Traffic light timing data compression method, device and autonomous vehicle

Abstract

The present disclosure provides a traffic light timing data compression method, device and autonomous vehicle, relates to the technical field of data processing, specifically to the technical field of autonomous driving, intelligent transportation and cloud computing, and a specific implementation scheme is as follows: original timing data of a traffic light is acquired, and feature extraction is performed on the original timing data to obtain feature information of the traffic light; feature information with a jump is identified from the original timing data, and original timing data corresponding to the feature information with the jump is extracted to obtain target timing data; the target timing data and target feature information corresponding to the target timing data are compressed to obtain compressed timing data of the traffic light. The present disclosure compresses the target timing data and the target feature information corresponding to the target timing data, which can compress the data to the greatest extent, saves storage space and improves data access speed, and the compressed timing data can be inversely converted into original timing data.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to the fields of autonomous driving, intelligent transportation and cloud computing technology, and particularly to a method, apparatus and autonomous vehicle for compressing time-series data of traffic lights. Background Technology

[0002] In related technologies, traffic light time-series data is compressed using frame-sampling compression. However, this method results in irreversible changes in the time-series data, ineffective compression of highly repetitive data, and potential loss of data for frequently changing original data. Therefore, improving the efficiency of time-series data compression, reducing storage space, and increasing access speed are urgent problems to be solved. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, storage medium, program product, and autonomous vehicle for compressing timing data of traffic lights.

[0004] According to a first aspect, a method for compressing time-series data of traffic lights is provided, comprising: acquiring original time-series data of traffic lights and extracting features from the original time-series data to obtain feature information of the traffic lights; filtering the original time-series data according to the feature information of the traffic lights to obtain target time-series data of the traffic lights; and compressing the target time-series data and the target feature information corresponding to the target time-series data to obtain compressed time-series data of the traffic lights.

[0005] According to a second aspect, a compression device for traffic light timing data is provided, comprising: an acquisition module for acquiring original timing data of a traffic light and performing feature extraction on the original timing data to obtain feature information of the traffic light; a tracking module for tracking the extracted feature information to identify whether the feature information has changed; an extraction module for identifying the feature information that has changed from the original timing data and extracting the original timing data corresponding to the feature information that has changed to obtain target timing data; and a compression module for compressing the target timing data and the target feature information corresponding to the target timing data to obtain compressed timing data of the traffic light.

[0006] According to a third aspect, an electronic device is provided, 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 the at least one processor to enable the at least one processor to perform the method for compressing timing data of traffic lights as described in the first aspect of this disclosure.

[0007] According to a fourth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute a method for compressing timing data of traffic lights according to a first aspect of this disclosure.

[0008] According to a fifth aspect, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method for compressing timing data of traffic lights according to a first aspect of this disclosure.

[0009] According to the sixth aspect, an autonomous vehicle is provided, including: electronic equipment as described in the third aspect.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0012] Figure 1 This is a flowchart illustrating a method for compressing timing data of traffic lights according to a first embodiment of this disclosure;

[0013] Figure 2 This is a flowchart illustrating a method for compressing timing data of traffic lights according to a second embodiment of the present disclosure;

[0014] Figure 3 This is a flowchart illustrating a method for compressing timing data of traffic lights according to a third embodiment of this disclosure;

[0015] Figure 4 This is a flowchart illustrating the fourth embodiment of the present disclosure;

[0016] Figure 5 This is a schematic diagram of compressed traffic light timing data based on this disclosure;

[0017] Figure 6 This is a block diagram of a device for compressing timing data of traffic lights to implement embodiments of the present disclosure;

[0018] Figure 7 This is a block diagram of an electronic device used to implement the method for compressing timing data of traffic lights according to embodiments of the present disclosure. Detailed Implementation

[0019] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0020] Data processing is a fundamental component of systems engineering and automatic control. It permeates all areas of social production and life. The development of data processing technology and the breadth and depth of its applications have profoundly influenced the progress of human society. Data is a form of expression of facts, concepts, or instructions, which can be processed manually or by automated devices. After being interpreted and given meaning, data becomes information. Data processing involves the collection, storage, retrieval, processing, transformation, and transmission of data. The fundamental purpose of data processing is to extract and derive valuable and meaningful data from large amounts of potentially chaotic and difficult-to-understand data.

[0021] Automatic operation (AOP) systems employ advanced communication, computer, network, and control technologies to achieve real-time, continuous control of trains. Utilizing modern communication methods and directly interacting with the train, AOP systems enable two-way data communication between the train and the ground, achieving high transmission rates and large data volumes. This allows subsequent train tracking and the control center to promptly obtain the precise location of the preceding train, resulting in more flexible operation management, more effective control, and better adaptation to the demands of automatic train operation.

[0022] Intelligent Transportation Systems (ITS), also known as Intelligent Transportation Systems, effectively integrate advanced technologies (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) into transportation, service control, and vehicle manufacturing. This strengthens the connection between vehicles, roads, and users, thereby forming a comprehensive transportation system that ensures safety, improves efficiency, enhances the environment, and conserves energy.

[0023] Cloud computing refers to a technology system that provides access to a shared pool of physical or virtual resources via a network, which can be elastically scalable. These resources may include servers, operating systems, networks, software, applications, and storage devices, and can be deployed and managed on demand and in a self-service manner. Cloud computing technology provides efficient and powerful data processing capabilities for applications such as artificial intelligence and blockchain, as well as for model training. Located within the core network of data centers, cloud computing collects data from terminals through layers of network devices and leverages its powerful storage and computing capabilities for big data analysis.

[0024] The following describes a method for compressing timing data of traffic lights according to an embodiment of the present disclosure, with reference to the accompanying drawings.

[0025] Figure 1 This is a flowchart illustrating a method for compressing timing data of traffic lights according to a first embodiment of the present disclosure.

[0026] like Figure 1 As shown, the method for compressing the timing data of traffic lights according to this embodiment of the present disclosure may specifically include the following steps:

[0027] S101, acquire the original time-series data of the traffic lights, and extract features from the original time-series data to obtain the feature information of the traffic lights.

[0028] Specifically, the execution entity of the traffic light timing data compression method in this embodiment of the present disclosure can be the traffic light timing data compression device provided in this embodiment of the present disclosure. This traffic light timing data compression device can be a hardware device with data information processing capabilities and / or the necessary software to drive the hardware device. Optionally, the execution entity may include a workstation, server, computer, user terminal, and other devices. The user terminal includes, but is not limited to, mobile phones, computers, intelligent voice interaction devices, smart home appliances, and vehicle terminals.

[0029] Time series data refers to data collected over time. Time series data is a series of data recorded in chronological order according to the same unified indicator. All data in the same series must be of the same caliber and be comparable.

[0030] Optionally, the original timing data of traffic lights can be red light timing data, green light timing data, and yellow light timing data, etc.

[0031] It should be noted that this disclosure does not limit the specific method for obtaining the raw time-series data of traffic lights, and the appropriate method can be selected according to the actual situation.

[0032] Optionally, the system can receive compressed packages of traffic light timing data reported by vehicles and decompress the packages to obtain the original timing data of the traffic lights.

[0033] In this embodiment of the disclosure, after obtaining the original time-series data of the traffic lights, feature extraction can be performed on the original time-series data to obtain the feature information of the traffic lights.

[0034] It should be noted that this disclosure does not limit the characteristic information of traffic lights.

[0035] Optionally, the characteristic information of the traffic light can be the color information of the traffic light, the location information of the traffic light, the duration information of any color of traffic light, and the distance information from the traffic light to the vehicle.

[0036] S102, track the extracted feature information to identify whether the feature information changes abruptly.

[0037] It should be noted that the raw time-series data of traffic lights is often massive. Simply storing all the raw time-series data would occupy a large amount of storage space, resulting in high storage costs. Therefore, in order to reduce the storage cost of the raw time-series data of traffic lights, the raw time-series data can be filtered according to the feature information of the traffic lights. That is, the extracted feature information is tracked to identify whether the feature information has changed, and the target time-series data of the traffic lights can be obtained based on the change results.

[0038] S103, identify the feature information of the jump from the original time series data, and extract the original time series data corresponding to the feature information of the jump to obtain the target time series data.

[0039] For example, if the color information of a traffic light changes within a preset time range, the original time series data of the change can be used as the target time series data of the traffic light; if the position information of a traffic light changes within a preset time range, the original time series data of the change can be used as the target time series data of the traffic light.

[0040] S104, compress the target time series data and the target feature information corresponding to the target time series data to obtain the compressed time series data of the traffic light.

[0041] Data compression refers to a technical method that reduces the amount of data to reduce storage space and improve its transmission, storage and processing efficiency without losing useful information, or reorganizes data according to certain algorithms to reduce data redundancy and storage space. Data compression includes lossy compression and lossless compression.

[0042] It should be noted that after obtaining the target time series data, the target feature information corresponding to the target time series data can be obtained, and the target time series data and the target feature information corresponding to the target time series data can be compressed to obtain the compressed time series data of the traffic lights.

[0043] Furthermore, after obtaining the compressed time-series data of the traffic lights, the compressed time-series data of the traffic lights can be restored based on the timestamps to obtain the target time-series data and the feature information corresponding to the target time-series data.

[0044] In summary, the traffic light time-series data compression method of this disclosure involves acquiring the original time-series data of the traffic lights, extracting features from the original time-series data to obtain the feature information of the traffic lights, tracking the extracted feature information to identify whether the feature information has abruptly changed, identifying the feature information that has abruptly changed from the original time-series data, extracting the original time-series data corresponding to the feature information that has abruptly changed to obtain target time-series data, and compressing the target time-series data and the target feature information corresponding to the target time-series data to obtain compressed time-series data of the traffic lights. This disclosure compresses the target time-series data and the target feature information corresponding to the target time-series data, thereby maximizing the compression of time-series data, saving storage space and improving the access speed of time-series data, and after obtaining the compressed time-series data, it can be reverse-engineered back into the original time-series data.

[0045] Figure 2 This is a flowchart illustrating a method for compressing timing data of traffic lights according to a second embodiment of the present disclosure.

[0046] like Figure 2 As shown, in Figure 1 Based on the illustrated embodiment, the method for compressing the timing data of traffic lights according to this disclosure may specifically include the following steps:

[0047] S201, Obtain the original time-series data of the traffic lights and extract features from the original time-series data to obtain the feature information of the traffic lights.

[0048] As one possible implementation, such as Figure 3 As shown, based on the above embodiment, the specific process of obtaining the original timing data of traffic lights in step S201 includes the following steps:

[0049] S301 receives compressed packets of traffic light timing data reported by vehicles.

[0050] Optionally, during vehicle operation, the vehicle-mounted camera can acquire the original timing data of the traffic lights and store the original timing data of the traffic lights recognized by the vehicle in a database.

[0051] It should be noted that after storing the original timing data of the traffic lights in the database in advance, it can receive compressed packages of traffic timing data reported by vehicles.

[0052] S302, decompress the compressed package to obtain the original timing data of the traffic lights.

[0053] In this embodiment of the disclosure, after obtaining the time-series data compressed package, the compressed package can be decompressed to obtain the original time-series data of the traffic lights.

[0054] In this embodiment of the disclosure, after obtaining the original time-series data of the traffic lights, feature extraction can be performed on the original time-series data to obtain the feature information of the traffic lights.

[0055] S202, track the extracted feature information to identify whether the feature information changes abruptly.

[0056] S203, identify the feature information of the jump from the original time series data, and extract the original time series data corresponding to the feature information of the jump to obtain the target time series data.

[0057] Specifically, steps S202-S203 in this embodiment are the same as steps S102-S103 in the above embodiment, and will not be repeated here.

[0058] The step S104 in the above embodiment, "compressing the target time series data and the target feature information corresponding to the target time series data to obtain compressed time series data", may specifically include the following steps S204 and S205.

[0059] S204, perform feature aggregation on the target feature information to obtain the aggregated feature information of the target time series data.

[0060] It should be noted that after obtaining the target feature information, feature aggregation can be performed on the target feature information to reflect the changes in the target time series data and obtain the aggregated feature information of the target time series data.

[0061] For example, when the target feature information obtained is the color information of a traffic light, feature aggregation can be performed on the target feature information to obtain aggregated feature information such as the number of transitions between red and yellow lights and the number of times a red light appears; when the target feature information obtained is the distance information from the traffic light to the vehicle, feature aggregation can be performed on the target feature information to obtain aggregated feature information such as the minimum distance from the traffic light to the vehicle and the maximum distance from the traffic light to the vehicle.

[0062] S205, compress the target time series data and aggregated feature information to obtain compressed time series data.

[0063] The step S205 in the above embodiment, "compressing the target time series data and aggregated feature information to obtain compressed time series data", may specifically include the following steps S401 and S403.

[0064] S401, Obtain the first business requirement for the target time series data.

[0065] It should be noted that this disclosure does not limit the first business requirement, but can be limited according to the actual situation.

[0066] Optionally, the first business requirement can be to obtain the duration of the yellow light; alternatively, the first business requirement can be to obtain the number of times the traffic light changes.

[0067] S402, Obtain the aggregated feature information of the target time series data corresponding to the first business requirement.

[0068] Optionally, after obtaining the first business requirement for the target time series data, target feature information corresponding to the target time series data can be obtained based on the first business requirement, and then the target feature information can be aggregated to obtain aggregated feature information of the target time series data corresponding to the first business requirement.

[0069] S403 compresses the target time series data and aggregated feature information to obtain compressed time series data.

[0070] In this embodiment of the disclosure, after obtaining compressed time series data, a timestamp associated with the target time series data can be obtained. Based on the timestamp, the compressed time series data can be restored to obtain the target time series data and aggregated feature information associated with the timestamp.

[0071] In summary, the traffic light time-series data compression method of this disclosure involves acquiring the original time-series data of the traffic lights, extracting features from the original time-series data to obtain the feature information of the traffic lights, tracking the extracted feature information to identify whether the feature information has changed, identifying the feature information that has changed from the original time-series data, extracting the original time-series data corresponding to the feature information that has changed to obtain target time-series data, aggregating the target feature information to obtain aggregated feature information of the target time-series data, and compressing the target time-series data and aggregated feature information to obtain compressed time-series data. This disclosure can acquire target time-series data by tracking the feature information of the original data, and can also compress the target time-series data and aggregated feature information according to business needs to obtain compressed time-series data. This maximizes the compression of time-series data, saves storage space, improves the access speed of time-series data, and allows for the reverse conversion of the obtained compressed time-series data back to the original time-series data.

[0072] The process of the traffic light timing data compression method proposed in this disclosure will be explained below.

[0073] For example, such as Figure 5As shown, the vehicle has multiple cameras (CAMERA1, CAMERA2, CAMERA3, CAMERA4). Optionally, the target camera can be selected based on the clarity of the images captured by the cameras. For example, CAMERA1 and CAMERA3 are selected at time t1, CAMERA1 and CAMERA3 at time t2, and so on, with CAMERA1 and CAMERA4 selected at time tn. By comparing the selected cameras, the time-series data collected by CAMERA1, CAMERA2, CAMERA3, CAMERA4, and CAMERA4 can be used as end-to-end time-series data. Among these, the end-to-end time-series data has a more direct impact on the vehicle's driving. Therefore, the CAMERA1 and CAMERA3 selected at time t1, the CAMERA1 and CAMERA3 selected at time t2, and so on, with CAMERA1 and CAMERA4 selected at time tn, can be used as end-to-end time-series data. 4. The collected time-series data serves as the model's result time-series data. Further, it can receive compressed packages of traffic light time-series data reported by vehicles, decompress the packages to obtain the raw traffic light time-series data, and acquire the target feature information and aggregated feature values ​​(label data and rule data). It can also associate label data and rule data based on timestamps (keys), such as: task information, vehicle identification ID, and time information. Label data can include location information, distance from the vehicle to the stop line (distance1), distance from the vehicle to the traffic light (distance2), road intersection, occlusion information, etc. Rule data can include color-end data, color data (color-model), focus tag data (focus tag), traffic light information, camera information, and time information. For example, for transition scenarios where the duration is less than 1 second, the traffic light time-series data can be compressed.

[0074] In this embodiment of the disclosure, after obtaining compressed time-series data, a second service requirement for the compressed time-series data can be obtained. Based on the required characteristics of the second service requirement, features are extracted from the compressed time-series data related to the second service requirement to obtain the result corresponding to the second service request.

[0075] The following explains the business requirements of the traffic light time-series data compression method proposed in this disclosure.

[0076] Optionally, for yellow light duration statistics, traffic lights with the characteristic of yellow are selected based on compressed time-series data to achieve duration statistics for each yellow light; for traffic light change index statistics: based on the instability of traffic light recognition results, which manifests as jumps in the data, the number of jumps is calculated by extracting the data and calculating the number of times in unstable scenarios to obtain the traffic light change index; for data mining of black light missed or false detection issues, black lights with the characteristic of nighttime are selected to mine data on missed or false detection issues of black lights at night; for traffic light data visualization: compressed data is reversed to restore time-series data; for traffic light mining range extraction: by aggregating feature information, the corresponding time information range is extracted from the data range required by the business.

[0077] For example, in the scenario of mining traffic light time-series data, a date range can be preset, such as 2022.09.11-2022.09.18; a CD image version can be set; a road network, i.e., the vehicle's driving area, can be set; a corresponding scenario, such as a daytime scenario or a nighttime scenario, can be set; vehicle identification information and equipment parameters, radar parameter information, and the distance from the main vehicle to the stop line can be set, such as setting the distance from the main vehicle to the stop line to within 10-50m, etc. By setting the above parameters, traffic light time-series data can be mined to obtain the number of transitions, the number of underwater cases (potential risk issues on the vehicle side), the number of times the vehicle passes through the intersection, the number of underwater key cases (potential key risk issues on the vehicle side), and the number of vehicle recalls for takeover, etc.

[0078] In summary, the traffic light time-series data compression method of this disclosure can obtain target time-series data by tracking the feature information of the original data. At the same time, it can compress the target time-series data and aggregated feature information in combination with business requirements to obtain compressed time-series data. This can maximize the compression of time-series data, save storage space and improve the access speed of time-series data. After obtaining the compressed time-series data, it can be reversed to be converted into the original time-series data, which is beneficial for mining time-series data.

[0079] It should be noted that the acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0080] Figure 6 This is a schematic diagram of a device for compressing timing data of traffic lights according to an embodiment of the present disclosure.

[0081] like Figure 6 As shown, the traffic light timing data compression device 600 includes: an acquisition module 610, a tracking module 620, an extraction module 630, and a compression module 640. Wherein:

[0082] The acquisition module 610 is used to acquire the original time-series data of the traffic lights and perform feature extraction on the original time-series data to obtain the feature information of the traffic lights;

[0083] The tracking module 620 is used to track the extracted feature information in order to identify whether the feature information has changed abruptly.

[0084] The extraction module 630 is used to identify the feature information of the jump from the original time series data, and extract the original time series data corresponding to the feature information of the jump to obtain the target time series data.

[0085] Compression module 640 is used to compress the target time series data and the target feature information corresponding to the target time series data to obtain the compressed time series data of the traffic light.

[0086] The compression module 640 is also used for:

[0087] The target feature information is aggregated to obtain the aggregated feature information of the target time series data;

[0088] The target time series data and the aggregated feature information are compressed to obtain the compressed time series data.

[0089] The compression module 640 is also used for:

[0090] Obtain the first business requirement for the target time-series data;

[0091] Obtain aggregated feature information of the target time series data corresponding to the first business requirement; compress the target time series data and the aggregated feature information to obtain the compressed time series data.

[0092] The device 600 is also used for:

[0093] Obtain the timestamp associated with the target time series data;

[0094] Based on the timestamp, the compressed time-series data is restored to obtain the target time-series data associated with the timestamp and the aggregated feature information.

[0095] The device 600 is also used for:

[0096] Obtain the second service requirement for the compressed time-series data;

[0097] Based on the required characteristics of the second business requirement, feature extraction is performed on the compressed time-series data related to the second business requirement to obtain the result corresponding to the second business request.

[0098] The acquisition module 610 is also used for:

[0099] Receive the timing data compressed package of the traffic lights reported by the vehicle;

[0100] The compressed package is decompressed to obtain the original timing data of the traffic lights.

[0101] It should be noted that the above explanation of the method embodiment for compressing traffic light timing data also applies to the traffic light timing data compression device of this disclosure embodiment, and the specific process will not be repeated here.

[0102] In summary, the traffic light timing data compression device of this disclosure acquires the original timing data of the traffic lights, extracts features from the original timing data to obtain the feature information of the traffic lights, filters the original timing data based on the feature information of the traffic lights to obtain target timing data of the traffic lights, and compresses the target timing data and the target feature information corresponding to the target timing data to obtain compressed timing data of the traffic lights. This disclosure compresses the target timing data and the target feature information corresponding to the target timing data, thereby maximizing the compression of timing data, saving storage space and improving the access speed of timing data, and can reversely convert the obtained compressed timing data back into the original timing data.

[0103] According to embodiments of this disclosure, this disclosure also provides an electronic device, an autonomous vehicle, a readable storage medium, and a computer program product.

[0104] Figure 7 A schematic block diagram of an example electronic device 700 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0105] like Figure 7As shown, device 700 includes a computing unit 701, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 702 or a computer program loaded from storage unit 708 into random access memory (RAM) 703. RAM 703 may also store various programs and data required for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704.

[0106] Multiple components in device 700 are connected to I / O interface 705, including: input unit 706, such as keyboard, mouse, etc.; output unit 707, such as various types of monitors, speakers, etc.; storage unit 708, such as disk, optical disk, etc.; and communication unit 709, such as network card, modem, wireless transceiver, etc. Communication unit 709 allows device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0107] The computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above, such as a method for compressing timing data of traffic lights. For example, in some embodiments, the method for compressing timing data of traffic lights can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on device 700 via ROM 702 and / or communication unit 709. When the computer program is loaded into RAM 703 and executed by the computing unit 701, one or more steps of the method for compressing timing data of traffic lights described above can be performed. Alternatively, in other embodiments, the computing unit 701 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for compressing the timing data of traffic lights.

[0108] This disclosure also provides an autonomous vehicle that may include electronic devices as described in the above embodiments, the electronic devices being used to perform a method for compressing timing data of traffic lights as described in the above embodiments.

[0109] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0111] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the 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 can be received in any form (including sound input, voice input, or tactile input).

[0113] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0114] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0115] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the method for compressing the timing data of traffic lights as described above.

[0116] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0117] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for compressing time-series data of traffic lights, wherein, include: The original time-series data of the traffic lights is obtained, and feature extraction is performed on the original time-series data to obtain the feature information of the traffic lights; The extracted feature information is tracked to identify whether the feature information changes abruptly; The feature information of the jump is identified from the original time series data, and the original time series data corresponding to the feature information of the jump is extracted to obtain the target time series data; The target feature information corresponding to the target time series data is aggregated to obtain the aggregated feature information of the target time series data. The target time-series data and the aggregated feature information are compressed to obtain the compressed time-series data of the traffic light.

2. The method according to claim 1, wherein, The step of compressing the target time-series data and the aggregated feature information to obtain compressed time-series data includes: Obtain the first business requirement for the target time-series data; Obtain aggregated feature information of the target time-series data corresponding to the first business requirement; The target time series data and the aggregated feature information are compressed to obtain the compressed time series data.

3. The method according to claim 1, wherein, The step of compressing the target time series data and the aggregated feature information to obtain compressed time series data includes: Obtain the timestamp associated with the target time series data; Based on the timestamp, the compressed time-series data is restored to obtain the target time-series data associated with the timestamp and the aggregated feature information.

4. The method according to any one of claims 1-3, wherein, After obtaining the compressed timing data of the traffic lights, the process includes: Obtain the second service requirement for the compressed time-series data; Based on the required characteristics of the second business requirement, feature extraction is performed on the compressed time-series data related to the second business requirement to obtain the result corresponding to the second business request.

5. The method according to claim 1, wherein, The acquisition of the original time-series data of the traffic lights includes: Receive the compressed package of timing data of the traffic lights reported by the vehicle; The compressed package is decompressed to obtain the original timing data of the traffic lights.

6. A device for compressing timing data of traffic lights, comprising: The acquisition module is used to acquire the original time-series data of the traffic lights and perform feature extraction on the original time-series data to obtain the feature information of the traffic lights; A tracking module is used to track the extracted feature information in order to identify whether the feature information changes abruptly. The extraction module is used to identify the feature information of the jump from the original time series data, and extract the original time series data corresponding to the feature information of the jump to obtain the target time series data. Compression module, used for target time series data The target feature information corresponding to the target time series data is aggregated to obtain the aggregated feature information of the target time series data. The target time-series data and the aggregated feature information are compressed to obtain the compressed time-series data of the traffic light.

7. The compression device according to claim 6, wherein, The compression module is also used for: Obtain the first business requirement for the target time-series data; Obtain aggregated feature information of the target time-series data corresponding to the first business requirement; The target time series data and the aggregated feature information are compressed to obtain the compressed time series data.

8. The compression device according to claim 6, wherein, The device is also used for: Obtain the timestamp associated with the target time series data; Based on the timestamp, the compressed time-series data is restored to obtain the target time-series data associated with the timestamp and the aggregated feature information.

9. The compression device according to any one of claims 6-8, wherein, The device is also used for: Obtain the second service requirement for the compressed time-series data; Based on the required characteristics of the second business requirement, feature extraction is performed on the compressed time-series data related to the second business requirement to obtain the result corresponding to the second business request.

10. The compression device according to claim 6, wherein, The acquisition module is also used for: Receive the compressed package of timing data of the traffic lights reported by the vehicle; The compressed package is decompressed to obtain the original timing data of the traffic lights.

11. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

13. A computer program product comprising a computer program that, when executed by a processor, implements the steps according to any one of claims 1-5.

14. An autonomous vehicle, wherein, Including the electronic device as described in claim 11.

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