment, the vehicle steering geometric model is a vehicle steering geometric model of a pretightening type:
wherein L is the wheelbase of the vehicle, and the unit is m and v x Is the longitudinal speed of the vehicle, the unit is m/s, e p The unit is m, k is a control parameter, and delta is a steering angle of the vehicle. Based on the vehicle steering geometry model, the vehicle steering angle δ is the steering angle at which the vehicle is expected to respond to a curve given the road environment.
Because the vehicle continuously runs on the road, the pretightening point determined according to the mass center of the vehicle, the pretightening distance and the lane central line dynamically changes, an included angle is formed between the connecting line between the pretightening point and the mass center of the vehicle and the extending line of the front direction of the vehicle, and the transverse tracking error e can be obtained by multiplying the sine value of the included angle and the pretightening distance p 。
S4: and comparing the steering angle of the vehicle with the maximum steering angle, and acquiring a vehicle curve trafficability judging result according to the comparison result.
Specifically, step S4 includes: and comparing the steering angle of the vehicle with the maximum steering angle, outputting a curve passing performance judging result, judging that the vehicle can pass through the curve when the steering angle of the vehicle is smaller than or equal to the maximum steering angle, and judging that the vehicle cannot pass through the curve when the steering angle of the vehicle is larger than the maximum steering angle.
In this embodiment, the method further includes, when the curve passing performance determination result is not passable, switching the automatic driving mode of the vehicle to the human driving mode, or adjusting the longitudinal vehicle speed of the vehicle based on the vehicle steering geometric model until the curve passing performance determination result is passable, and when the longitudinal vehicle speed is adjusted to 0 and the curve passing performance determination result is still not passable, controlling the vehicle to stop or switching the automatic driving mode of the vehicle to the human driving mode, so as to protect the safety of the vehicle passing through the curve. When the passing characteristic of the front curve is judged as being unable to pass, a warning is triggered, and at the moment, the intelligent driving system can be switched from a driving mode along the central line of the road to a driving mode along the outer side of the curve, or a manual taking-over mode passes through the curve.
Based on the same inventive concept, an embodiment of the present application further provides a curve passing performance determining system of an automatic driving vehicle, as shown in fig. 2, including:
an information acquisition module 10 for acquiring a lane center line, running parameters, and vehicle-inherent parameters including a maximum steering angle;
a tracking error calculation module 20 for obtaining the transverse tracking error of the vehicle and the lane center line according to the lane center line and the operation parameters
A steering angle calculation module 30 for calculating a vehicle steering angle by applying a preset vehicle steering geometric model according to the vehicle inherent parameters, the running parameters and the lateral tracking error;
the trafficability determination module 40 is configured to compare the steering angle of the vehicle with the maximum steering angle, and obtain a vehicle curve trafficability determination result according to the comparison result.
In this embodiment, the information acquisition module 10 includes a lane line recognition module for recognizing a lane center line in front of a vehicle using a lane line recognition technique, and a high-precision map module for acquiring vehicle positioning information and acquiring the lane center line in front of the vehicle in combination with road information given by a high-precision map by taking the vehicle positioning information as an origin of a vehicle body coordinate system.
In other embodiments, the vehicle information acquisition module 10 may include only a lane line recognition module or a high-precision map module as long as acquisition of lane center line information in front of the vehicle can be achieved.
Based on the same inventive concept, an embodiment of the present application also provides an electronic device corresponding to the method of any embodiment, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, where the processor executes the program to implement the method for determining curve passability of an autonomous vehicle according to any embodiment.
Fig. 3 shows a more specific hardware schematic of the electronic device provided in this embodiment, where the device may include: processor 100, memory 200, input/output interface 300, communication interface 400, and bus 500. Wherein the processor 100, the memory 200, the input/output interface 300 and the communication interface 400, the bus 500 enable a communication connection between each other within the device.
The processor 100 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided by the embodiments of the present application.
The Memory 200 may be implemented in the form of ROM (Read Only Memory), RAM (RandomAccess Memory ), a static storage device, a dynamic storage device, or the like. Memory 200 may store an operating system and other application programs, and when implementing the techniques provided by embodiments of the present application by software or firmware, the associated program code is stored in memory 200 and invoked for execution by processor 100.
The input/output interface 300 is used for connecting with an input/output module to realize information input and output. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 400 is used to connect with a communication module (not shown in the figure) to enable communication interaction between the present device and other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 500 includes a path for transferring information between components of the device (e.g., processor 100, memory 200, input/output interface 300, and communication interface 400).
It should be noted that although the above-described device only shows the processor 100, the memory 200, the input/output interface 300, the communication interface 400, and the bus 500, the device may include other components necessary for achieving normal operation in the implementation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
Based on the same inventive concept, an embodiment of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for determining curve passability of an autonomous vehicle according to any of the embodiments described above.
The computer-readable storage media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology; the information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computer device.
The computer program stored in the computer storage medium of the above embodiment is configured to cause a computer to perform the curve passing performance determining method of the automatic driving vehicle according to any one of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein.
According to the method, the system and the electronic equipment for judging the trafficability of the curve of the automatic driving vehicle, the vehicle inherent parameters including the lane center line, the running parameters and the maximum steering angle are obtained, the transverse tracking errors of the vehicle and the lane center line are obtained according to the lane center line and the running parameters, the vehicle steering angle is calculated by applying a preset vehicle steering geometric model according to the vehicle inherent parameters, the running parameters and the transverse tracking errors, the vehicle steering angle is compared with the maximum steering angle, the judgment result of the trafficability of the curve of the vehicle is obtained, the method involves simple parameters, and the pre-aiming algorithm is adopted to pre-judge the trafficability of the curve of the vehicle; the vehicle steering geometric model, the lane line recognition technology and the high-precision map technology are combined, the trafficability of the vehicle curve is prejudged based on the running track of the vehicle, the problem of intelligent driving of the vehicle curve is solved, and the intellectualization and safety of a driving system are improved; meanwhile, the lane line recognition system is matched with the high-precision map, so that the lane line recognition system can be used for calculating in real time to judge the passage of the curve, and the curve passage judgment result can be calculated in advance according to the high-precision map, so that the lane line recognition system has higher safety and practicability.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of protection of the application is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the application, the steps may be implemented in any order and there are many other variations of the different aspects of one or more embodiments of the application as described above, which are not provided in detail for the sake of brevity.
One or more embodiments of the present application are intended to embrace all such alternatives, modifications and variations as fall within the broad scope of the present application. Accordingly, any omissions, modifications, equivalents, improvements and others which are within the spirit and principles of the one or more embodiments of the application are intended to be included within the scope of the application.

Claims (10)

1. A method for judging the curve passing performance of an automatic driving vehicle is characterized by comprising the following steps:
acquiring a lane center line, running parameters and vehicle inherent parameters including a maximum steering angle;
acquiring a transverse tracking error of a vehicle and the lane center line according to the lane center line and the running parameters;
calculating a vehicle steering angle by applying a preset vehicle steering geometric model according to the vehicle inherent parameters, the running parameters and the transverse tracking errors;
and comparing the steering angle of the vehicle with the maximum steering angle, and acquiring a vehicle curve trafficability judging result according to a comparison result.
2. The automated driving vehicle curve trafficability determination method according to claim 1, wherein the acquiring the lane center line includes:
identifying the lane centerline in front of a vehicle using lane line identification techniques; or alternatively, the process may be performed,
and acquiring vehicle positioning information, and acquiring the lane center line in front of the vehicle by taking the vehicle positioning information as an origin of a vehicle body coordinate system and combining road information given by a high-precision map.
3. The method for determining the curve trafficability of an automatically driven vehicle according to claim 1, wherein the operation parameters include a pre-aiming distance, and the acquiring the lateral tracking error of the vehicle from the lane center line according to the lane center line and the operation parameters includes:
determining a pretightening point according to the mass center of the vehicle, the pretightening distance and the lane center line;
and calculating the transverse tracking error according to the mass center of the vehicle, the pre-aiming point, the pre-aiming distance and the extending line of the front direction of the vehicle.
4. The method of determining the curve trafficability of an automatically driven vehicle according to claim 3, wherein the determining a pre-aiming point based on a vehicle centroid, the pre-aiming distance, and the lane center line includes:
determining a circle by taking the mass center of the vehicle as an origin and the pretightening distance as a radius;
and determining an intersection point of the circle and the lane center line as the pre-aiming point.
5. The method of determining curve passability of an autonomous vehicle according to claim 3, wherein said calculating said lateral tracking error from said vehicle centroid, said pre-aiming point, said pre-aiming distance, and a vehicle forward direction extension line comprises:
determining an included angle between a connecting line between the pre-aiming point and the mass center of the vehicle and an extension line of the front direction of the vehicle;
multiplying the pretightening distance by the sine value of the included angle to obtain the transverse tracking error.
6. The automated driving vehicle curve trafficability determination method according to any one of claims 1 to 5, wherein the operation parameters include a vehicle longitudinal speed and a control parameter, the vehicle-inherent parameters further include a vehicle wheelbase, and the vehicle steering geometric model is:
wherein L is the wheelbase of the vehicle, and the unit is m and v x For the longitudinal speed of the vehicle, the unit is m/s, e p The unit is m, k is a control parameter, and delta is a vehicle steering angle.
7. The automated driving vehicle curve passability determination method according to any one of claims 1 to 5, wherein the method further comprises:
and when the curve passing performance judging result is not passing, switching the automatic driving mode of the vehicle into a human driving mode, or adjusting the longitudinal speed of the vehicle based on the vehicle steering geometric model until the curve passing performance judging result is passing, and when the longitudinal speed is adjusted to 0 and the curve passing performance judging result is still not passing, controlling the vehicle to stop or switching the automatic driving mode of the vehicle into the human driving mode.
8. A curve trafficability characteristic determination system of an automatically driven vehicle, comprising:
the information acquisition module is used for acquiring a lane center line, running parameters and vehicle inherent parameters including a maximum steering angle;
the tracking error calculation module is used for obtaining the transverse tracking error of the vehicle and the lane center line according to the lane center line and the running parameters
The steering angle calculation module is used for calculating a vehicle steering angle by applying a preset vehicle steering geometric model according to the vehicle inherent parameters, the running parameters and the transverse tracking errors;
and the trafficability judging module is used for comparing the steering angle of the vehicle with the maximum steering angle and acquiring a vehicle curve trafficability judging result according to a comparison result.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 7.
CN202210346292.9A 2022-04-02 2022-04-02 Method and system for judging curve trafficability of automatic driving vehicle and electronic equipment Pending CN116923405A (en)

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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210346292.9A CN116923405A (en) 2022-04-02 2022-04-02 Method and system for judging curve trafficability of automatic driving vehicle and electronic equipment

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