CN115497194A - Driving data processing method and device and storage medium - Google Patents

Driving data processing method and device and storage medium Download PDF

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Publication number
CN115497194A
CN115497194A CN202210876758.6A CN202210876758A CN115497194A CN 115497194 A CN115497194 A CN 115497194A CN 202210876758 A CN202210876758 A CN 202210876758A CN 115497194 A CN115497194 A CN 115497194A
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abnormal
data
driving
algorithm
driving data
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尹扬
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Ningbo Lutes Robotics Co ltd
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Wuhan Lotus Technology Co Ltd
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network

Abstract

The application discloses a driving data processing method, which is applied to a vehicle-mounted terminal and comprises the following steps: controlling the target vehicle to drive automatically based on a driving control algorithm; storing automatic driving data generated by a target vehicle in an automatic driving process; carrying out anomaly detection on the automatic driving data based on a monitoring algorithm; under the condition that abnormal driving data corresponding to a target abnormal event exist in the automatic driving data, sending the abnormal driving data corresponding to the target abnormal event to a cloud server; receiving algorithm updating data sent by a cloud server, wherein the algorithm updating data are obtained by performing algorithm optimization on a driving control algorithm based on abnormal driving data; and updating the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm. According to the driving data processing method, the technical staff can access abnormal driving data under the condition of a non-contact vehicle, and meanwhile, the driving control algorithm can be updated under the condition of the non-contact vehicle.

Description

Driving data processing method and device and storage medium
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a driving data processing method, device, and storage medium.
Background
Automatic driving is the development direction of future driving technology, can replace manual driving completely, generally adopts automatic driving system based on advanced communication, computer, network and control technology, realizes real-time and continuous control to the vehicle, along with the continuous development of automatic driving technology, automatic driving function is becoming perfect and diversified day by day.
At present, when a vehicle is abnormal in an automatic driving process, a technician needs to actively perform physical contact communication with the vehicle, access a vehicle storage medium and read abnormal driving data through a vehicle-mounted interface by using a local debugging tool, for example, access the vehicle storage medium and read a DTC (Diagnostic Trouble Code) through an OBD interface by using a Diagnostic apparatus. In addition, when a driving control algorithm capable of realizing automatic driving is optimally updated, repair and upgrade of a vehicle are required. The prior art has high labor cost, low efficiency and poor timeliness for processing abnormal conditions of the vehicle in the automatic driving process.
Disclosure of Invention
The application provides a driving data processing method, a driving data processing device and a storage medium, which can realize that a technician accesses abnormal driving data under the condition of a non-contact vehicle, and can simultaneously realize the updating of a driving control algorithm under the condition of the non-contact vehicle, reduce the processing cost of abnormal conditions in the automatic driving process and improve the processing efficiency and timeliness.
In one aspect, the present application provides a driving data processing method applied to a vehicle-mounted terminal, where the method includes:
controlling the target vehicle to automatically drive based on a driving control algorithm;
storing autonomous driving data generated by the target vehicle during autonomous driving;
performing anomaly detection on the automatic driving data based on a monitoring algorithm;
under the condition that abnormal driving data corresponding to a target abnormal event exist in the automatic driving data, sending the abnormal driving data corresponding to the target abnormal event to a cloud server;
receiving algorithm updating data sent by the cloud server, wherein the algorithm updating data is obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data;
and updating the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
Preferably, after the abnormal driving data corresponding to the target abnormal event is sent to a cloud server when the abnormal driving data corresponding to the target abnormal event is detected to exist in the automatic driving data, the method further includes:
and deleting the abnormal driving data under the condition that the abnormal driving data is successfully transmitted.
Preferably, the monitoring algorithm-based abnormality detection of the automatic driving data includes:
screening abnormal data of the automatic driving data based on the monitoring algorithm to obtain initial abnormal data;
determining an abnormal event corresponding to the initial abnormal data based on a preset corresponding relation, wherein the preset corresponding relation represents the corresponding relation between the abnormal data and the abnormal event;
determining an abnormality tolerance corresponding to the initial abnormal data, wherein the abnormality tolerance characterizes an abnormal level of abnormal data;
and when the abnormal tolerance is larger than a tolerance threshold corresponding to the abnormal event, determining the initial abnormal data as the abnormal driving data corresponding to the target abnormal event.
Preferably, the determining the anomaly tolerance corresponding to the initial anomaly data includes:
determining a target event type of an abnormal event corresponding to the initial abnormal data;
and calculating the tolerance of the initial abnormal data based on a tolerance calculation algorithm corresponding to the target event category to obtain the abnormal tolerance.
Preferably, the method further comprises:
and sending abnormal alarm information to the cloud server under the condition that the abnormal tolerance is greater than an alarm threshold, wherein the alarm threshold is greater than a tolerance threshold corresponding to the abnormal event, and the abnormal alarm information is used for indicating the cloud server to send the abnormal driving data corresponding to the target abnormal event to a remote processing end.
Preferably, the driving data processing method further includes:
and sending the identification code of the target vehicle to the cloud server, wherein the identification code represents the identity information of the target vehicle.
Preferably, the driving data processing method further includes:
storing reference driving data of the target vehicle in the non-automatic driving process of the target vehicle, wherein the reference driving data are data generated by performing preset reference operation or under preset driving conditions in the non-automatic driving process of the target vehicle;
transmitting the reference driving data to the cloud server.
In another aspect, the present application provides a driving data processing method applied to a cloud server, including:
receiving abnormal driving data corresponding to a target abnormal event, wherein the abnormal driving data corresponding to the target abnormal event is sent by a vehicle-mounted terminal under the condition that the abnormal driving data corresponding to the target abnormal event exists in the automatic driving data, and the automatic driving data is generated and stored in the process that the vehicle-mounted terminal controls the automatic driving of a target vehicle based on a driving control algorithm;
acquiring algorithm updating data, wherein the algorithm updating data is obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data;
and sending the algorithm updating data to the vehicle-mounted terminal so that the vehicle-mounted terminal updates the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
In another aspect, the present application provides a driving data processing apparatus, the apparatus including:
a control module: for controlling the target vehicle to autonomously drive based on a driving control algorithm;
a first storage module: for storing autonomous driving data generated by the target vehicle during autonomous driving;
a detection module: for anomaly detection of the autopilot data based on a monitoring algorithm;
a first sending module: the automatic driving data processing system is used for sending abnormal driving data corresponding to a target abnormal event to a cloud server under the condition that the abnormal driving data corresponding to the target abnormal event exists in the automatic driving data;
a first receiving module: the driving control algorithm updating system is used for receiving algorithm updating data sent by the cloud server, and the algorithm updating data is obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data;
a first update module: and the driving control algorithm is updated based on the algorithm updating data to obtain an updated driving control algorithm.
In another aspect, the present application provides a driving data processing apparatus, the apparatus including:
a second receiving module: the system comprises a vehicle-mounted terminal, a monitoring algorithm and a cloud server, wherein the vehicle-mounted terminal is used for receiving abnormal driving data corresponding to a target abnormal event, storing automatic driving data generated by a target vehicle in an automatic driving process based on a driving control algorithm in the process of controlling the target vehicle to automatically drive, carrying out abnormal detection on the automatic driving data based on the monitoring algorithm, and sending the abnormal driving data corresponding to the target abnormal event to the cloud server to obtain the abnormal driving data corresponding to the target abnormal event under the condition that the abnormal driving data corresponding to the target abnormal event exists in the automatic driving data;
an acquisition module: the driving control algorithm updating device is used for acquiring algorithm updating data, and the algorithm updating data is obtained by carrying out algorithm optimization on the driving control algorithm based on the abnormal driving data;
a second update module: and the algorithm updating data is sent to the vehicle-mounted terminal so that the vehicle-mounted terminal updates the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
In another aspect, the present application provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded by a processor and executes the driving data processing method as described above.
In another aspect, the present application provides an electronic device implementing the driving data processing method, where the electronic device includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the driving data processing method.
The driving data processing method, the driving data processing device and the storage medium have the following beneficial effects:
the technical scheme of the application controls the target vehicle to automatically drive based on the driving control algorithm; storing automatic driving data generated by a target vehicle in an automatic driving process; carrying out anomaly detection on the automatic driving data based on a monitoring algorithm; under the condition that abnormal driving data corresponding to the target abnormal event exist in the automatic driving data, sending the abnormal driving data corresponding to the target abnormal event to a cloud server; receiving algorithm updating data sent by a cloud server, wherein the algorithm updating data is obtained by performing algorithm optimization on a driving control algorithm based on abnormal driving data; updating the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm; therefore, the abnormal driving data can be accessed by a technician under the condition of a non-contact vehicle, meanwhile, the driving control algorithm can be updated under the condition of the non-contact vehicle, the processing cost of the abnormal condition in the automatic driving process is reduced, and the processing efficiency and the timeliness are improved.
Drawings
In order to more clearly illustrate the technical solutions and advantages of the embodiments or the prior art of the present application, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic application environment diagram of a driving data processing method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a driving data processing method according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating the process of detecting anomalies in autopilot data according to an embodiment of the present application;
FIG. 4 is a schematic flow chart illustrating a process of determining anomaly tolerance corresponding to initial anomaly data according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a driving data processing method according to an embodiment of the present application;
FIG. 6 is a schematic flow chart illustrating another driving data processing method according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of a driving data processing device according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of another driving data processing device according to an embodiment of the present application;
fig. 9 is a block diagram of a hardware structure of an electronic device for implementing a driving data processing method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Artificial Intelligence (AI): the theory, method, technique and application system that uses digital computer or machine controlled by digital computer to simulate, extend and expand human intelligence, sense environment, obtain knowledge and use knowledge to obtain optimal result. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making. The artificial intelligence technology is a comprehensive subject, and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence software technology mainly comprises a computer vision technology, a machine learning/deep learning direction and the like.
Machine Learning (ML): the method is a multi-field cross discipline and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The method specially studies how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning generally include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formal education learning.
Deep learning: deep learning is one of machine learning, and machine learning is a must-pass path for realizing artificial intelligence. The concept of deep learning is derived from the research of artificial neural networks, and a multi-layer perceptron with a plurality of hidden layers is a deep learning structure, and the deep learning forms more abstract high-layer representation attribute categories or features by combining low-layer features so as to find distributed feature representations of data.
In recent years, with research and development of artificial intelligence technology, artificial intelligence technology is widely applied in a plurality of fields, and the scheme provided by the embodiment of the application relates to technologies such as machine learning/deep learning of artificial intelligence and natural language processing, and is specifically described by the following embodiments.
Referring to fig. 1, fig. 1 is a schematic view of an application environment provided in an embodiment of the present application, and as shown in fig. 1, the application environment may include at least a vehicle-mounted terminal 01 and a cloud server 02. In practical applications, the in-vehicle terminal 01 and the cloud server 02 may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.
The cloud server 02 in this embodiment may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform, and the like.
Specifically, cloud technology (Cloud technology) refers to a hosting technology for unifying a series of resources such as hardware, software, network, etc. in a wide area network or a local area network to realize calculation, storage, processing and sharing of data. Cloud technology can be applied to various fields such as medical cloud, cloud internet of things, cloud security, cloud education, cloud conference, artificial intelligence cloud service, cloud application, cloud calling, cloud social interaction and the like, and is applied based on a cloud computing (cloud computing) business model, computing tasks are distributed on a resource pool formed by a large number of computers, and various application systems can acquire computing power, storage space and information service as required. The network providing resources is called "cloud", and resources in the cloud appear to be infinitely expandable to users, and can be acquired at any time, used as required, expanded at any time and paid for by use. As a basic capability provider of cloud computing, a cloud computing resource pool (called as laas (Infrastructure as a Service) platform for short) is established, and multiple types of virtual resources are deployed in the resource pool and are selectively used by external clients. The cloud computing resource pool mainly comprises: computing devices (being virtualized machines, including operating systems), storage devices, network devices.
Specifically, the cloud server 02 may include an entity device, may specifically include a network communication sub-module, a processor, a memory, and the like, may also include software running in the entity device, and may specifically include an application program, and the like.
Specifically, the remote processing end 03 may include a smart phone, a desktop computer, a tablet computer, a laptop computer, a digital assistant, an Augmented Reality (AR)/Virtual Reality (VR) device, an intelligent voice interaction device, an intelligent household appliance, an intelligent wearable device, a vehicle-mounted terminal device, and other types of physical devices, and may also include software running in the physical devices, such as an application program.
In the embodiment of the application, the vehicle-mounted terminal 01 can be used for sending abnormal driving data corresponding to a target abnormal event to the cloud server 02, sending algorithm data, vehicle state data and associated component data corresponding to the abnormal driving data to the cloud server 02, sending an identification code of a target vehicle to the cloud server 02, and sending reference driving data to the cloud server 02. The cloud server 02 may be configured to receive abnormal driving data corresponding to the target abnormal event sent by the vehicle-mounted terminal 01, and send the acquired algorithm update data to the vehicle-mounted terminal 01.
Furthermore, it is understood that fig. 1 illustrates only one application environment of the driving data processing method, and the application environment may include more or less nodes, and the application is not limited herein.
Referring to fig. 2, a driving data processing method provided in an embodiment of the present application is described below, where the driving data processing method is applied to a vehicle-mounted terminal, fig. 2 is a schematic flow diagram of the driving data processing method provided in the embodiment of the present application, and please refer to fig. 2, the driving data processing method provided in the embodiment of the present application includes:
s201, controlling the target vehicle to automatically drive based on a driving control algorithm.
In the embodiment of the present application, the driving control algorithm is a relevant algorithm for realizing vehicle driving automation, and specifically, the driving control algorithm includes, but is not limited to, a perception algorithm, a prediction algorithm, a planning algorithm, a decision algorithm, and a control algorithm.
In some embodiments, the driving automation system of the in-vehicle terminal controls the target vehicle to be automatically driven based on the driving control algorithm.
Specifically, the automatic driving comprises an L2-L5 level, the L2 level represents partial automatic driving, the vehicle provides driving support for multiple operations in a steering wheel and acceleration and deceleration, and a driver is responsible for other driving operations; the L3 level representation condition is automatically driven, the vehicle finishes most driving operations, and the driver needs to provide response at a proper time; the L4 level represents highly autonomous driving, all driving operations are completed by the vehicle, the driver does not need to respond to all system requests, but limits road and environmental conditions; the L5 level characterizes fully autonomous driving, all driving operations being performed by the vehicle, without the driver needing to keep his attention.
And S203, storing automatic driving data generated by the target vehicle in the automatic driving process.
In some embodiments, the automated driving data includes operational result data of a driving automation system, which refers to a system composed of hardware and software that implement driving automation together.
Specifically, the automatic driving data includes operation result data of a driving control algorithm, and the operation result data of the driving control algorithm includes operation result data of a perception algorithm, a prediction algorithm, a planning algorithm, a decision algorithm, and a control algorithm.
Specifically, the automatic driving data includes abnormal driving data and normal driving data.
And S205, carrying out abnormity detection on the automatic driving data based on a monitoring algorithm.
In some embodiments, a monitoring algorithm for detecting automatic driving data generated by the target vehicle during automatic driving and identifying abnormal driving data is preset in the driving automation system. Specifically, referring to fig. 3, S205 includes:
s301, screening abnormal data of the automatic driving data based on a monitoring algorithm to obtain initial abnormal data.
In some embodiments, the monitoring algorithm includes a screening algorithm for performing abnormal data screening on the autonomous driving data based on the standard driving data to obtain initial abnormal data. Specifically, the standard driving data is preset data representing expected operation result data of the target vehicle driving automation system.
In some embodiments, the initial abnormal data is autopilot data corresponding to an abnormal condition generated by the target vehicle during autopilot, such as autopilot data corresponding to an abnormal termination of the drive automation system and a degraded operating condition of the drive automation system.
And S303, determining an abnormal event corresponding to the initial abnormal data based on a preset corresponding relation, wherein the preset corresponding relation represents the corresponding relation between the abnormal data and the abnormal event.
In some embodiments, based on the preset correspondence and the initial abnormal data, an abnormal event corresponding to the initial abnormal data may be determined.
In some embodiments, the abnormal event is set in the driving automation system in advance, and represents an abnormality of the driving automation system occurring in the target vehicle during the automatic driving. Specifically, the abnormal event may include, but is not limited to, an abnormal termination or exit of the driving automation system, a degraded operation of the driving automation system, a target vehicle having a vehicle speed equal to or greater than a preset vehicle speed or a steering wheel angle change rate equal to or greater than a preset change rate in a case where the driving automation system is not turned on, and the like. The degraded operation means that the driving automation system performs only a part of the original functions.
It should be noted that the abnormal event may include a conventional abnormal event, and may also include a personalized abnormal event customized by a vehicle manufacturer, and the abnormal event is not limited in the present application.
S305, determining the corresponding exception tolerance of the initial exception data, wherein the exception tolerance characterizes the exception grade of the exception data.
In some embodiments, the monitoring algorithm includes a tolerance calculation algorithm for calculating an anomaly tolerance of the initial anomaly data based on the initial anomaly data. The larger the anomaly tolerance of the initial abnormal data is, the higher the anomaly level of the initial abnormal data is, and the more dangerous the abnormal condition represented by the initial abnormal data is; conversely, the lower the risk of an abnormal condition characterized by the initial anomaly data. Specifically, referring to fig. 4, S305 includes:
s401, determining the target event type of the abnormal event corresponding to the initial abnormal data.
In some embodiments, the exceptions are classified by urgency, and the event categories include class i, class ii, and class iii, and in particular, class i may be algorithm level events, class ii may be controller level events, and class iii may be vehicle level events.
Specifically, the algorithm level event may include that the positioning information in the algorithm component is normal in state, but the high-precision positioning information jumps; controller level events may include chassis power controller issuing an unavailable status; the vehicle level events may include lateral acceleration calculated by the algorithm and user driving lateral bias under a large curvature curve road.
And S403, tolerance calculation is carried out on the initial abnormal data based on a tolerance calculation algorithm corresponding to the target event type, and the abnormal tolerance is obtained.
In some embodiments, under the condition that the abnormal event corresponding to the initial abnormal data is a class I event, tolerance calculation is performed on the initial abnormal data based on a first tolerance calculation algorithm of the class I event to obtain the abnormal tolerance of the initial abnormal data; under the condition that the abnormal event corresponding to the initial abnormal data is a class II event, carrying out tolerance calculation on the initial abnormal data based on a second tolerance calculation algorithm of the class II event to obtain the abnormal tolerance of the initial abnormal data; and under the condition that the abnormal event corresponding to the initial abnormal data is a III-type event, carrying out tolerance calculation on the initial abnormal data based on a third tolerance calculation algorithm of the III-type event to obtain the abnormal tolerance of the initial abnormal data.
And S307, under the condition that the abnormal tolerance is larger than the tolerance threshold corresponding to the abnormal event, determining the initial abnormal data as the abnormal driving data corresponding to the target abnormal event.
In some embodiments, the tolerance threshold corresponding to an exception event characterizes an upper value of an exception that the initial exception data can be accepted. When the anomaly tolerance of the initial abnormal data is less than or equal to the tolerance threshold corresponding to the abnormal event, the abnormal grade of the initial abnormal data is low, and the driving automation system can basically realize the driving automation of the target vehicle; and under the condition that the abnormal tolerance of the initial abnormal data is greater than the tolerance threshold corresponding to the abnormal event, the abnormal grade of the initial abnormal data is high, namely the initial abnormal data is determined to be the abnormal driving data corresponding to the target abnormal event.
In some embodiments, the method for determining the tolerance threshold corresponding to the abnormal event includes: setting a tolerance threshold according to the operation result of a corresponding algorithm in the driving automation system under the condition that the abnormal event is the I-type event; under the condition that the abnormal event is a class II event, the tolerance threshold is a threshold which can be stably output by the domain controller and is set according to the bench test result; and when the abnormal event is a III-type event, setting a tolerance threshold value in a manual driving state according to a road test result.
S207, under the condition that abnormal driving data corresponding to the target abnormal event exist in the automatic driving data, the abnormal driving data corresponding to the target abnormal event are sent to a cloud server.
Specifically, a technician can remotely access the cloud server to obtain abnormal driving data corresponding to the target abnormal event, so that the technician can access the abnormal driving data in a non-contact manner, the processing cost of abnormal conditions in the automatic driving process is reduced, and the processing efficiency and the timeliness are improved.
In some embodiments, abnormal driving data corresponding to the target abnormal event is wirelessly sent to the cloud server. The transmission algorithm is used for realizing transmission of abnormal driving data corresponding to the target abnormal event between a local storage medium of the target vehicle and the cloud server.
In some embodiments, algorithm data corresponding to the abnormal driving data is wirelessly transmitted to the cloud server based on the transmission algorithm. The algorithm data is preset data of a driving control algorithm for realizing automatic driving, and includes but is not limited to perception algorithm data, prediction algorithm data, planning algorithm data, decision algorithm data and control algorithm data.
In some embodiments, vehicle state data and associated component data corresponding to the abnormal driving data are wirelessly transmitted to the cloud server at the same time. The vehicle state data comprises the running state data of wheels, and the associated component data comprises the running state data of components which are related to the automatic driving of the target vehicle and do not belong to the driving automation system, including but not limited to the use state data of the vehicle-mounted human-computer interaction device, such as the use state data of a central control screen.
In some embodiments, the identification code of the target vehicle is sent to the cloud server at the same time. The identification code represents identity information of the target vehicle, and specifically, the identification code may be a frame number of the target vehicle.
In an embodiment of the present application, the driving data processing method further includes: and sending abnormal alarm information to the cloud server under the condition that the abnormal tolerance is greater than the alarm threshold, wherein the alarm threshold is greater than the tolerance threshold corresponding to the abnormal event, and the abnormal alarm information is used for indicating the cloud server to send the abnormal driving data corresponding to the target abnormal event to the remote processing end.
In some embodiments, when the anomaly tolerance is greater than the alarm threshold, it is indicated that the anomaly level of the initial anomaly data is extremely high, that is, the anomaly alarm information is sent to the cloud server, so that the cloud server sends the abnormal driving data corresponding to the target anomaly event to the remote processing terminal, so as to start an emergency processing scheme.
It should be noted that the abnormal driving data corresponding to the target abnormal event sent to the cloud server is desensitized data after desensitization processing.
Further, after S207, the driving data processing method further includes: and deleting the abnormal driving data when the abnormal driving data is successfully transmitted. In this way, the problem that abnormal driving data is easily lost due to the size of the vehicle storage medium can be solved.
In some embodiments, the cloud server interacts with the target vehicle-mounted terminal, after the cloud server successfully receives abnormal driving data corresponding to the target abnormal event, the cloud server sends an acknowledgement to the target vehicle-mounted terminal, and after the target vehicle-mounted terminal receives the acknowledgement sent by the cloud server, the target vehicle-mounted terminal deletes the abnormal driving data corresponding to the target abnormal event in a local storage medium of the target vehicle, so that the storage space of the local storage medium of the target vehicle is released, and the problem that the abnormal driving data are easily lost due to the fact that the abnormal driving data are limited by the size of the vehicle storage medium can be solved. And under the condition that the target vehicle-mounted terminal does not receive the acknowledgement sent by the cloud server due to poor regional signals and the like, reserving the abnormal driving data corresponding to the target abnormal event in the local storage medium of the target vehicle until the acknowledgement sent by the cloud server is received. The positive response represents that the cloud server successfully receives abnormal driving data corresponding to the target abnormal event sent by the target vehicle-mounted terminal.
Correspondingly, under the condition that the transmission of the algorithm data, the vehicle state data and the associated component data corresponding to the abnormal driving data is successful, the algorithm data, the vehicle state data and the associated component data corresponding to the abnormal driving data are deleted from the local storage medium of the target vehicle, so that the storage space of the local storage medium of the target vehicle is released, and the problem that the abnormal driving data are easily lost due to the limitation of the size of the vehicle storage medium can be solved.
S209, algorithm updating data sent by the cloud server are received, and the algorithm updating data are obtained by performing algorithm optimization on the driving control algorithm based on abnormal driving data.
In some embodiments, an optimization module is arranged in the cloud server, and the optimization module performs algorithm optimization on the driving control algorithm based on the abnormal driving data to obtain algorithm update data. And then, sending the algorithm updating data to the target vehicle-mounted terminal. The algorithm update data may include perceptual algorithm update data, predictive algorithm update data, planning algorithm update data, decision algorithm update data, and control algorithm update data.
In some embodiments, a technician may remotely access the cloud server, obtain abnormal driving data corresponding to a target abnormal event, algorithm data corresponding to the abnormal driving data, vehicle state data and associated component data, perform algorithm optimization on a driving control algorithm through a manual analysis or optimization algorithm to obtain algorithm update data, and then send the algorithm update data to the cloud server through a standardized program so that the cloud server forwards the algorithm update data to a target vehicle-mounted terminal.
And S211, updating the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
In some embodiments, the target vehicle-mounted terminal updates the driving control algorithm based on the algorithm update data sent by the cloud server, so that the driving control algorithm can be updated under the condition of a non-contact vehicle.
In the embodiment of the present application, please refer to fig. 5, the driving data processing method further includes:
s501, storing reference driving data of the target vehicle in the non-automatic driving process of the target vehicle, wherein the reference driving data are data generated by performing preset reference operation or under preset driving conditions in the non-automatic driving process of the target vehicle.
In some embodiments, during non-automated driving of the target vehicle, driving data corresponding to a preset reference operation or a preset driving condition is stored in a local storage medium of the target vehicle. Specifically, the preset driving condition includes that the target vehicle passes through a road with specific characteristics under non-automatic driving, such as a narrow road, a mountain road and the like. Specifically, the reference driving data is used for optimization of the driving control algorithm.
And S503, sending the reference driving data to a cloud server.
In some embodiments, the target vehicle-mounted terminal sends the reference driving data to the cloud server, and correspondingly, under the condition that the reference driving data are successfully transmitted, the reference driving data are deleted from the local storage medium of the target vehicle, so that the storage space of the local storage medium of the target vehicle is released, and the problem that the abnormal driving data and the reference driving data are easily lost due to the fact that the size of the vehicle storage medium is limited can be solved.
Further, a technician may remotely access the cloud server and obtain the reference driving data to optimize the driving control algorithm, so that obtaining the reference driving data under the condition of a non-contact vehicle may be achieved.
The driving data processing method is applied to the vehicle-mounted terminal, and controls the target vehicle to automatically drive based on the driving control algorithm; storing automatic driving data generated by a target vehicle in an automatic driving process; carrying out anomaly detection on the automatic driving data based on a monitoring algorithm; under the condition that abnormal driving data corresponding to a target abnormal event exist in the automatic driving data, sending the abnormal driving data corresponding to the target abnormal event to a cloud server; receiving algorithm updating data sent by a cloud server, wherein the algorithm updating data is obtained by performing algorithm optimization on a driving control algorithm based on abnormal driving data; updating the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm; therefore, technicians can access abnormal driving data under the condition of non-contact vehicles, meanwhile, the driving control algorithm can be updated under the condition of non-contact vehicles, the processing cost of abnormal conditions in the automatic driving process is reduced, and the processing efficiency and timeliness are improved; further, by deleting the abnormal driving data in the case where the transmission of the abnormal driving data is successful, it is possible to solve the problem that the abnormal driving data is easily lost due to the size of the vehicle storage medium being limited.
Referring to fig. 6, another driving data processing method provided in the embodiment of the present application is described below, and is applied to a cloud server, and referring to fig. 6, the driving data processing method provided in the embodiment of the present application includes:
s601, receiving abnormal driving data corresponding to a target abnormal event, wherein the abnormal driving data corresponding to the target abnormal event is sent by a vehicle-mounted terminal when the vehicle-mounted terminal detects that abnormal driving data corresponding to the target abnormal event exists in the automatic driving data based on a monitoring algorithm, and the automatic driving data is generated and stored by the vehicle-mounted terminal in the process of controlling the automatic driving of a target vehicle based on a driving control algorithm.
S603, algorithm updating data are obtained, and the algorithm updating data are obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data.
And S605, sending the algorithm updating data to the vehicle-mounted terminal so that the vehicle-mounted terminal updates the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
In the embodiment of the present application, after S601, the driving data processing method further includes:
and sending a positive response to the target vehicle-mounted terminal so that the target vehicle-mounted terminal deletes the abnormal driving data corresponding to the target abnormal event in the local storage medium of the target vehicle. In this way, the storage space of the local storage medium of the target vehicle is released, so that the problem that abnormal driving data is easily lost due to the size of the vehicle storage medium can be solved. The positive response represents that the cloud server successfully receives abnormal driving data corresponding to the target abnormal event sent by the target vehicle-mounted terminal.
In an embodiment of the present application, the driving data processing method further includes:
and receiving abnormal alarm information, and sending abnormal driving data corresponding to the target abnormal event to the remote processing terminal, wherein the abnormal alarm information is sent by the vehicle-mounted terminal under the condition that the abnormal tolerance corresponding to the abnormal driving data is greater than an alarm threshold value, so that an emergency processing scheme is started.
In an embodiment of the present application, the driving data processing method further includes:
and receiving the identification code of the target vehicle sent by the vehicle-mounted terminal, wherein the identification code represents the identity information of the target vehicle. Specifically, the identification code may be a frame number of the target vehicle.
In an embodiment of the present application, the driving data processing method further includes:
and receiving reference driving data sent by the vehicle-mounted terminal, wherein the reference driving data are data generated by carrying out preset reference operation or preset driving working conditions in the non-automatic driving process of the target vehicle. The reference driving data is used to optimize the driving control algorithm, so that the reference driving data can be obtained in the case of a contactless vehicle.
An embodiment of the present application further provides a driving data processing apparatus, please refer to fig. 7, and the driving data processing apparatus provided in the embodiment of the present application includes:
the control module 710: for controlling the target vehicle to autonomously drive based on a driving control algorithm.
The first storage module 720: for storing autonomous driving data generated by the target vehicle during autonomous driving.
The detection module 730: for anomaly detection of the autopilot data based on the monitoring algorithm.
The first transmitting module 740: and the automatic driving data processing unit is used for sending the abnormal driving data corresponding to the target abnormal event to the cloud server when detecting that the abnormal driving data corresponding to the target abnormal event exists in the automatic driving data.
The first receiving module 750: the method is used for receiving algorithm updating data sent by the cloud server, and the algorithm updating data are obtained by performing algorithm optimization on the driving control algorithm based on abnormal driving data.
The first update module 760: and the driving control algorithm updating module is used for updating the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
In this embodiment of the application, the detecting module 730 includes:
a processing unit: and the automatic driving data screening device is used for screening abnormal data of the automatic driving data based on a monitoring algorithm to obtain initial abnormal data.
A first determination unit: and the method is used for determining the abnormal event corresponding to the initial abnormal data based on a preset corresponding relation, and the preset corresponding relation represents the corresponding relation between the abnormal data and the abnormal event.
A second determination unit: and the abnormal tolerance is used for determining the abnormal tolerance corresponding to the initial abnormal data, and the abnormal tolerance characterizes the abnormal level of the abnormal data.
A third determination unit: and the abnormal driving data processing unit is used for determining the initial abnormal data as the abnormal driving data corresponding to the target abnormal event when the abnormal tolerance is larger than the tolerance threshold corresponding to the abnormal event.
In an embodiment of the present application, the second determining unit includes:
a first determining subunit: and the method is used for determining the target event category of the abnormal event corresponding to the initial abnormal data.
A processing subunit: and the tolerance calculation method is used for calculating the tolerance of the initial abnormal data based on the tolerance corresponding to the target event category to obtain the abnormal tolerance.
In an embodiment of the present application, the driving data processing apparatus further includes:
a second storage module: the device is used for storing reference driving data of the target vehicle in the non-automatic driving process of the target vehicle, and the reference driving data are data generated by carrying out preset reference operation or preset driving working conditions in the non-automatic driving process of the target vehicle.
A second sending module: for sending the reference driving data to the cloud server.
Referring to fig. 8, the driving data processing apparatus provided in the embodiment of the present application further includes:
the second receiving module 810: the system comprises a monitoring algorithm, a cloud server and a vehicle-mounted terminal, and is used for receiving abnormal driving data corresponding to a target abnormal event, wherein the abnormal driving data corresponding to the target abnormal event is obtained by the steps that the vehicle-mounted terminal stores automatic driving data generated by a target vehicle in an automatic driving process based on a driving control algorithm in the process of controlling the target vehicle to automatically drive, carrying out abnormal detection on the automatic driving data based on the monitoring algorithm, and sending the abnormal driving data corresponding to the target abnormal event to the cloud server under the condition that the abnormal driving data corresponding to the target abnormal event exists in the automatic driving data.
The obtaining module 820: the method is used for obtaining algorithm updating data, and the algorithm updating data is obtained by carrying out algorithm optimization on the driving control algorithm based on abnormal driving data.
The second update module 830: and the vehicle-mounted terminal is used for sending the algorithm updating data to the vehicle-mounted terminal so as to update the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
In an embodiment of the present application, the driving data processing apparatus further includes:
a third sending module: and the system is used for sending a positive response to the target vehicle-mounted terminal so that the target vehicle-mounted terminal deletes the abnormal driving data corresponding to the target abnormal event in the local storage medium of the target vehicle.
In an embodiment of the present application, the driving data processing apparatus further includes:
and the third receiving module is used for receiving abnormal alarm information and sending abnormal driving data corresponding to the target abnormal event to the remote processing terminal, wherein the abnormal alarm information is sent by the vehicle-mounted terminal under the condition that the abnormal tolerance corresponding to the abnormal driving data is greater than the alarm threshold.
In an embodiment of the present application, the driving data processing apparatus further includes:
and the fourth receiving module is used for receiving the identification code of the target vehicle sent by the vehicle-mounted terminal, and the identification code represents the identity information of the target vehicle.
In an embodiment of the present application, the driving data processing apparatus further includes:
and the fifth receiving module is used for receiving the reference driving data sent by the vehicle-mounted terminal, wherein the reference driving data is data generated by carrying out preset reference operation or preset driving working conditions in the non-automatic driving process of the target vehicle.
The device and method embodiments in the device embodiment described above are based on the same application concept.
Referring to fig. 9, an embodiment of the present application provides an electronic device for implementing the driving data processing method, where the electronic device includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the driving data processing method provided in the foregoing method embodiment.
The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to use of the apparatus, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, a server, or a similar computing device, that is, the electronic device may include a mobile terminal, a computer terminal, a server, or a similar computing device. The server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN, big data and an artificial intelligence platform. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like.
Fig. 9 is a block diagram of a hardware structure of an electronic device for implementing the driving data processing method according to an embodiment of the present application. As shown in fig. 9, the electronic device 900 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 910 (the processor 910 may include but is not limited to a Processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 930 for storing data, and one or more storage media 920 (e.g., one or more mass storage devices) for storing applications 923 or data 922. Memory 930 and storage media 920 may be, among other things, transient or persistent storage. The program stored in the storage medium 920 may include one or more modules, each of which may include a series of instruction operations in the electronic device. Still further, central processor 910 may be configured to communicate with storage medium 920 to execute a series of instruction operations in storage medium 920 on electronic device 900. The electronic device 900 may also include one or more power supplies 960, one or more wired or wireless network interfaces 950, one or more input-output interfaces 940, and/or one or more operating systems 921, such as a Windows Server TM ，Mac OS X TM ，Unix TM ,Linux TM ，FreeBSD TM And so on.
The Processor 910 may be an integrated circuit chip having Signal processing capabilities, such as a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like, wherein the general purpose Processor may be a microprocessor or any conventional Processor, or the like.
The input/output interface 940 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the electronic device 900. In one example, the input/output Interface 940 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the input/output interface 940 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The operating system 921 may include system programs, such as a framework layer, a core library layer, a driver layer, etc., for processing various basic system services and performing hardware-related tasks, for implementing various basic services and for processing hardware-based tasks.
It will be understood by those skilled in the art that the structure shown in fig. 9 is only an illustration and is not intended to limit the structure of the electronic device. For example, electronic device 900 may also include more or fewer components than shown in FIG. 9, or have a different configuration than that shown in FIG. 9.
Embodiments of the present application further provide a computer-readable storage medium, where the storage medium may be disposed in an electronic device to store at least one instruction or at least one program for implementing a driving data processing method in the method embodiments, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the driving data processing method provided in the method embodiments.
Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, which can store program codes.
Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method provided in the various alternative implementations described above.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And that specific embodiments have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the system embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (10)

1. A driving data processing method is applied to a vehicle-mounted terminal and is characterized by comprising the following steps:
controlling the target vehicle to automatically drive based on a driving control algorithm;
storing automatic driving data generated by the target vehicle in an automatic driving process;
performing anomaly detection on the automatic driving data based on a monitoring algorithm;
under the condition that abnormal driving data corresponding to a target abnormal event exist in the automatic driving data, sending the abnormal driving data corresponding to the target abnormal event to a cloud server;
receiving algorithm updating data sent by the cloud server, wherein the algorithm updating data is obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data;
and updating the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
2. The driving data processing method according to claim 1, wherein after the abnormal driving data corresponding to a target abnormal event is sent to a cloud server when it is detected that abnormal driving data corresponding to the target abnormal event exists in the automatic driving data, the method further comprises:
and deleting the abnormal driving data under the condition that the abnormal driving data is successfully transmitted.
3. The driving data processing method according to claim 1, wherein the abnormality detecting the automatic driving data based on the monitoring algorithm includes:
screening abnormal data of the automatic driving data based on the monitoring algorithm to obtain initial abnormal data;
determining an abnormal event corresponding to the initial abnormal data based on a preset corresponding relation, wherein the preset corresponding relation represents the corresponding relation between the abnormal data and the abnormal event;
determining an abnormality tolerance corresponding to the initial abnormal data, wherein the abnormality tolerance characterizes the abnormal level of the abnormal data;
and determining the initial abnormal data as the abnormal driving data corresponding to the target abnormal event when the abnormal tolerance is larger than the tolerance threshold corresponding to the abnormal event.
4. The driving data processing method according to claim 3, wherein the determining of the abnormality tolerance corresponding to the initial abnormality data includes:
determining a target event type of an abnormal event corresponding to the initial abnormal data;
and calculating the tolerance of the initial abnormal data based on a tolerance calculation algorithm corresponding to the target event category to obtain the abnormal tolerance.
5. The driving data processing method according to claim 3, characterized in that the method further comprises:
and sending abnormal alarm information to the cloud server under the condition that the abnormal tolerance is greater than an alarm threshold, wherein the alarm threshold is greater than a tolerance threshold corresponding to the abnormal event, and the abnormal alarm information is used for indicating the cloud server to send the abnormal driving data corresponding to the target abnormal event to a remote processing end.
6. The driving data processing method according to claim 1, characterized by further comprising:
and sending the identification code of the target vehicle to the cloud server, wherein the identification code represents the identity information of the target vehicle.
7. The driving data processing method according to claim 1, characterized by further comprising:
storing reference driving data of the target vehicle in the non-automatic driving process of the target vehicle, wherein the reference driving data are data generated by performing preset reference operation or under preset driving conditions in the non-automatic driving process of the target vehicle;
transmitting the reference driving data to the cloud server.
8. A driving data processing method is applied to a cloud server and is characterized by comprising the following steps:
receiving abnormal driving data corresponding to a target abnormal event, wherein the abnormal driving data corresponding to the target abnormal event is sent by a vehicle-mounted terminal under the condition that the abnormal driving data corresponding to the target abnormal event exists in the automatic driving data, and the automatic driving data is generated and stored in the process that the vehicle-mounted terminal controls the automatic driving of a target vehicle based on a driving control algorithm;
acquiring algorithm updating data, wherein the algorithm updating data is obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data;
and sending the algorithm updating data to the vehicle-mounted terminal so that the vehicle-mounted terminal updates the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
9. A driving data processing apparatus, characterized in that the apparatus comprises:
a control module: for controlling the target vehicle to autonomously drive based on a driving control algorithm;
a first storage module: for storing autonomous driving data generated by the target vehicle during autonomous driving;
a detection module: for anomaly detection of the autopilot data based on a monitoring algorithm;
a first transmitting module: the automatic driving data processing system is used for sending abnormal driving data corresponding to a target abnormal event to a cloud server under the condition that the abnormal driving data corresponding to the target abnormal event exist in the automatic driving data;
a first receiving module: the driving control algorithm updating system is used for receiving algorithm updating data sent by the cloud server, and the algorithm updating data is obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data;
a first update module: and the driving control algorithm is updated based on the algorithm updating data to obtain an updated driving control algorithm.
10. A driving data processing apparatus, characterized in that the apparatus comprises:
a second receiving module: the system comprises a monitoring algorithm, a cloud server and a vehicle-mounted terminal, wherein the monitoring algorithm is used for detecting the abnormal driving data corresponding to a target abnormal event, and the abnormal driving data corresponding to the target abnormal event is obtained by sending the abnormal driving data corresponding to the target abnormal event to the cloud server under the condition that the abnormal driving data corresponding to the target abnormal event exists in the automatic driving data;
an acquisition module: the method comprises the steps of obtaining algorithm updating data, wherein the algorithm updating data are obtained by performing algorithm optimization on the driving control algorithm based on the abnormal driving data;
a second update module: and the algorithm updating data is sent to the vehicle-mounted terminal so that the vehicle-mounted terminal updates the driving control algorithm based on the algorithm updating data to obtain an updated driving control algorithm.
CN202210876758.6A 2022-07-25 2022-07-25 Driving data processing method and device and storage medium Pending CN115497194A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117002538A (en) * 2023-10-07 2023-11-07 格陆博科技有限公司 Automatic driving control system based on deep learning algorithm

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117002538A (en) * 2023-10-07 2023-11-07 格陆博科技有限公司 Automatic driving control system based on deep learning algorithm
CN117002538B (en) * 2023-10-07 2024-05-07 格陆博科技有限公司 Automatic driving control system based on deep learning algorithm

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