CN115144201A - Method, device, equipment and medium for measuring braking distance of automatic driving vehicle - Google Patents

Method, device, equipment and medium for measuring braking distance of automatic driving vehicle Download PDF

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CN115144201A
CN115144201A CN202210744449.3A CN202210744449A CN115144201A CN 115144201 A CN115144201 A CN 115144201A CN 202210744449 A CN202210744449 A CN 202210744449A CN 115144201 A CN115144201 A CN 115144201A
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data frame
data
vehicle
test process
determining
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王涵
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Apollo Zhilian Beijing Technology Co Ltd
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Apollo Zhilian Beijing Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles

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Abstract

The disclosure provides a method, a device, equipment and a medium for measuring a braking distance of an automatic driving vehicle, and relates to the technical field of computers, in particular to the field of automatic driving. The implementation scheme is as follows: acquiring a plurality of data frames of an autonomous vehicle arranged in time sequence; sequentially detecting the driving state data of each data frame in the plurality of data frames until a first data frame in the plurality of data frames is determined; in response to determining the first data frame, sequentially detecting a vehicle speed of the first data frame and a vehicle speed of a subsequent data frame of the first data frame until determining a second data frame; and determining a braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.

Description

Method, device, equipment and medium for measuring braking distance of automatic driving vehicle
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to the field of autonomous driving, and more particularly, to a method and apparatus for measuring a braking distance of an autonomous vehicle, an electronic device, a computer-readable storage medium, and a computer program product.
Background
Manual takeover is the last means for ensuring the safety of the vehicle during the automatic driving test, and plays an extremely important role when the automatic driving technology is not completely mature. For safety personnel in the automobile, it is clear that the braking distances of different take-over modes can adopt an effective braking take-over mode in time before collision danger occurs, and unnecessary conservative take-over is avoided on the premise of ensuring safety.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.
Disclosure of Invention
The present disclosure provides a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for measuring a braking distance of an autonomous vehicle.
According to an aspect of the present disclosure, there is provided a method of measuring a braking distance of an autonomous vehicle, including: obtaining a plurality of data frames of an autonomous vehicle arranged in a time sequence, wherein each data frame of the plurality of data frames includes driving state data, a vehicle speed, and vehicle coordinates, the driving state data indicating whether the autonomous vehicle is in an autonomous state; sequentially detecting the driving state data of each data frame in the plurality of data frames until a first data frame in the plurality of data frames is determined, wherein at the moment corresponding to the first data frame, the driving state of the automatic driving vehicle is switched from an automatic driving state to a manual takeover state; responding to the first data frame, sequentially detecting the vehicle speed of the first data frame and the vehicle speed of the subsequent data frame of the first data frame until a second data frame is determined, wherein the automatic driving vehicle finishes braking at the moment corresponding to the second data frame; and determining a braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.
According to another aspect of the present disclosure, there is provided an apparatus for measuring a braking distance of an autonomous vehicle, including: a first acquisition unit configured to acquire a plurality of data frames of an autonomous vehicle arranged in time series, wherein each of the plurality of data frames includes driving state data indicating whether the autonomous vehicle is in an autonomous state, a vehicle speed, and vehicle coordinates; the first detection unit is configured to detect the driving state data of each data frame in the plurality of data frames in sequence until a first data frame in the plurality of data frames is determined, wherein the driving state of the automatic driving vehicle is switched from the automatic driving state to the manual takeover state at the moment corresponding to the first data frame; a second detection unit configured to detect, in response to determining the first data frame, a vehicle speed of the first data frame and a vehicle speed of a subsequent data frame of the first data frame in sequence until determining a second data frame, wherein at a time corresponding to the second data frame, the autonomous vehicle completes braking; and a first determination unit configured to determine a braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one a processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of measuring braking distance of an autonomous vehicle as described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to execute the above-described method of measuring a braking distance of an autonomous vehicle.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the above-described method of measuring a braking distance of an autonomous vehicle.
According to one or more embodiments of the disclosure, the measurement and calculation of the braking distance of the automatic driving vehicle after the brake connection pipe can be realized, and the dependence on manual operation and measuring tools is reduced, so that the testing efficiency and accuracy are improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.
FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;
FIG. 2 shows a flow chart of a method of measuring braking distance of an autonomous vehicle according to an embodiment of the disclosure;
FIG. 3 shows a flow chart of a method of measuring braking distance of an autonomous vehicle according to an exemplary embodiment of the present disclosure;
FIG. 4 shows a block diagram of a device for measuring braking distance of an autonomous vehicle according to an embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, for the sake of clarity and conciseness, descriptions of well-known functions and constructions are omitted in the following description.
In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to define a positional relationship, a temporal relationship, or an importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, while in some cases they may refer to different instances based on the context of the description.
The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or a plurality of. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.
The take-over modes for automatically driving vehicles are generally divided into brake take-over, accelerator take-over, steering wheel take-over and corresponding combined take-over modes. The brake takeover is used at the highest frequency in most takeover scenes, so that emergency stop of the vehicle is guaranteed, and collision danger is avoided. The brake connecting pipe can be divided into a common brake connecting pipe, a slow brake button connecting pipe and an emergency stop button connecting pipe according to a specific application scene, the common brake connecting pipe is a brake pedal connecting pipe, the brake force is variable, the slow brake button connecting pipe and the emergency stop button connecting pipe trigger the brake by pressing a button at a fixed position in the vehicle, and the brake force is relatively fixed.
Currently, in the related art, a method for measuring a braking distance after an autonomous vehicle takes over includes: selecting a long straight road to place an automatic driving vehicle, and setting an obvious marker at a far position in front of the vehicle; modifying the maximum speed limited by automatic driving as a target speed; no other obstacles interfere in the test period, so that the vehicle can be accelerated to the highest speed in a straight line; starting the automatic driving vehicle, judging that the vehicle head is level to the mark through naked eyes after the automatic driving vehicle reaches the set maximum speed, and selecting a corresponding brake pipe connecting mode to execute pipe connection; when the automatic driving vehicle stops completely, measuring the longitudinal distance between the vehicle head and the rear marker at the moment; the measurement was repeated several times to find the average value.
By applying the method, the braking distance after the automatic driving vehicle takes over is measured, and because the relative position of the vehicle and a reference object is observed manually to execute the taking over action, the measurement error cannot be ensured; and, need measure outside the car many times during the measurement, when consuming more time, comparatively rely on manual operation and measuring tool, more measuring error has been introduced to inevitable, influence the measuring degree of accuracy.
The embodiment of the disclosure provides a method for measuring the braking distance of an automatic driving vehicle, which comprises the steps of collecting a plurality of continuous data frames in the testing process, and judging the corresponding data frame when a brake takeover occurs based on the data frames; subsequently, carrying out speed detection on the subsequent data frames until the vehicle is detected to be still; and then, the braking distance is calculated based on the related data frame, so that the braking distance of the automatic driving vehicle after the braking take-over can be measured and calculated, the dependence on manual operation and measuring tools is reduced, and the testing efficiency and accuracy are improved.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 coupling the motor vehicle 110 to the server 120.
In embodiments of the present disclosure, motor vehicle 110 may include a computing device and/or be configured to perform a method in accordance with embodiments of the present disclosure.
The server 120 may run one or more services or software applications that enable a method of measuring the braking distance of an autonomous vehicle after a brake take-over occurs. In some embodiments, the server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user of motor vehicle 110 may, in turn, utilize one or more client applications to interact with server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.
The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.
The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.
In some embodiments, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of motor vehicle 110.
Network 130 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a satellite communication network, a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a blockchain network, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (including, for example, bluetooth, wiFi), and/or any combination of these and other networks.
The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. The data store 150 may reside in various locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 150 may be of different types. In certain embodiments, the data store used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.
In some embodiments, one or more of the databases 150 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or conventional stores supported by a file system.
Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. The sensors 111 may include one or more of the following sensors: a vision camera, an infrared camera, an ultrasonic sensor, a millimeter wave radar, and a laser radar (LiDAR). Different sensors may provide different detection accuracies and ranges. The camera may be mounted in front of, behind, or otherwise on the vehicle. The visual camera may capture conditions inside and outside the vehicle in real time and present to the driver and/or passengers. In addition, by analyzing the picture captured by the visual camera, information such as traffic light indication, intersection situation, other vehicle running state, and the like can be acquired. The infrared camera can capture objects under night vision conditions. The ultrasonic sensors can be arranged around the vehicle and used for measuring the distance between an object outside the vehicle and the vehicle by utilizing the characteristics of strong ultrasonic directionality and the like. The millimeter wave radar may be installed in front of, behind, or other positions of the vehicle for measuring the distance of an object outside the vehicle from the vehicle using the characteristics of electromagnetic waves. The lidar may be mounted in front of, behind, or otherwise of the vehicle for detecting object edges, shape information, and thus object identification and tracking. The radar apparatus can also measure the velocity change of the vehicle and the moving object due to the doppler effect.
Motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., beidou, GPS, GLONASS, and GALILEO) from the satellites 141 and generating coordinates based on these signals. The communication device 112 may also include modules to communicate with a mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as current or evolving wireless communication technologies (e.g., 5G technologies) like GSM/GPRS, CDMA, LTE, etc. The communication device 112 may also have a Vehicle-to-Vehicle (V2X) module configured to enable Vehicle-to-Vehicle (V2V) communication with other vehicles 143 and Vehicle-to-Infrastructure (V2I) communication with Infrastructure 144, for example. Further, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smartphone, tablet, or wearable device such as a watch), for example, via wireless local area network using IEEE802.11 standards or bluetooth. Motor vehicle 110 may also access server 120 via network 130 using communication device 112.
Motor vehicle 110 may also include a control device 113. The control device 113 may include a processor, such as a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), or other special purpose processor, etc., in communication with various types of computer-readable storage devices or media. The control device 113 may include an autopilot system for automatically controlling various actuators in the vehicle. The autopilot system is configured to control a powertrain (not shown), a steering system, and a braking system, etc., of a motor vehicle 110 (not shown) via a plurality of actuators in response to inputs from a plurality of sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without or with limited human intervention. Part of the processing functions of the control device 113 may be realized by cloud computing. For example, some processing may be performed using an onboard processor while other processing may be performed using the computing resources in the cloud. The control device 113 may be configured to perform a method according to the present disclosure. Furthermore, the control apparatus 113 may be implemented as one example of a computing device on the motor vehicle side (client) according to the present disclosure.
The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.
According to an embodiment of the present disclosure, as shown in fig. 2, there is provided a method for measuring a braking distance of an autonomous vehicle, including: step S201, a plurality of data frames of the automatic driving vehicle arranged according to time sequence are obtained, wherein each data frame in the plurality of data frames comprises driving state data, vehicle speed and vehicle coordinates, and the driving state data indicates whether the automatic driving vehicle is in an automatic driving state or not; step S202, sequentially detecting the driving state data of each data frame in the plurality of data frames until a first data frame in the plurality of data frames is determined, wherein at the moment corresponding to the first data frame, the driving state of the automatic driving vehicle is switched from the automatic driving state to a manual takeover state; step S203, responding to the first data frame, sequentially detecting the vehicle speed of the first data frame and the vehicle speed of the subsequent data frame of the first data frame until a second data frame is determined, wherein the automatically driven vehicle finishes braking at the moment corresponding to the second data frame; and step S204, determining the braking distance of the automatic driving vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.
From this, can realize taking over the measurement and calculation of back braking distance at the braking to the self-driving vehicle, reduce the reliance to manual operation and measuring tool to the efficiency and the degree of accuracy of test have been promoted.
In some embodiments, vehicle data during an autonomous vehicle test may be collected to obtain a plurality of data frames, where each data frame includes data collected at the same time.
In some embodiments, the data related to the vehicle may be collected at a fixed frequency, for example, the data may be collected at a frequency of collecting data every 0.01 s.
In some embodiments, each data frame may include driving status data, vehicle speed, and vehicle coordinates. The driving state data is used for indicating whether the automatic driving vehicle is in an automatic driving state, and generally comprises three states of an automatic driving state, a manual take-over state, a manual driving state and the like, wherein the manual take-over state is an intermediate state for switching from the automatic driving state to the manual driving state, and generally occurs when a vehicle driver triggers take-over. The vehicle speed can be detected in real time, for example, by a wheel speed sensor of the vehicle. The vehicle coordinates can be acquired in real time by, for example, a global satellite navigation system (e.g., GPS system, beidou satellite navigation system, etc.).
In some embodiments, each data frame may be collected and stored correspondingly in a braking distance testing process after the automatic driving vehicle takes over the brake, and after the test is finished, the data frames may be uploaded to a database and stored for subsequent calculation and measurement of the braking distance.
In some embodiments, the detection of the driving state data may be performed sequentially in time order for the plurality of data frames. Usually, in a set of normal test data, the driving state data in the first multiple data frames are all in an automatic driving state, when a tester observes that the vehicle reaches a target speed, a brake takeover is triggered, and the driving state data of the corresponding data frame can be changed from the automatic driving state to a manual takeover state. Therefore, the driving state data are detected in sequence for each data frame until the first driving state data is detected to be the data frame in the manual takeover state, the data frame is the first data frame, namely, the driving state of the automatic driving vehicle is switched from the automatic driving state to the manual takeover state at the moment corresponding to the data frame.
Through the detection of the driving state data, some abnormal conditions (for example, the vehicle is braked in an automatic driving state due to the fact that obstacles appear in the field in the test process) can be further eliminated, and therefore the accuracy of the measured data is further improved.
In some embodiments, after the first data frame is detected, the vehicle speed may be detected for each subsequent data frame in turn, and the vehicle may be determined to have completed braking by determining whether the vehicle speed is less than a speed threshold.
Generally, since the vehicle speed has signal fluctuation, in order to further improve the accuracy of the determination and eliminate the error caused by the speed signal fluctuation, it is possible to determine whether the vehicle has completed braking by determining whether a plurality of vehicle speeds are all smaller than a speed threshold within a preset time (e.g., 1 s). And when the preset time is met and the speeds of a plurality of vehicles are all smaller than the speed threshold, the vehicles are indicated to be braked, and the detected data frame is the second data frame.
In some embodiments, the braking distance of the vehicle after taking over the brake can be directly obtained by calculating the distance between the two coordinates based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.
In some embodiments, the braking distance of the autonomous vehicle is determined based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame, or the braking distance of the autonomous vehicle after taking over the brake may be obtained by respectively obtaining the corresponding vehicle coordinates of the first data frame, the second data frame and one or more data frames between the two data frames, calculating the coordinate distances of adjacent data frames in the data frames, and accumulating the coordinate distances. Thereby, the accuracy of the braking distance measurement can be further improved.
According to some embodiments, the plurality of data frames includes at least one data frame group respectively corresponding to at least one braking test procedure, each data frame of the plurality of data frames further includes a data acquisition time, and the method of measuring a braking distance of the autonomous vehicle may further include: acquiring the starting time of each brake test process in at least one brake test process; and for each brake test process in at least one brake test process, determining a starting data frame corresponding to the brake test process based on the starting time of the brake test process and the data acquisition time of each data frame in the plurality of data frames, so as to start to detect the driving state data based on the starting data frame.
In some embodiments, the plurality of data frames may be all data frames collected for the autonomous vehicle from vehicle power-on to vehicle power-off. Thus, the plurality of data frames may include data for one or more brake test procedures, as well as data frames for other test procedures.
In some embodiments, the start time of each brake test process may be recorded, and based on the start time and the data acquisition time in each data frame, the start data frame of the test process may be found, and from the start data frame, the driving state detection and the subsequent measurement process may be performed.
Therefore, the initial data of each test process in the data is determined by recording the initial time of each test, and the distance calculation is respectively carried out, so that the batch processing of the test data for many times is realized, the data transmission resource is saved, and the measurement efficiency is improved.
According to some embodiments, determining the corresponding start data frame of the brake test procedure based on the start time of the brake test procedure and the data acquisition time of each of the plurality of data frames may include: determining a third data frame based on the starting time of the brake test process, wherein the data acquisition time of the third data frame corresponds to the starting time of the brake test process; and determining an initial data frame corresponding to the brake test process based on the preset backtracking duration and the data acquisition time of the third data frame.
In some embodiments, a third data frame corresponding to the time may be first found based on the start time of the brake test process and the data acquisition time in each data frame, and based on a preset trace-back duration (for example, 10 s), the preset trace-back duration is traced back forward from the acquisition time corresponding to the third data frame, and the data frame corresponding to the trace-back duration is used as the start data frame, and the driving state detection and the subsequent measurement process are performed from the start data frame.
Therefore, possible manual errors can be further eliminated (for example, the conditions that the time is recorded after the test personnel takes over the trigger, and the like), the dependence on manual work is reduced, the integrity of data is ensured, and the measurement accuracy is improved.
According to some embodiments, the method of measuring a braking distance of an autonomous vehicle may further comprise: and responding to the fact that the vehicle speed of the first data frame is smaller than the target speed, and obtaining the starting time of the next brake test process of the brake test process corresponding to the first data frame so as to start the brake distance measurement of the next brake test process.
In each test process, the triggering of manual takeover is manually judged by a tester, so that the vehicle speed may deviate from the target speed when the takeover is triggered, and therefore, in order to further ensure the validity of test data and the accuracy of measurement, the vehicle speed of a first data frame can be detected at first, and when the vehicle speed of the first data frame is less than the target speed, the vehicle speed in the subsequent vehicle braking process cannot reach the target speed, so that the data of the test process is invalid data, the data of the test process can not be processed, and the braking distance measurement of the next braking test process is directly started.
According to some embodiments, the method of measuring a braking distance of an autonomous vehicle may further comprise: in response to determining the first data frame, determining whether a vehicle speed of the first data frame is greater than a target speed; responding to the fact that the vehicle speed of the first data frame is larger than the target speed, sequentially judging whether the vehicle speed of at least one subsequent data frame of the first data frame is larger than the target speed or not until a fourth data frame is determined, wherein the vehicle speed of the fourth data frame is smaller than or equal to the target speed, and the vehicle speed of a previous data frame of the fourth data frame is larger than the target speed; and determining the braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame may comprise: determining a braking distance of the autonomous vehicle based on the vehicle coordinates of the fourth data frame and the vehicle coordinates of the second data frame.
In some embodiments, when the vehicle speed of the first data frame is greater than the target speed, the vehicle speeds of the subsequent data frames may be sequentially detected, and the data frame whose vehicle speed is less than or equal to the target speed, that is, the fourth data frame, may be guided to be detected. The braking distance can then be calculated by the method described above based on the vehicle coordinates of the fourth data frame and the second data frame. Therefore, the deviation of the current vehicle speed possibly brought by a tester when the test personnel triggers the take-over can be further eliminated, and the measurement accuracy is improved.
In some embodiments, during the vehicle braking test, different brake take-over modes are generally tested for different target speeds. The average value of the braking distances obtained in multiple testing processes under the same testing condition can be calculated, so that the final result of the braking distance of the automatic driving vehicle after the brake is taken over under the testing condition is output.
In some exemplary embodiments, a method for measuring a braking distance of an autonomous vehicle is provided as shown in fig. 3, comprising the following specific steps: step S301, carrying out data initialization, respectively setting three parameters such as braking distance S, pipe connection times cnt and accumulated time delay, and respectively initializing the three parameters to be zero; step S302, determining a starting data frame based on the starting time and the preset backtracking duration (for example, 10S) of the brake test process; step S303, detecting the driving state data of the current data frame, and judging whether the driving state is a manual takeover state; step S304, responding to the fact that the driving state of the current data frame is not a manual takeover state, and continuously detecting the driving state data of the next data frame; step S305, responding to the driving state of the current data frame being a manual takeover state, and dockingTube count cnt = cnt +1; step S306, judging whether the taking-over time cnt is equal to 1 or not, and judging whether the taking-over time cnt is the first taking-over time in the brake test process so as to determine that the current data frame is the first data frame; step S307, responding to the first data frame, judging whether the vehicle speed of the current data frame is greater than or equal to the target speed; step S308, responding to the fact that the vehicle speed of the current data frame is smaller than the target speed, obtaining the starting time of the next braking test process, and starting the braking distance measurement of the next braking test process; step S309, responding to the vehicle speed of the current data frame being greater than or equal to the target speed, judging whether the vehicle speed of the current data frame is less than or equal to the target speed; step S310, responding to the fact that the vehicle speed of the current data frame is larger than the target speed, and detecting the vehicle speed of the next data frame; step S311, responding to the situation that the vehicle speed of the current data frame is less than or equal to the target speed, determining the current data frame as a fourth data frame, and acquiring the next data frame; step S312, based on the vehicle coordinates of the current data frame and the previous data frame, calculating the distance between the two coordinates △ s and calculating the braking distance s = s + △ s; step 313, judging whether the vehicle speed of the current data frame is less than or equal to a speed threshold (for example, 0.1 m/S); step S314, in response to the fact that the vehicle speed of the current data frame is larger than the speed threshold value, setting the accumulated delay t to be 0, and returning to the step S311; step S315, responding to the vehicle speed of the current data frame being less than or equal to the speed threshold value, calculating the time difference of the two data frames based on the data acquisition time of the current data frame and the data acquisition time of the previous data frame △ t and calculating the cumulative delay t = t + △ t; step S316, determining whether the accumulated time delay is greater than or equal to a preset time (e.g., 1S), and returning to step S311 in response to the accumulated time delay being less than the preset time; and step S317, responding to the condition that the accumulated time delay is larger than or equal to the preset time, and outputting the brake distance S at the moment.
According to some embodiments, as shown in fig. 4, there is provided an autonomous vehicle braking distance measuring device 400, comprising: a first acquisition unit 410 configured to acquire a plurality of data frames of the autonomous vehicle arranged in time series, wherein each of the plurality of data frames includes driving state data indicating whether the autonomous vehicle is in an autonomous state, a vehicle speed, and vehicle coordinates; a first detecting unit 420 configured to sequentially detect the driving state data of each of the plurality of data frames until a first data frame of the plurality of data frames is determined, wherein the driving state of the autonomous vehicle is switched from the autonomous driving state to a manual take-over state at a time corresponding to the first data frame; a second detecting unit 430 configured to detect, in response to determining the first data frame, a vehicle speed of the first data frame and a vehicle speed of a subsequent data frame of the first data frame in sequence until determining a second data frame, wherein at a time corresponding to the second data frame, the autonomous vehicle completes braking; and a first determination unit 440 configured to determine a braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.
The operations of the units 410 to 440 of the device 400 for measuring the braking distance of the autonomous vehicle are similar to the operations of the steps S201 to S204 of the method for measuring the braking distance of the autonomous vehicle, and are not described herein again.
According to some embodiments, the plurality of data frames may include at least one data frame group respectively corresponding to at least one braking test procedure, each of the plurality of data frames further includes a data acquisition time, and the measuring device of the braking distance of the autonomous vehicle may further include: a second acquisition unit configured to acquire a start time of each of the at least one brake test procedure; and a second determination unit configured to determine, for each of the at least one brake test procedure, a start data frame corresponding to the brake test procedure based on the start time of the brake test procedure and the data acquisition time of each of the plurality of data frames to start detection of the driving state data based on the start data frame.
According to some embodiments, the second determining unit may include: a first determining subunit configured to determine a third data frame based on the start time of the brake test process, a data acquisition time of the third data frame corresponding to the start time of the brake test process; and the second determining subunit is configured to determine a starting data frame corresponding to the brake test process based on the preset backtracking duration and the data acquisition time of the third data frame.
According to some embodiments, the apparatus for measuring a braking distance of an autonomous vehicle may further include: a first determination unit configured to determine whether a vehicle speed of the first data frame is greater than a target speed in response to determining the first data frame; a second determination unit configured to sequentially determine, in response to the vehicle speed of the first data frame being greater than the target speed, whether the vehicle speed of at least one subsequent data frame of the first data frame is greater than the target speed until a fourth data frame is determined, wherein the vehicle speed of the fourth data frame is less than or equal to the target speed and the vehicle speed of a previous data frame of the fourth data frame is greater than the target speed; and the first determination unit may be further configured to: and determining the braking distance of the automatic driving vehicle based on the vehicle coordinates of the fourth data frame and the vehicle coordinates of the second data frame.
According to some embodiments, the apparatus for measuring a braking distance of an autonomous vehicle may further include: and the third acquisition unit is configured to respond to the vehicle speed of the first data frame being less than the target speed, and acquire the starting time of the next braking test process of the braking test process corresponding to the first data frame so as to start the braking distance measurement of the next braking test process.
According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.
Referring to fig. 5, a block diagram of a structure of an electronic device 500, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, there is a memory, various programs and data required for the operation of the electronic device 500 may also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the electronic device 500 are connected to the I/O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the electronic device 500, and the input unit 506 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote controller. Output unit 507 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 508 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
The computing unit 501 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 executes the respective methods and processes described above, such as the above-described measurement method of the braking distance of the autonomous vehicle. For example, in some embodiments, the above-described method of measuring the braking distance of an autonomous vehicle may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the above described method of measuring a braking distance of an autonomous vehicle may be performed. Alternatively, in other embodiments, the calculation unit 501 may be configured to perform the above-described method of measuring the braking distance of the autonomous vehicle in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. Digital data may be communicated via any form or medium (e.g., a communications network) to interconnect the components of the system. Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the present disclosure.

Claims (13)

1. A method of measuring a braking distance of an autonomous vehicle, the method comprising:
obtaining a plurality of time-sequenced data frames of the autonomous vehicle, wherein each data frame of the plurality of data frames comprises driving status data, vehicle speed, and vehicle coordinates, the driving status data indicating whether the autonomous vehicle is in an autonomous state;
sequentially detecting the driving state data of each data frame in the plurality of data frames until a first data frame in the plurality of data frames is determined, wherein at the moment corresponding to the first data frame, the driving state of the automatic driving vehicle is switched from an automatic driving state to a manual takeover state;
in response to determining the first data frame, sequentially detecting the vehicle speed of the first data frame and the vehicle speed of a subsequent data frame of the first data frame until a second data frame is determined, wherein the autonomous vehicle completes braking at a time corresponding to the second data frame; and
determining a braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.
2. The method of claim 1, wherein the plurality of data frames includes at least one set of data frames respectively corresponding to at least one braking test procedure, each data frame of the plurality of data frames further including a data acquisition time, and the method further comprises:
acquiring the starting time of each brake test process in the at least one brake test process; and
and for each brake test process in the at least one brake test process, determining a starting data frame corresponding to the brake test process based on the starting time of the brake test process and the data acquisition time of each data frame in the plurality of data frames, so as to start to detect the driving state data based on the starting data frame.
3. The method of claim 2, wherein determining the starting data frame for the brake test procedure based on the starting time of the brake test procedure and the data acquisition time of each of the plurality of data frames comprises:
determining a third data frame based on the starting time of the brake test process, wherein the data acquisition time of the third data frame corresponds to the starting time of the brake test process; and
and determining an initial data frame corresponding to the brake test process based on a preset backtracking duration and the data acquisition time of the third data frame.
4. The method of claim 2 or 3, further comprising:
in response to determining the first data frame, determining whether a vehicle speed of the first data frame is greater than a target speed;
in response to the vehicle speed of the first data frame being greater than the target speed, sequentially determining whether the vehicle speed of at least one subsequent data frame of the first data frame is greater than the target speed until a fourth data frame is determined, wherein the vehicle speed of the fourth data frame is less than or equal to the target speed and the vehicle speed of a previous data frame of the fourth data frame is greater than the target speed; and is
The determining a braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame comprises:
determining a braking distance of the autonomous vehicle based on the vehicle coordinates of the fourth data frame and the vehicle coordinates of the second data frame.
5. The method of claim 4, further comprising:
and responding to the fact that the vehicle speed of the first data frame is smaller than the target speed, and obtaining the starting time of the next brake test process of the brake test process corresponding to the first data frame so as to start the brake distance measurement of the next brake test process.
6. An autonomous vehicle stopping distance measuring device, the device comprising:
a first acquisition unit configured to acquire a plurality of data frames of the autonomous vehicle arranged in time series, wherein each of the plurality of data frames includes driving state data indicating whether the autonomous vehicle is in an autonomous state, a vehicle speed, and a vehicle coordinate;
a first detection unit configured to detect driving state data of each of the plurality of data frames in sequence until a first data frame of the plurality of data frames is determined, wherein at a time corresponding to the first data frame, a driving state of the autonomous vehicle is switched from an autonomous driving state to a manual takeover state;
a second detection unit configured to detect, in response to determining the first data frame, a vehicle speed of the first data frame and a vehicle speed of a subsequent data frame of the first data frame in sequence until determining a second data frame, wherein the autonomous vehicle completes braking at a time corresponding to the second data frame; and
a first determination unit configured to determine a braking distance of the autonomous vehicle based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame.
7. The apparatus of claim 6, wherein the plurality of data frames includes at least one set of data frames respectively corresponding to at least one braking test procedure, each data frame of the plurality of data frames further including a data acquisition time, and the apparatus further comprises:
a second obtaining unit configured to obtain a start time of each of the at least one brake test procedure; and
and the second determining unit is configured to determine, for each brake test process in the at least one brake test process, a starting data frame corresponding to the brake test process based on the starting time of the brake test process and the data acquisition time of each data frame in the plurality of data frames, so as to start detection of the driving state data based on the starting data frame.
8. The apparatus of claim 7, wherein the second determining unit comprises:
a first determining subunit configured to determine a third data frame based on the start time of the brake test process, a data acquisition time of the third data frame corresponding to the start time of the brake test process; and
and the second determining subunit is configured to determine a starting data frame corresponding to the brake test process based on a preset backtracking time length and the data acquisition time of the third data frame.
9. The apparatus of claim 7 or 8, further comprising:
a first determination unit configured to determine whether a vehicle speed of the first data frame is greater than a target speed in response to determining the first data frame;
a second determination unit configured to sequentially determine whether a vehicle speed of at least one subsequent data frame of the first data frame is greater than the target speed in response to the vehicle speed of the first data frame being greater than the target speed until a fourth data frame is determined, wherein the vehicle speed of the fourth data frame is less than or equal to the target speed and the vehicle speed of a previous data frame of the fourth data frame is greater than the target speed; and is
The first determination unit is further configured to:
determining a braking distance of the autonomous vehicle based on the vehicle coordinates of the fourth data frame and the vehicle coordinates of the second data frame.
10. The apparatus of claim 9, further comprising:
and the third acquisition unit is configured to respond to the fact that the vehicle speed of the first data frame is smaller than the target speed, and acquire the starting time of the next brake test process of the brake test process corresponding to the first data frame so as to start the brake distance measurement of the next brake test process.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-5 when executed by a processor.
CN202210744449.3A 2022-06-27 2022-06-27 Method, device, equipment and medium for measuring braking distance of automatic driving vehicle Pending CN115144201A (en)

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CN202210744449.3A CN115144201A (en) 2022-06-27 2022-06-27 Method, device, equipment and medium for measuring braking distance of automatic driving vehicle

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