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

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 autonomous vehicle

technical field

The present disclosure relates to the field of computer technology, in particular to the field of automatic driving, and in particular to a method, device, electronic device, computer-readable storage medium and computer program product for measuring braking distance of an automatic driving vehicle.

Background technique

Manual takeover is the last resort to ensure vehicle safety during autonomous driving testing, and plays an extremely important role when autonomous driving technology is not fully mature. For the safety officer in the car, clarifying the braking distance of different takeover methods can take effective brake takeover methods in time before the danger of collision occurs, and avoid unnecessary conservative takeovers under the premise of ensuring safety.

The approaches described in this section are not necessarily approaches that have been previously conceived or employed. 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 issues raised in this section should not be considered to be recognized in any prior art.

SUMMARY OF THE INVENTION

The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium and computer program product for measuring the braking distance of an automatic driving vehicle.

According to an aspect of the present disclosure, there is provided a method for measuring braking distance of an autonomous driving vehicle, comprising: acquiring a plurality of data frames of the autonomous driving vehicle arranged in time series, wherein each data frame of the plurality of data frames includes Driving state data, vehicle speed and vehicle coordinates, the driving state data indicates whether the autonomous driving vehicle is in an autonomous driving state; the driving state data of each data frame in the multiple data frames are detected in turn, until the driving state data in the multiple data frames is determined. The first data frame, 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 the manual takeover state; in response to determining the first data frame, the vehicle speed and The vehicle speed of the subsequent data frames of the first data frame is detected until the second data frame is determined, wherein, at the moment corresponding to the second data frame, the automatic driving vehicle completes braking; and the vehicle coordinates based on the first data frame and The vehicle coordinates of the second data frame determine the braking distance of the autonomous vehicle.

According to another aspect of the present disclosure, there is provided an apparatus for measuring braking distance of an automatic driving vehicle, comprising: a first obtaining unit configured to obtain a plurality of data frames of the automatic driving vehicle arranged in time series, wherein a plurality of data frames are Each data frame in the data frame includes driving state data, vehicle speed and vehicle coordinates, and the driving state data indicates whether the autonomous driving vehicle is in an autonomous driving state; the first detection unit is configured to sequentially detect each of the plurality of data frames The driving state data of the data frame is detected until the first data frame among the multiple data frames is determined, wherein, at the moment corresponding to the first data frame, the driving state of the autonomous driving vehicle is switched from the automatic driving state to the manual takeover state; The second detection unit is configured to, in response to determining the first data frame, sequentially detect the vehicle speed of the first data frame and the vehicle speed of subsequent data frames of the first data frame until the second data frame is determined, wherein the At the moment corresponding to the second data frame, the automatic driving vehicle completes braking; and the first determining unit is configured to determine 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.

According to another aspect of the present disclosure, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by at least one processor A processor executes to enable at least one processor to execute the above-described method for measuring braking distance of an autonomous vehicle.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the above-described method for measuring a braking distance of an autonomous vehicle.

According to another aspect of the present disclosure, there is provided a computer program product including a computer program, wherein the computer program, when executed by a processor, implements the above-described method for measuring braking distance of an autonomous vehicle.

According to one or more embodiments of the present disclosure, it is possible to measure and calculate the braking distance of the autonomous vehicle after the brake takes over, reduce the dependence on manual operation and measurement tools, and improve the efficiency and accuracy of the test.

It should be understood that what is described in this section is not intended to identify key or critical features of embodiments of the disclosure, nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become readily understood from the following description.

Description of drawings

The accompanying drawings illustrate the embodiments by way of example and constitute a part of the specification, and together with the written description of the specification serve to explain exemplary implementations of the embodiments. The shown embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numbers refer to similar but not necessarily identical elements.

1 shows a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to embodiments of the present disclosure;

2 shows a flowchart of a method for measuring braking distance of an autonomous vehicle according to an embodiment of the present disclosure;

FIG. 3 shows a flowchart of a method for measuring braking distance of an autonomous vehicle according to an exemplary embodiment of the present disclosure;

FIG. 4 shows a structural block diagram of an apparatus for measuring braking distance of an autonomous driving vehicle according to an embodiment of the present disclosure;

5 shows a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed ways

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding and should be considered as exemplary only. 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, descriptions of well-known functions and constructions are omitted from the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship or importance relationship of these elements, and such terms are only used for Distinguish one element from another. In some examples, the first element and the 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 dictates otherwise, if the number of an element is not expressly limited, the element may be one or more. Furthermore, as used in this disclosure, the term "and/or" covers any and all possible combinations of the listed items.

The takeover methods for autonomous vehicles are generally divided into brake takeovers, accelerator takeovers, steering wheel takeovers, and corresponding combined takeover methods. Among them, the brake takeover is used most frequently in most takeover scenarios, ensuring the vehicle to stop in an emergency and avoiding the danger of collision. Brake takeover can be divided into ordinary brake takeover, slow brake button takeover, and emergency stop button takeover according to specific application scenarios. Common brake takeover is taken over by brake pedal, and the braking force is variable. Slow brake button takeover and emergency stop button takeover can be taken over by pressing Get off the fixed position button in the car to trigger the brake, and the braking force is relatively fixed.

At present, in the related art, the method for measuring the braking distance after the automatic driving vehicle takes over includes: selecting a long straight road to place the automatic driving vehicle, and setting a clear marker farther in front of the vehicle; modifying the maximum speed limited by the automatic driving to be Target speed; no other obstacles interfered during the test, to ensure that the vehicle can accelerate to the maximum speed in a straight line; start the automatic driving vehicle, after the automatic driving vehicle reaches the set maximum speed, judge by the naked eye that the front of the vehicle is flush with the above signs, select the corresponding After the automatic driving vehicle stops completely, measure the longitudinal distance between the front of the vehicle and the rear marker; repeat the measurement several times to obtain the average value.

Applying the above method to measure the braking distance after the automatic driving vehicle takes over, because the relative position of the vehicle and the reference object is manually observed to perform the takeover action, the measurement error cannot be guaranteed; moreover, the measurement needs to be performed outside the vehicle many times during the measurement. Measurement, while time-consuming, relies more on manual operation and measurement tools, which inevitably introduces more measurement errors and affects the accuracy of measurement.

The embodiment of the present disclosure provides a method for measuring the braking distance of an autonomous vehicle, by collecting multiple consecutive data frames during the test, and judging the data frame corresponding to the occurrence of brake takeover based on the above data frames; Subsequent data frames are used for speed detection until it is detected that the vehicle is stationary; then the braking distance is calculated based on the relevant data frames, so that the braking distance of the autonomous vehicle after the braking takes over can be measured, reducing the need for manual operation and measurement. Reliance on tools improves the efficiency and accuracy of testing.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

1 shows a schematic diagram of an exemplary system 100 in which the various methods and apparatuses 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 that couple the motor vehicle 110 to the server 120 .

In an embodiment of the present disclosure, the motor vehicle 110 may include and/or be configured to perform a method according to an embodiment 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 braking takeover occurs. In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized 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 executable by one or more processors, hardware components, or a combination thereof. A user of motor vehicle 110 may in turn utilize one or more client applications to interact with server 120 to utilize 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.

Server 120 may include one or more general purpose computers, special purpose server computers (eg, PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination . Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures that involve virtualization (eg, may be virtualized to maintain one or more flexible pools of logical storage devices of the server's virtual storage devices). In various embodiments, 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. 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 implementations, server 120 may include one or more applications to analyze and incorporate 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, and the like. By way of example only, the one or more networks 110 may be a satellite communications 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 , extranet, blockchain network, public switched telephone network (PSTN), infrared network, wireless network (including, for example, Bluetooth, WiFi) and/or any combination of these and other networks.

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. Data repository 150 may reside in various locations. For example, the data repository used by server 120 may be local to server 120, or may be remote from server 120 and may communicate with server 120 via a network-based or dedicated connection. Data repository 150 may be of different types. In some embodiments, the data repository used by 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 databases in response to commands.

In some embodiments, one or more of the databases 150 may also be used by applications to store application data. Databases used by applications can be different types of databases such as key-value stores, object stores, or regular stores backed by a file system.

Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. Sensors 111 may include one or more of the following sensors: vision cameras, infrared cameras, ultrasonic sensors, millimeter-wave radar, and laser radar (LiDAR). Different sensors can provide different detection accuracy and range. Cameras can be installed in front of, behind, or elsewhere in the vehicle. Vision cameras can capture what's inside and outside the vehicle in real time and present it to the driver and/or passengers. In addition, by analyzing the footage captured by the visual camera, information such as traffic light indications, intersection conditions, and other vehicle operating states can be obtained. Infrared cameras can capture objects with night vision. Ultrasonic sensors can be installed around the vehicle to measure the distance of objects outside the vehicle from the vehicle by using the characteristics of the strong directionality of ultrasonic waves. Millimeter-wave radar can be installed in the front, rear or other positions of the vehicle to measure the distance of objects outside the vehicle from the vehicle using the characteristics of electromagnetic waves. Lidar can be installed in the front, rear or other positions of the vehicle to detect object edges and shape information for object recognition and tracking. Due to the Doppler effect, the radar unit can also measure changes in the speed of vehicles and moving objects.

The motor vehicle 110 may also include a communication device 112 . Communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (eg, BeiDou, GPS, GLONASS, and GALILEO) from satellites 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with the mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as GSM/GPRS, CDMA, LTE and other current or developing wireless communication technologies (eg, 5G technology) . The communication device 112 may also have a vehicle-to-vehicle (Vehicle-to-Everything, V2X) module configured to implement, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and with infrastructure The facility 144 performs vehicle-to-infrastructure (V2I) communication with the outside world. In addition, 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), eg, via wireless local area network or Bluetooth using the IEEE 802.11 standard. Using the communication device 112 , the motor vehicle 110 may also access the server 120 via the network 130 .

The motor vehicle 110 may also include a control device 113 . Control device 113 may include a processor in communication with various types of computer-readable storage devices or media, such as a central processing unit (CPU) or graphics processing unit (GPU), or other special purpose processors, or the like. The control device 113 may include an automated driving system for automatically controlling various actuators in the vehicle. The automated driving system is configured to control the motor vehicle 110 (not shown) powertrain, steering system, and braking system, etc., via a plurality of actuators in response to inputs from a plurality of sensors 111 or other input devices to control acceleration, respectively , steering and braking without or with limited human intervention. Part of the processing functions of the control device 113 can be realized by cloud computing. For example, some processing may be performed using an on-board processor, while other processing may be performed using computing resources in the cloud. The control device 113 may be configured to perform the method according to the present disclosure. Furthermore, the control device 113 may be implemented as one example of a computing device on the motor vehicle side (client side) 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 apparatuses described in accordance with the present disclosure.

According to an embodiment of the present disclosure, as shown in FIG. 2 , a method for measuring the braking distance of an automatic driving vehicle is provided, including: step S201 , acquiring multiple data frames of the automatic driving vehicle arranged in time series, wherein multiple Each data frame in the data frame includes driving state data, vehicle speed and vehicle coordinates, and the driving state data indicates whether the autonomous driving vehicle is in an autonomous driving state; step S202 , sequentially evaluating the driving state of each data frame in the multiple data frames The data is detected until the 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 the manual takeover state; step S203, in response to determining In the first data frame, the vehicle speed of the first data frame and the vehicle speed of the subsequent data frames of the first data frame are sequentially detected, until the second data frame is determined, wherein, at the moment corresponding to the second data frame, the automatic driving The vehicle completes braking; 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.

In this way, it is possible to measure the braking distance of the autonomous vehicle after the brake takes over, reduce the dependence on manual operation and measurement tools, and improve the efficiency and accuracy of the test.

In some embodiments, vehicle data during the test of the autonomous vehicle may be collected to obtain multiple data frames, wherein the data included in each data frame is the data collected at the same time.

In some embodiments, the relevant data of the vehicle may be collected at a fixed frequency, for example, data collection may be performed at a frequency of collecting data every 0.01s.

In some embodiments, each data frame may include driving state data, vehicle speed, and vehicle coordinates. Among them, the driving status data is used to indicate whether the autonomous vehicle is in the autonomous driving status, which generally includes three statuses: the autonomous driving status, the manual takeover status, and the manual driving status. An intermediate state of the switch, which usually occurs when the vehicle driver triggers a takeover. The vehicle speed can be acquired in real time, for example, by the vehicle's wheel speed sensors. The vehicle coordinates can be acquired in real time through, for example, a global satellite navigation system (eg, GPS system, Beidou satellite navigation system, etc.).

In some embodiments, each data frame may be collected during the braking distance test after the automatic driving vehicle takes over the brakes, and stored accordingly, and after the test, the above-mentioned multiple data frames may be collected. Upload the database and store it for subsequent calculation and measurement of braking distance.

In some embodiments, the detection of the driving state data may be performed sequentially on the above-mentioned multiple data frames in chronological order. Usually in a set of normal test data, the driving state data in the first multiple data frames are all in the automatic driving state. When the tester observes that the vehicle reaches the target speed, the brake will be triggered to take over, and the corresponding data The frame's driving state data changes from the autopilot state to the manual takeover state. Therefore, by sequentially detecting the driving state data for each data frame, until the first data frame whose driving state data is the manual takeover state is detected, the data frame is the first data frame, that is, in the data frame At the moment corresponding to the frame, the driving state of the autonomous vehicle is switched from the automatic driving state to the manual takeover state.

Through the detection of the driving state data, some abnormal situations can be further excluded (for example, due to obstacles in the field during the test, the vehicle brakes in the automatic driving state, etc.), thereby further improving the accuracy of the measurement data.

In some embodiments, after the first data frame is detected, the vehicle speed may be sequentially detected for each subsequent data frame, and it is determined whether the vehicle has completed braking by judging whether the vehicle speed is less than a speed threshold.

Usually, due to the signal fluctuation of the vehicle speed, in order to further improve the accuracy of the judgment and eliminate the error caused by the speed signal fluctuation, it is possible to judge whether the speed of multiple vehicles is less than Speed threshold to determine whether the vehicle has finished braking. When the speed of multiple vehicles is less than the speed threshold within the preset time, it means that the vehicle has completed braking, and the detected data frame is the second data frame.

In some embodiments, based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame, the braking distance of the vehicle after the brake takeover can be obtained by directly calculating the distance between the two coordinates.

In some embodiments, the braking distance of the autonomous driving vehicle is determined based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame, which may also be based on the first data frame, the second data frame, and the above two data frames. One or more data frames between them respectively obtain their corresponding vehicle coordinates, and calculate the coordinate distance of adjacent data frames in the above data frames, and then accumulate them to obtain the braking distance of the vehicle after the brake takes over. As a result, 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 process, each data frame in the plurality of data frames further includes a data collection time, and the autonomous vehicle braking distance The measurement method may further include: obtaining a start time of each brake test process in the at least one brake test process; and for each brake test process in the at least one brake test process, based on the brake test process The starting time and the data collection time of each data frame in the multiple data frames are determined, and the starting data frame corresponding to the braking test process is determined, so as to start the detection of driving state data based on the starting data frame.

In some embodiments, the plurality of data frames may be all data frames collected during the process of the corresponding autonomous vehicle from power-on to power-off of the vehicle. Therefore, the above-mentioned multiple data frames may include data of one or more braking test processes, and may also include data frames of other test processes.

In some embodiments, the starting time of each braking test process can be recorded, and based on the starting time and the data collection time in each data frame, the starting data frame of the testing process can be found, and the starting data frame of the testing process can be found from the starting time. Starting from the initial data frame, driving state detection and subsequent measurement processes are performed.

Therefore, by recording the starting time of each test, determining the starting data of each test process in the data, and calculating the distance separately, the batch processing of multiple test data is realized, data transmission resources are saved, and the measurement is improved. efficiency.

According to some embodiments, based on the start time of the brake test process and the data collection time of each data frame in the plurality of data frames, determining the start data frame corresponding to the brake test process may include: based on the brake test process The starting time of the test process is to determine a third data frame, and the data collection time of the third data frame corresponds to the start time of the braking test process; and based on the preset backtracking duration and the data collection time of the third data frame, determine The starting data frame corresponding to the braking test process.

In some embodiments, the third data frame corresponding to the time may be found first based on the start time of the braking test process and the data collection time in each data frame, and based on the preset backtracking duration (for example, it may be 10s), From the collection time corresponding to the third data frame, the preset backtracking duration is backtracked forward, and the data frame corresponding to the backtracking time is used as the starting data frame, and starting from the starting data frame, the driving state detection and subsequent measurement process.

As a result, possible manual errors can be further eliminated (for example, the tester records the time after triggering the takeover), reducing the dependence on manual labor, ensuring the integrity of the data, and improving the measurement accuracy.

According to some embodiments, the method for measuring the braking distance of the automatic driving vehicle may further include: in response to the vehicle speed of the first data frame being less than the target speed, acquiring the data of the next braking test process of the braking test process corresponding to the first data frame Start time to start the braking distance measurement for the next braking test session.

Since the trigger of manual takeover is manually judged by the tester during each test process, the vehicle speed may deviate from the target speed when triggering the takeover. Therefore, in order to further ensure the validity of the test data and the accuracy of the measurement , the vehicle speed of the first data frame can be detected first. 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. Therefore, the data of the test process If it is invalid data, the data of this test process can be no longer processed, and the braking distance measurement of the next braking test process can be started directly.

According to some embodiments, the method for measuring the braking distance of an automatic driving vehicle may further include: in response to determining the first data frame, determining whether the vehicle speed 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 speed, sequentially judging whether the vehicle speed of at least one subsequent data frame of the first data frame is greater than the target speed, until the 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 fourth data frame The vehicle speed of the previous data frame is greater than the target speed; and based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame, determining the braking distance of the autonomous vehicle may include: based on the vehicle coordinates of the fourth data frame and The vehicle coordinates of the second data frame determine the braking distance of the autonomous vehicle.

In some embodiments, when the vehicle speed of the first data frame is greater than the target speed, the vehicle speed of the subsequent data frames may be sequentially detected, so as to guide the detection of the data frame of the vehicle speed less than or equal to the target speed, that is, the fourth data frame. Data Frame. Then, the braking distance can be calculated by the above method based on the vehicle coordinates of the fourth data frame and the second data frame. In this way, the deviation of the current vehicle speed that may be caused by the tester when the takeover is triggered can be further eliminated, thereby improving the accuracy of the measurement.

In some embodiments, during the vehicle braking test process, different brake takeover modes are generally tested for different target speeds for multiple times. The average value can be calculated for the braking distances obtained during multiple test processes under the same test conditions, so as to output the final result of the vehicle braking distance of the autonomous vehicle under the test conditions after the brakes take over.

In some exemplary embodiments, as shown in FIG. 3, a method for measuring the braking distance of an automatic driving vehicle is provided, and the specific steps include: step S301, performing data initialization, setting the braking distance s, the number of takeovers cnt, the cumulative Delay and other three parameters, and initialize the three parameters to zero respectively; step S302, based on the start time of the braking test process and the preset backtracking duration (for example, 10s), determine the start data frame; step S303, to Detect the driving state data of the current data frame to determine whether the driving state is the manual takeover state; Step S304, in response to the current data frame's driving state not being the manual takeover state, continue to detect the driving state data of the next data frame; Step S305 2. In response to the driving state of the current data frame being the manual takeover state, perform cnt=cnt+1 on the number of takeovers; step S306, by judging whether the number of takeovers cnt is equal to 1, determine whether it is the first takeover in the braking test process, with Determine that the current data frame is the first data frame; Step S307, in response to determining the first data frame, determine whether the vehicle speed of the current data frame is greater than or equal to the target speed; Step S308, in response to the current data frame The vehicle speed is less than the target speed, Obtain the starting time of the next braking test process to start the braking distance measurement of the next braking test process; Step S309, in response to the vehicle speed of the current data frame being greater than or equal to the target speed, determine the vehicle speed of the current data frame Whether it is less than or equal to the target speed; Step S310, in response to the vehicle speed of the current data frame being greater than the target speed, detect the vehicle speed of the next data frame; Step S311, in response to the current data frame The vehicle speed is less than or equal to the target speed, Determine that the current data frame is the fourth data frame, and obtain the next data frame; Step S312, calculate the distance _Δs between the two coordinates based on the vehicle coordinates of the current data frame and the previous data frame, and calculate the braking distance s= s+ _Δs ; Step S313, determine whether the vehicle speed of the current data frame is less than or equal to the speed threshold (for example, it may be 0.1m/s); Step S314, in response to the vehicle speed of the current data frame being greater than the speed threshold, set the cumulative delay to t is 0, and return to step S311; step S315, in response to the vehicle speed of the current data frame being less than or equal to the speed threshold, based on the data collection time of the current data frame and the previous data frame, calculate the time difference _Δt of the two data frames, and Calculate the cumulative delay t=t+ _Δt ; step S316, determine whether the cumulative delay is greater than or equal to the preset time (for example, 1s), and in response to the cumulative delay being less than the preset time, return to step S311; and step S317, in response to The accumulated delay is greater than or equal to the preset time, and the braking distance s at this time is output.

According to some embodiments, as shown in FIG. 4 , an apparatus 400 for measuring the braking distance of an autonomous driving vehicle is provided, including: a first acquiring unit 410 configured to acquire a plurality of data frames of the autonomous driving vehicle arranged in time series , wherein each of the multiple data frames includes driving state data, vehicle speed and vehicle coordinates, and the driving state data indicates whether the autonomous driving vehicle is in an autonomous driving state; the first detection unit 420 is configured to sequentially detect multiple The driving state data of each data frame in the data frame is detected until the first data frame in the multiple 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 the manual takeover state; the second detection unit 430 is configured to, in response to determining the first data frame, sequentially detect the vehicle speed of the first data frame and the vehicle speed of subsequent data frames of the first data frame, until the first data frame is determined. Two data frames, wherein, at the moment corresponding to the second data frame, the automatic driving vehicle completes braking; and the first determination unit 440 is configured to determine based on the vehicle coordinates of the first data frame and the vehicle coordinates of the second data frame Braking distance for autonomous vehicles.

The operations of the units 410 to 440 in the apparatus 400 for measuring the braking distance of an autonomous vehicle are similar to the operations of steps S201 to S204 of the above method for measuring the braking distance of an autonomous vehicle, and will not be repeated here.

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 data frame of the plurality of data frames further includes a data collection time, and the autonomous vehicle brakes The distance measuring device may further include: a second acquisition unit configured to acquire a start time of each brake test process in the at least one brake test process; and a second determination unit configured for the at least one brake test process For each brake test process in the test process, based on the start time of the brake test process and the data collection time of each data frame in the multiple data frames, determine the start data frame corresponding to the brake test process, The detection of driving state data starts with the starting data frame.

According to some embodiments, the second determination unit may include: a first determination subunit configured to determine a third data frame based on the start time of the braking test process, the data collection time of the third data frame being the same as the braking time The starting time of the test process corresponds to; and the second determining subunit is configured to determine the starting data frame corresponding to the braking test process based on the preset backtracking duration and the data collection time of the third data frame.

According to some embodiments, the apparatus for measuring the braking distance of an autonomous vehicle may further include: a first judgment unit configured to, in response to determining the first data frame, judge whether the vehicle speed of the first data frame is greater than the target speed; a second judgment unit The unit is configured to, in response to the vehicle speed of the first data frame being greater than the target speed, sequentially determine whether the vehicle speed of at least one subsequent data frame of the first data frame is greater than the target speed, until the fourth data frame is determined, wherein the fourth data frame is The vehicle speed of the data frame is less than or equal to the target speed, and the vehicle speed of the previous data frame of the fourth data frame is greater than the target speed; and the first determining unit may be further configured to: based on the vehicle coordinates of the fourth data frame and the second The vehicle coordinates of the data frame to determine the braking distance of the autonomous vehicle.

According to some embodiments, the apparatus for measuring the braking distance of an autonomous vehicle may further include: a third obtaining unit, configured to obtain a braking test process corresponding to the first data frame in response to the vehicle speed of the first data frame being less than the target speed The starting time of the next braking test process to start the braking distance measurement of the next braking test process.

According to embodiments of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

Referring to FIG. 5 , a structural block diagram of an electronic device 500 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are by way of example 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 that can be programmed according to a computer program stored in a read only memory (ROM) 502 or loaded into a random access memory (RAM) 503 from a storage unit 508 . Various appropriate actions and processes are performed. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501 , the ROM 502 , and the RAM 503 are connected to each other through a bus 504 . An input/output (I/O) interface 505 is also connected to bus 504 .

Various 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, the input unit 506 may receive input numerical or character information, and generate key signal input related to user settings and/or function control of the electronic device, and This may include, but is not limited to, a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone and/or remote control. The output unit 507 may be any type of device capable of presenting information, and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. The storage unit 508 may include, but is not limited to, magnetic disks and optical disks. The communication unit 509 allows the electronic device 500 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chips Groups such as Bluetooth™ devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices and/or the like.

Computing unit 501 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of computing units 501 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above, such as the above-described method for measuring the braking distance of an autonomous vehicle. For example, in some embodiments, the above-described method of measuring braking distance of an autonomous vehicle may be implemented as a computer software program tangibly embodied on 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 on the electronic device 500 via the ROM 502 and/or the communication unit 509 . When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the above-described method for measuring the braking distance of an autonomous driving vehicle described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured in any other suitable manner (eg, by means of firmware) to perform the above-described method of measuring the braking distance of an autonomous vehicle.

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an 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 code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

A computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a distributed system server, or a server combined with blockchain.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which are not limited herein.

Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-described methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is limited only by the appended claims and their equivalents. Various elements of the embodiments or examples may be omitted or replaced by equivalents thereof. Furthermore, the steps may be performed in an order different from that described in this disclosure. Further, various elements of the embodiments or examples may be combined in various ways. Importantly, as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear later in this 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.

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