CN117208020A - Dangerous behavior assessment method and system for automatic driving vehicle - Google Patents
Dangerous behavior assessment method and system for automatic driving vehicle Download PDFInfo
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Abstract
The application relates to the technical field of automatic driving, and particularly discloses a dangerous behavior assessment method and system for an automatic driving vehicle. According to the application, through carrying out automatic driving environment monitoring, environment monitoring data are obtained, and a target driving lane is determined; performing hazard early warning analysis on the environment monitoring data, and judging whether the environment monitoring data has a hazard early warning state or not; when the dangerous early warning state exists, environment linkage broadcasting is carried out; and (3) integrating the environment monitoring data and the plurality of linkage environment data, performing risk assessment on the target driving lane, and performing risk intervention treatment according to a risk assessment result. The method comprises the steps of acquiring environment monitoring data, determining a target driving lane, judging a dangerous early warning state, carrying out environment linkage broadcasting when the dangerous early warning state exists, receiving a plurality of linkage environment data, carrying out comprehensive dangerous assessment on the target driving lane, further carrying out corresponding dangerous intervention treatment, realizing dangerous behavior assessment in a larger range, and improving the safety of automatic driving.
Description
Technical Field
The application belongs to the technical field of automatic driving, and particularly relates to a dangerous behavior assessment method and system for an automatic driving vehicle.
Background
An automatic driving vehicle, also called an unmanned vehicle, a computer driving vehicle or a wheeled mobile robot, is an intelligent automobile which realizes unmanned through a computer system. The automatic driving technology of automobile is to know the surrounding traffic condition mainly through video camera, radar sensor and laser range finder and to constitute one detailed map for navigation of road in front.
In the automatic driving process of the vehicle, real-time dangerous behavior identification and evaluation are required to be carried out, so that corresponding driving intervention is carried out, and dangerous accidents are avoided. In the prior art, dangerous behavior evaluation of an automatic driving vehicle can only be monitored and analyzed according to a video camera, a radar sensor and a laser range finder of the vehicle, so that the monitoring range is too limited, dangerous behavior evaluation can not be realized in a larger range, and dangerous behavior recognition, evaluation and intervention can not be performed on a shielded area, so that potential safety hazards exist in the automatic driving vehicle.
Disclosure of Invention
The embodiment of the application aims to provide a dangerous behavior assessment method and system for an automatic driving vehicle, and aims to solve the problems in the background technology.
In order to achieve the above object, the embodiment of the present application provides the following technical solutions:
a method of assessing dangerous behavior of an autonomous vehicle, the method comprising the steps of:
performing automatic driving environment monitoring, acquiring environment monitoring data, performing lane recognition analysis on the environment monitoring data, and determining a target driving lane;
performing hazard early warning analysis on the environment monitoring data, and judging whether the environment monitoring data has a hazard early warning state or not;
when the dangerous early warning state exists, environment linkage broadcasting is carried out, and a plurality of linkage environment data are received;
and integrating the environment monitoring data and the plurality of linkage environment data, performing risk assessment on the target driving lane, and performing risk intervention processing according to a risk assessment result.
As a further limitation of the technical solution of the embodiment of the present application, the performing environmental monitoring of automatic driving, obtaining environmental monitoring data, and performing lane recognition analysis on the environmental monitoring data, and determining a target driving lane specifically includes the following steps:
performing automatic driving state monitoring to obtain state monitoring data;
analyzing the state monitoring data, and generating an environment monitoring instruction when the state monitoring data is in an automatic driving state;
according to the environment monitoring instruction, environment monitoring is carried out, and environment monitoring data are obtained;
and carrying out lane recognition analysis on the environment monitoring data to determine a target driving lane.
As a further limitation of the technical solution of the embodiment of the present application, the performing risk early warning analysis on the environmental monitoring data, and determining whether there is a risk early warning state specifically includes the following steps:
performing direct danger early warning analysis on the environment monitoring data to obtain a direct early warning result;
carrying out hidden danger early warning analysis on the environment monitoring data to obtain a hidden early warning result;
and carrying out comprehensive early warning analysis on the direct early warning result and the hidden early warning result to judge whether a dangerous early warning state exists.
As a further limitation of the technical solution of the embodiment of the present application, when the dangerous early warning state is provided, the environment linkage broadcasting is performed, and the receiving of the plurality of linkage environment data specifically includes the following steps:
generating a linkage instruction when the dangerous early warning state exists;
generating and broadcasting an environment linkage request according to the linkage instruction;
a plurality of linked environmental data is received.
As a further limitation of the technical solution of the embodiment of the present application, the generating and broadcasting the environment linkage request according to the linkage instruction specifically includes the following steps:
according to the linkage instruction, performing target analysis on the environment monitoring data to determine a plurality of environment linkage targets;
calculating target linkage distances of a plurality of environment linkage targets;
constructing a target linkage range according to a plurality of target linkage distances;
and generating and broadcasting an environment linkage request according to the target linkage range.
As a further limitation of the technical solution of the embodiment of the present application, the synthesizing the environmental monitoring data and the plurality of linkage environmental data, performing risk assessment on the target driving lane, and performing risk intervention processing according to a risk assessment result specifically includes the following steps:
integrating the environment monitoring data and the plurality of linkage environment data, and performing risk assessment on the target driving lane to obtain a risk assessment result;
judging whether dangerous intervention requirements exist according to the dangerous evaluation result;
when dangerous intervention demands exist, dangerous intervention measures are analyzed and determined, and corresponding dangerous intervention instructions are generated;
and performing active control of dangerous intervention according to the dangerous intervention instruction.
A dangerous behavior assessment system for an autonomous vehicle, the system comprising an environmental monitoring analysis module, a dangerous early warning analysis module, an environmental linkage broadcasting module and a dangerous assessment intervention module, wherein:
the environment monitoring analysis module is used for carrying out environment monitoring of automatic driving, acquiring environment monitoring data, carrying out lane recognition analysis on the environment monitoring data and determining a target driving lane;
the danger early warning analysis module is used for carrying out danger early warning analysis on the environment monitoring data and judging whether the environment monitoring data has a danger early warning state or not;
the environment linkage broadcasting module is used for carrying out environment linkage broadcasting and receiving a plurality of linkage environment data when the dangerous early warning state exists;
and the risk assessment intervention module is used for integrating the environment monitoring data and the plurality of linkage environment data, carrying out risk assessment on the target driving lane and carrying out risk intervention processing according to a risk assessment result.
As a further limitation of the technical solution of the embodiment of the present application, the environmental monitoring analysis module specifically includes:
the state monitoring unit is used for monitoring the automatic driving state and acquiring state monitoring data;
the state analysis unit is used for analyzing the state monitoring data and generating an environment monitoring instruction when the state is in an automatic driving state;
the environment monitoring unit is used for performing environment monitoring according to the environment monitoring instruction to acquire environment monitoring data;
and the lane recognition unit is used for carrying out lane recognition analysis on the environment monitoring data and determining a target driving lane.
As a further limitation of the technical solution of the embodiment of the present application, the hazard early warning analysis module specifically includes:
the direct early warning analysis unit is used for carrying out direct dangerous early warning analysis on the environment monitoring data to obtain a direct early warning result;
the hidden early warning analysis unit is used for carrying out hidden danger early warning analysis on the environment monitoring data to obtain a hidden early warning result;
and the comprehensive early warning analysis unit is used for carrying out comprehensive early warning analysis on the direct early warning result and the hidden early warning result and judging whether the dangerous early warning state exists or not.
As a further limitation of the technical solution of the embodiment of the present application, the risk assessment intervention module specifically includes:
the risk assessment unit is used for integrating the environment monitoring data and the plurality of linkage environment data, and carrying out risk assessment on the target driving lane to obtain a risk assessment result;
the requirement judging unit is used for judging whether dangerous intervention requirements exist or not according to the dangerous evaluation result;
the instruction generation unit is used for analyzing and determining dangerous intervention measures when dangerous intervention demands exist, and generating corresponding dangerous intervention instructions;
and the intervention control unit is used for performing active control of dangerous intervention according to the dangerous intervention instruction.
Compared with the prior art, the application has the beneficial effects that:
according to the embodiment of the application, through carrying out automatic driving environment monitoring, environment monitoring data are obtained, and a target driving lane is determined; performing hazard early warning analysis on the environment monitoring data, and judging whether the environment monitoring data has a hazard early warning state or not; when the dangerous early warning state exists, environment linkage broadcasting is carried out; and (3) integrating the environment monitoring data and the plurality of linkage environment data, performing risk assessment on the target driving lane, and performing risk intervention treatment according to a risk assessment result. The method comprises the steps of acquiring environment monitoring data, determining a target driving lane, judging a dangerous early warning state, carrying out environment linkage broadcasting when the dangerous early warning state exists, receiving a plurality of linkage environment data, carrying out comprehensive dangerous assessment on the target driving lane, further carrying out corresponding dangerous intervention treatment, realizing dangerous behavior assessment in a larger range, and improving the safety of automatic driving.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present application.
Fig. 2 shows a flowchart of automatic driving environment monitoring in the method according to the embodiment of the present application.
Fig. 3 shows a flowchart of risk early warning analysis in the method provided by the embodiment of the application.
Fig. 4 shows a flowchart of performing environment-linked broadcasting in the method provided by the embodiment of the application.
Fig. 5 shows a flowchart of broadcasting an environment linkage request in the method provided by the embodiment of the application.
FIG. 6 shows a flow chart of a risk assessment and intervention process in a method provided by an embodiment of the present application.
Fig. 7 shows an application architecture diagram of a system provided by an embodiment of the present application.
Fig. 8 shows a block diagram of the environment monitoring analysis module in the system according to the embodiment of the present application.
Fig. 9 shows a block diagram of a hazard early warning analysis module in the system according to the embodiment of the present application.
FIG. 10 shows a block diagram of the risk assessment intervention module in the system provided by the embodiment of the application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It can be understood that in the automatic driving process of the vehicle, real-time dangerous behavior identification and evaluation are required to be performed, so that corresponding driving intervention is performed, and dangerous accidents are avoided. In the prior art, dangerous behavior evaluation of an automatic driving vehicle can only be monitored and analyzed according to a video camera, a radar sensor and a laser range finder of the vehicle, so that the monitoring range is too limited, dangerous behavior evaluation can not be realized in a larger range, and dangerous behavior recognition, evaluation and intervention can not be performed on a shielded area, so that potential safety hazards exist in the automatic driving vehicle.
In order to solve the problems, the embodiment of the application acquires environment monitoring data through the environment monitoring of automatic driving, carries out lane recognition analysis and determines a target driving lane; performing hazard early warning analysis on the environment monitoring data, and judging whether the environment monitoring data has a hazard early warning state or not; when the dangerous early warning state exists, environment linkage broadcasting is carried out; and (3) integrating the environment monitoring data and the plurality of linkage environment data, performing risk assessment on the target driving lane, and performing risk intervention treatment according to a risk assessment result. The method comprises the steps of acquiring environment monitoring data, determining a target driving lane, judging a dangerous early warning state, carrying out environment linkage broadcasting when the dangerous early warning state exists, receiving a plurality of linkage environment data, carrying out comprehensive dangerous assessment on the target driving lane, further carrying out corresponding dangerous intervention treatment, realizing dangerous behavior assessment in a larger range, and improving the safety of automatic driving.
Fig. 1 shows a flowchart of a method provided by an embodiment of the present application.
In particular, in a preferred embodiment provided by the present application, a dangerous behavior assessment method for an autonomous vehicle, the method includes the steps of:
step S101, performing automatic driving environment monitoring, acquiring environment monitoring data, performing lane recognition analysis on the environment monitoring data, and determining a target driving lane.
In the embodiment of the application, after the vehicle is started, automatic driving state monitoring is carried out, state monitoring data are obtained, whether the vehicle is in an automatic driving state is judged by analyzing the state monitoring data, when the vehicle is judged to be in the automatic driving state, an environment monitoring instruction is generated, monitoring shooting of the external environment of the vehicle is carried out according to the environment monitoring instruction at the moment, the environment monitoring data are obtained, and then lane recognition analysis is carried out on the environment monitoring data to determine a target driving lane, wherein the target driving lane can be a lane where the vehicle is currently driven or a partition lane where the vehicle is to be driven.
Specifically, fig. 2 shows a flowchart of automatic driving environment monitoring in the method provided by the embodiment of the application.
In a preferred embodiment of the present application, the automatic driving environment monitoring, obtaining environment monitoring data, and performing lane recognition analysis on the environment monitoring data, to determine a target driving lane specifically includes the following steps:
step S1011, performing automatic driving state monitoring to obtain state monitoring data;
step S1012, analyzing the state monitoring data, and generating an environment monitoring instruction when the state monitoring data is in an automatic driving state;
step S1013, according to the environment monitoring instruction, performing environment monitoring to obtain environment monitoring data;
and step S1014, carrying out lane recognition analysis on the environment monitoring data to determine a target driving lane.
Further, the dangerous behavior evaluation method of the automatic driving vehicle further comprises the following steps:
and step S102, performing hazard early warning analysis on the environment monitoring data to judge whether the hazard early warning state exists or not.
In the embodiment of the application, whether the environment monitoring data has direct running dangerous behavior (such as lane changing, overtaking and the like of other vehicles) is identified by carrying out direct dangerous early warning analysis on the environment monitoring data to obtain a direct early warning result, then carrying out hidden dangerous early warning analysis on the environment monitoring data to identify whether the environment monitoring data has hidden running dangerous situation (such as large vehicle shielding, dangerous crossing and the like), and then carrying out early warning analysis by combining the direct early warning result and the hidden early warning result to judge whether the environment monitoring data has dangerous early warning state, particularly, only when the environment monitoring data does not have direct running dangerous behavior and does not have hidden running dangerous situation, judging that the environment monitoring data does not have dangerous early warning state; and when the vehicle has direct running dangerous behavior and/or hidden running dangerous situations, judging that the vehicle has a dangerous early warning state.
Specifically, fig. 3 shows a flowchart of risk early warning analysis in the method provided by the embodiment of the application.
In a preferred embodiment of the present application, the performing risk early warning analysis on the environmental monitoring data, determining whether a risk early warning state exists, specifically includes the following steps:
step S1021, performing direct danger early warning analysis on the environment monitoring data to obtain a direct early warning result;
step S1022, carrying out hidden danger early warning analysis on the environment monitoring data to obtain a hidden early warning result;
step S1023, carrying out comprehensive early warning analysis on the direct early warning result and the hidden early warning result, and judging whether a dangerous early warning state exists or not.
Further, the dangerous behavior evaluation method of the automatic driving vehicle further comprises the following steps:
and step S103, when the dangerous early warning state exists, environment linkage broadcasting is carried out, and a plurality of linkage environment data are received.
In the embodiment of the application, when a dangerous early warning state exists, a linkage instruction is generated, then target analysis is carried out on environment monitoring data according to the linkage instruction, a plurality of environment linkage targets (the environment linkage targets can be other vehicles or traffic equipment) around an automatic driving vehicle are determined, the target linkage distances of the environment linkage targets are calculated, the plurality of target linkage distances are compared, the longest distance is identified, the longest distance is set as a range radius, a target linkage range is constructed according to the range radius, the target linkage range is matched with request power, an environment linkage request is generated and broadcast, and then the environment linkage request is received and the linkage environment data fed back by the environment linkage requests are received after the environment linkage targets receive the environment linkage request and feed back the environment linkage request, so that the environment linkage data are obtained.
Specifically, fig. 4 shows a flowchart of performing environment linkage broadcasting in the method provided by the embodiment of the present application.
In a preferred embodiment of the present application, when the dangerous early warning state is provided, the environment linkage broadcasting is performed, and the receiving of the plurality of linkage environment data specifically includes the following steps:
step S1031, when a dangerous early warning state exists, generating a linkage instruction;
step S1032, generating and broadcasting an environment linkage request according to the linkage instruction;
step S1033, a plurality of linkage environment data is received.
Fig. 5 shows a flowchart of broadcasting an environment linkage request in the method provided by the embodiment of the application.
Specifically, in the preferred embodiment provided by the present application, the generating and broadcasting the environment linkage request according to the linkage instruction specifically includes the following steps:
step S10321, performing target analysis on the environmental monitoring data according to the linkage instruction, and determining a plurality of environmental linkage targets;
step S10322, calculating target linkage distances of a plurality of environment linkage targets;
step S10323, constructing a target linkage range according to a plurality of target linkage distances;
and step S10324, generating and broadcasting an environment linkage request according to the target linkage range.
Further, the dangerous behavior evaluation method of the automatic driving vehicle further comprises the following steps:
and step S104, integrating the environment monitoring data and the plurality of linkage environment data, performing risk assessment on the target driving lane, and performing risk intervention processing according to a risk assessment result.
In the embodiment of the application, the environment monitoring data and the plurality of linkage environment data are synthesized, the risk assessment is carried out on the target driving lane to obtain a risk assessment result, then whether the risk intervention requirement exists is judged according to the risk assessment result, when the risk intervention requirement exists is judged, the analysis of the risk intervention measures is carried out, the corresponding risk intervention instruction is generated, and further the active control of the risk intervention is carried out according to the risk intervention instruction, so that the early intervention of the risk situation is realized, and the safety of automatic driving is improved.
Specifically, fig. 6 shows a flowchart of risk assessment and intervention processing in the method according to the embodiment of the present application.
In a preferred embodiment of the present application, the synthesizing the environmental monitoring data and the plurality of linkage environmental data, performing risk assessment on the target driving lane, and performing risk intervention processing according to a risk assessment result specifically includes the following steps:
step S1041, integrating the environment monitoring data and a plurality of linkage environment data, and performing risk assessment on the target driving lane to obtain a risk assessment result;
step S1042, judging whether dangerous intervention requirements exist according to the dangerous evaluation result;
step S1043, when there is a dangerous intervention requirement, analyzing and determining dangerous intervention measures, and generating a corresponding dangerous intervention instruction;
step S1044, performing active control of the dangerous intervention according to the dangerous intervention command.
Further, fig. 7 shows an application architecture diagram of the system provided by the embodiment of the present application.
In another preferred embodiment of the present application, a dangerous behavior assessment system for an automatic driving vehicle includes:
the environment monitoring analysis module 101 is configured to perform environment monitoring of automatic driving, acquire environment monitoring data, perform lane recognition analysis on the environment monitoring data, and determine a target driving lane.
In the embodiment of the application, after the vehicle is started, the environment monitoring analysis module 101 monitors the automatic driving state, acquires state monitoring data, analyzes the state monitoring data to determine whether the vehicle is in the automatic driving state, generates an environment monitoring instruction when the vehicle is determined to be in the automatic driving state, monitors and shoots the external environment of the vehicle according to the environment monitoring instruction, acquires the environment monitoring data, and carries out lane recognition analysis on the environment monitoring data to determine a target driving lane, wherein the target driving lane can be a lane where the vehicle is currently driven, or can be a lane of a partition wall where the vehicle is to be driven in a lane change manner.
Specifically, fig. 8 shows a block diagram of the environment monitoring analysis module 101 in the system according to the embodiment of the present application.
In a preferred embodiment of the present application, the environmental monitoring analysis module 101 specifically includes:
a state monitoring unit 1011 for performing automatic driving state monitoring to acquire state monitoring data;
a state analysis unit 1012, configured to analyze the state monitoring data, and generate an environment monitoring instruction when the state is in an autopilot state;
an environment monitoring unit 1013 configured to perform environment monitoring according to the environment monitoring instruction, and obtain environment monitoring data;
and a lane recognition unit 1014 for performing lane recognition analysis on the environmental monitoring data to determine a target driving lane.
Further, the dangerous behavior evaluation system of the automatic driving vehicle further comprises:
and the danger early warning analysis module 102 is used for carrying out danger early warning analysis on the environment monitoring data and judging whether the environment monitoring data has a danger early warning state or not.
In the embodiment of the application, the hazard early warning analysis module 102 is used for identifying whether the environment monitoring data has direct running hazard behaviors (such as lane changing, overtaking and the like of other vehicles) to obtain a direct early warning result, then carrying out hidden hazard early warning analysis on the environment monitoring data to identify whether the environment monitoring data has hidden running hazard conditions (such as shielding of a large vehicle, having a hazard intersection and the like), and then carrying out early warning analysis by combining the direct early warning results and the hidden early warning results to judge whether the environment monitoring data has a hazard early warning state, specifically, only when the environment monitoring data does not have direct running hazard behaviors and does not have hidden running hazard conditions, judging that the environment monitoring data does not have the hazard early warning state; and when the vehicle has direct running dangerous behavior and/or hidden running dangerous situations, judging that the vehicle has a dangerous early warning state.
Specifically, fig. 9 shows a block diagram of the risk early warning analysis module 102 in the system according to the embodiment of the present application.
In a preferred embodiment of the present application, the hazard early warning analysis module 102 specifically includes:
the direct early warning analysis unit 1021 is used for carrying out direct dangerous early warning analysis on the environment monitoring data to obtain a direct early warning result;
the hidden warning analysis unit 1022 is configured to perform hidden danger warning analysis on the environmental monitoring data, so as to obtain a hidden warning result;
and the comprehensive early warning analysis unit 1023 is used for carrying out comprehensive early warning analysis on the direct early warning result and the hidden early warning result and judging whether the dangerous early warning state exists or not.
Further, the dangerous behavior evaluation system of the automatic driving vehicle further comprises:
and the environment linkage broadcasting module 103 is used for carrying out environment linkage broadcasting and receiving a plurality of linkage environment data when the dangerous early warning state exists.
In the embodiment of the application, when the dangerous early warning state exists, the environment linkage broadcasting module 103 generates a linkage instruction, then performs target analysis on environment monitoring data according to the linkage instruction, determines a plurality of environment linkage targets (environment linkage targets, other vehicles can be or traffic equipment) around the automatic driving vehicle, calculates target linkage distances of the plurality of environment linkage targets, compares the plurality of target linkage distances, identifies the longest distance, sets the longest distance as a range radius, constructs a target linkage range according to the range radius, matches request power with the target linkage range, generates and broadcasts environment linkage requests, and further receives the linkage environment data fed back by the plurality of environment linkage requests after the plurality of environment linkage targets receive the environment linkage requests and feed back the environment linkage requests, thereby obtaining a plurality of linkage environment data.
And the risk assessment intervention module 104 is configured to integrate the environmental monitoring data and the plurality of linkage environmental data, perform risk assessment on the target driving lane, and perform risk intervention processing according to a risk assessment result.
In the embodiment of the application, the risk assessment intervention module 104 integrates environment monitoring data and a plurality of linkage environment data, carries out risk assessment on a target driving lane to obtain a risk assessment result, judges whether the risk intervention requirement exists according to the risk assessment result, and when judging that the risk intervention requirement exists, the risk assessment intervention module 104 carries out analysis of risk intervention measures to generate corresponding risk intervention instructions, and then carries out active control of risk intervention according to the risk intervention instructions, thereby realizing early intervention of dangerous situations and improving the safety of automatic driving.
Specifically, fig. 10 shows a block diagram of the risk assessment intervention module 104 in the system according to the embodiment of the present application.
In a preferred embodiment provided by the present application, the risk assessment intervention module 104 specifically includes:
the risk assessment unit 1041 is configured to integrate the environmental monitoring data and the plurality of linkage environmental data, and perform risk assessment on the target driving lane to obtain a risk assessment result;
a requirement judging unit 1042, configured to judge whether there is a requirement for dangerous intervention according to the risk assessment result;
the instruction generating unit 1043 is configured to analyze and determine dangerous intervention measures when there is a dangerous intervention requirement, and generate a corresponding dangerous intervention instruction;
and an intervention control unit 1044, configured to perform active control of the dangerous intervention according to the dangerous intervention instruction.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (10)
1. A method of assessing dangerous behavior of an autonomous vehicle, the method comprising the steps of:
performing automatic driving environment monitoring, acquiring environment monitoring data, performing lane recognition analysis on the environment monitoring data, and determining a target driving lane;
performing hazard early warning analysis on the environment monitoring data, and judging whether the environment monitoring data has a hazard early warning state or not;
when the dangerous early warning state exists, environment linkage broadcasting is carried out, and a plurality of linkage environment data are received;
and integrating the environment monitoring data and the plurality of linkage environment data, performing risk assessment on the target driving lane, and performing risk intervention processing according to a risk assessment result.
2. The method for evaluating dangerous behavior of an automatically driven vehicle according to claim 1, wherein the performing environmental monitoring of the automatically driven vehicle, acquiring environmental monitoring data, and performing lane recognition analysis on the environmental monitoring data, determining a target driving lane specifically comprises the steps of:
performing automatic driving state monitoring to obtain state monitoring data;
analyzing the state monitoring data, and generating an environment monitoring instruction when the state monitoring data is in an automatic driving state;
according to the environment monitoring instruction, environment monitoring is carried out, and environment monitoring data are obtained;
and carrying out lane recognition analysis on the environment monitoring data to determine a target driving lane.
3. The method for evaluating dangerous behavior of an automatically driven vehicle according to claim 1, wherein the step of performing dangerous early warning analysis on the environmental monitoring data to determine whether there is a dangerous early warning state comprises the steps of:
performing direct danger early warning analysis on the environment monitoring data to obtain a direct early warning result;
carrying out hidden danger early warning analysis on the environment monitoring data to obtain a hidden early warning result;
and carrying out comprehensive early warning analysis on the direct early warning result and the hidden early warning result to judge whether a dangerous early warning state exists.
4. The method for evaluating dangerous behavior of an autonomous vehicle according to claim 1, wherein said performing an environment linked broadcast when having a dangerous pre-warning state, receiving a plurality of linked environment data specifically comprises the steps of:
generating a linkage instruction when the dangerous early warning state exists;
generating and broadcasting an environment linkage request according to the linkage instruction;
a plurality of linked environmental data is received.
5. The method for evaluating the dangerous behavior of the automatic driving vehicle according to claim 4, wherein the generating and broadcasting the environment linkage request according to the linkage instruction specifically comprises the following steps:
according to the linkage instruction, performing target analysis on the environment monitoring data to determine a plurality of environment linkage targets;
calculating target linkage distances of a plurality of environment linkage targets;
constructing a target linkage range according to a plurality of target linkage distances;
and generating and broadcasting an environment linkage request according to the target linkage range.
6. The method for evaluating the dangerous behavior of an automatically driven vehicle according to claim 1, wherein the step of integrating the environmental monitoring data and the plurality of linked environmental data to perform the dangerous evaluation on the target driving lane, and performing the dangerous intervention based on the dangerous evaluation result, specifically comprises the steps of:
integrating the environment monitoring data and the plurality of linkage environment data, and performing risk assessment on the target driving lane to obtain a risk assessment result;
judging whether dangerous intervention requirements exist according to the dangerous evaluation result;
when dangerous intervention demands exist, dangerous intervention measures are analyzed and determined, and corresponding dangerous intervention instructions are generated;
and performing active control of dangerous intervention according to the dangerous intervention instruction.
7. The system for evaluating the dangerous behavior of the automatic driving vehicle is characterized by comprising an environment monitoring analysis module, a dangerous early warning analysis module, an environment linkage broadcasting module and a dangerous evaluating intervention module, wherein:
the environment monitoring analysis module is used for carrying out environment monitoring of automatic driving, acquiring environment monitoring data, carrying out lane recognition analysis on the environment monitoring data and determining a target driving lane;
the danger early warning analysis module is used for carrying out danger early warning analysis on the environment monitoring data and judging whether the environment monitoring data has a danger early warning state or not;
the environment linkage broadcasting module is used for carrying out environment linkage broadcasting and receiving a plurality of linkage environment data when the dangerous early warning state exists;
and the risk assessment intervention module is used for integrating the environment monitoring data and the plurality of linkage environment data, carrying out risk assessment on the target driving lane and carrying out risk intervention processing according to a risk assessment result.
8. The system for assessing the dangerous behavior of an autonomous vehicle of claim 7, wherein said environmental monitoring analysis module includes:
the state monitoring unit is used for monitoring the automatic driving state and acquiring state monitoring data;
the state analysis unit is used for analyzing the state monitoring data and generating an environment monitoring instruction when the state is in an automatic driving state;
the environment monitoring unit is used for performing environment monitoring according to the environment monitoring instruction to acquire environment monitoring data;
and the lane recognition unit is used for carrying out lane recognition analysis on the environment monitoring data and determining a target driving lane.
9. The system for assessing the dangerous behavior of an autonomous vehicle of claim 7, wherein said dangerous early warning analysis module comprises:
the direct early warning analysis unit is used for carrying out direct dangerous early warning analysis on the environment monitoring data to obtain a direct early warning result;
the hidden early warning analysis unit is used for carrying out hidden danger early warning analysis on the environment monitoring data to obtain a hidden early warning result;
and the comprehensive early warning analysis unit is used for carrying out comprehensive early warning analysis on the direct early warning result and the hidden early warning result and judging whether the dangerous early warning state exists or not.
10. The system for assessing the dangerous behavior of an autonomous vehicle of claim 7, wherein said risk assessment intervention module comprises:
the risk assessment unit is used for integrating the environment monitoring data and the plurality of linkage environment data, and carrying out risk assessment on the target driving lane to obtain a risk assessment result;
the requirement judging unit is used for judging whether dangerous intervention requirements exist or not according to the dangerous evaluation result;
the instruction generation unit is used for analyzing and determining dangerous intervention measures when dangerous intervention demands exist, and generating corresponding dangerous intervention instructions;
and the intervention control unit is used for performing active control of dangerous intervention according to the dangerous intervention instruction.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101625797A (en) * | 2009-08-05 | 2010-01-13 | 中国人民解放军国防科学技术大学 | Early warning method when automobile closes at high speed and early warning device |
| CN106553655A (en) * | 2016-12-02 | 2017-04-05 | 深圳地平线机器人科技有限公司 | Hazardous vehicles detection method and system and the vehicle including the system |
| CN111383480A (en) * | 2018-12-28 | 2020-07-07 | 北京百度网讯科技有限公司 | Method, apparatus, device and medium for hazard warning of vehicles |
| CN113386786A (en) * | 2021-07-29 | 2021-09-14 | 阿波罗智联(北京)科技有限公司 | Information prompting method, device, equipment, medium, cloud control platform and vehicle |
| CN115394079A (en) * | 2022-08-23 | 2022-11-25 | 湖南汽车工程职业学院 | Automatic driving danger prediction system based on artificial intelligence |
-
2023
- 2023-11-09 CN CN202311481426.9A patent/CN117208020B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101625797A (en) * | 2009-08-05 | 2010-01-13 | 中国人民解放军国防科学技术大学 | Early warning method when automobile closes at high speed and early warning device |
| CN106553655A (en) * | 2016-12-02 | 2017-04-05 | 深圳地平线机器人科技有限公司 | Hazardous vehicles detection method and system and the vehicle including the system |
| CN111383480A (en) * | 2018-12-28 | 2020-07-07 | 北京百度网讯科技有限公司 | Method, apparatus, device and medium for hazard warning of vehicles |
| CN113386786A (en) * | 2021-07-29 | 2021-09-14 | 阿波罗智联(北京)科技有限公司 | Information prompting method, device, equipment, medium, cloud control platform and vehicle |
| CN115394079A (en) * | 2022-08-23 | 2022-11-25 | 湖南汽车工程职业学院 | Automatic driving danger prediction system based on artificial intelligence |
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