CN115731714A - Road environment sensing method and device - Google Patents

Road environment sensing method and device Download PDF

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Publication number
CN115731714A
CN115731714A CN202211533275.2A CN202211533275A CN115731714A CN 115731714 A CN115731714 A CN 115731714A CN 202211533275 A CN202211533275 A CN 202211533275A CN 115731714 A CN115731714 A CN 115731714A
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road environment
road
weak learning
abnormal
current intersection
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CN115731714B (en
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褚文博
张锐
王年明
胥毅峰
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Western Science City Intelligent Connected Vehicle Innovation Center Chongqing Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/012—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/12—Detection or prevention of fraud
    • H04W12/121—Wireless intrusion detection systems [WIDS]; Wireless intrusion prevention systems [WIPS]
    • H04W12/122—Counter-measures against attacks; Protection against rogue devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

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Abstract

The invention discloses a road environment sensing method and device, which can be used for rapidly identifying the safety condition of a road. The invention relates to the technical field of information, which comprises the following steps: the device acquires road environment data from a V2X message gateway (or other equipment), and then inputs the road environment data into a strong classification learner consisting of a plurality of weak classification learners to identify abnormal road conditions, so as to obtain a plurality of abnormal road condition identification results, namely V2X safety events; and finally, transmitting the road environment sensing result to the current intersection and other intersections. The road environment sensing device can automatically synchronize data with other systems. By applying the technical scheme, the road environment perception capability can be provided for the road without the road side equipment.

Description

Road environment sensing method and device
Technical Field
The invention relates to the technical field of information, in particular to a road environment sensing method and device.
Background
The road environment perception decision is the basic capability of the automatic driving cloud control basic platform, and provides basic support for a V2X communication network.
Currently, perceptual decisions of the road environment are usually implemented at the road side. However, in this way, additional equipment needs to be deployed on the roadside, which is high in construction cost and long in construction period, and cannot provide road environment perception capability for a road without additional equipment, meanwhile, the calculation capability of the roadside equipment is generally limited, which cannot ensure the reliability of the road environment perception result, and the road environment perception result of the current intersection cannot be forwarded to other intersections, so that it is not beneficial for other intersections to know the whole road condition.
Disclosure of Invention
The invention provides a road environment perception method and a road environment perception device, which are mainly characterized in that a road environment perception device is independently deployed at a cloud end, so that road environment perception capability can be provided for a road without road side equipment, reliability of a road environment perception result can be guaranteed, and the road environment perception device is issued to different intersections.
According to a first aspect of the embodiments of the present invention, there is provided a method for processing roaming data of a motor vehicle, applied to a road environment sensing device deployed independently at a cloud end, where the road environment sensing device is configured with a standardized interface, including:
when the target equipment calls the standardized interface to send road environment data of the current intersection to the road environment sensing device, acquiring an internet protocol address of the target equipment;
if the internet protocol address is in a preset trust list, receiving road environment data of the current intersection;
inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers for identifying abnormal road conditions to obtain abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results;
determining a road environment perception result of the current intersection according to the abnormal road condition identification results respectively corresponding to the weak learning classifiers, wherein the road environment perception result comprises a V2X safety event;
the road environment perception result is sent to the current intersection and other intersections;
and if the internet protocol address is not in the preset trust list and the preset attack list, judging whether to receive the road environment data or not according to the danger coefficient and the historical access frequency corresponding to the target equipment.
According to a second aspect of embodiments of the present invention, there is provided a road environment sensing device configured with a standardized interface, including:
the acquisition unit is used for acquiring the internet protocol address of the target equipment when the target equipment calls the standardized interface to send the road environment data of the current intersection to the road environment sensing device;
the receiving unit is used for receiving the road environment data of the current intersection if the internet protocol address is in a preset trust list;
the identification unit is used for inputting the road environment data into a strong learning classifier consisting of a plurality of weak learning classifiers to identify abnormal road conditions, and obtaining abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results;
the determining unit is used for determining a road environment sensing result of the current intersection according to the abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the road environment sensing result comprises a V2X safety event;
the issuing unit is used for issuing the road environment sensing result to the current intersection and other intersections;
and the judging unit is used for judging whether to receive the road environment data according to the danger coefficient and the historical access frequency corresponding to the target equipment if the internet protocol address is not in the preset trust list and the preset attack list.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
when the target equipment calls the standardized interface to send road environment data of the current intersection to the road environment sensing device, acquiring an internet protocol address of the target equipment;
if the internet protocol address is in a preset trust list, receiving the road environment data of the current intersection;
inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers for identifying abnormal road conditions to obtain abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results;
determining a road environment perception result of the current intersection according to the abnormal road condition identification results respectively corresponding to the weak learning classifiers, wherein the road environment perception result comprises a V2X safety event;
the road environment perception result is sent to the current intersection and other intersections;
and if the internet protocol address is not in the preset trust list and the preset attack list, judging whether to accept the road environment data or not according to the danger coefficient and the historical access frequency corresponding to the target equipment.
According to a fourth aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the program:
when the target equipment calls the standardized interface to send road environment data of the current intersection to the road environment sensing device, acquiring an internet protocol address of the target equipment;
if the internet protocol address is in a preset trust list, receiving the road environment data of the current intersection;
inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers for identifying abnormal road conditions to obtain abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results;
determining a road environment perception result of the current intersection according to the abnormal road condition identification results respectively corresponding to the weak learning classifiers, wherein the road environment perception result comprises a V2X safety event;
the road environment perception result is sent to the current intersection and other intersections;
and if the internet protocol address is not in the preset trust list and the preset attack list, judging whether to accept the road environment data or not according to the danger coefficient and the historical access frequency corresponding to the target equipment.
The innovation points of the embodiment of the invention comprise that:
1. the road environment sensing device is independently deployed at the cloud end, and the large-scale and standardized deployment and operation of the automatic driving cloud control basic platform are supported.
2. The embodiment of the invention has the innovation point that the road environment data is acquired by utilizing the standardized interface provided externally, and the road safety perception capability is provided for the road side and the vehicle end through AI analysis and big data analysis.
3. The method for ensuring the system security access is one of the innovation points of the embodiment of the invention by setting the preset trust list, the preset attack list, the symmetric encryption and other modes.
Compared with the road environment sensing decision-making mode at the road side in the prior art, the road environment sensing method and the road environment sensing device can acquire the internet protocol address of the target equipment when the target equipment calls the standardized interface to send the road environment data of the current intersection to the road environment sensing device, if the internet protocol address is in a preset trust list, the road environment data of the current intersection is received, the road environment data is input into a strong learning classifier formed by a plurality of weak learning classifiers to identify abnormal road conditions, abnormal road condition identification results corresponding to the weak learning classifiers are obtained, wherein the weak learning classifiers respectively represent the mapping relation between the road environment data and the abnormal road condition identification results, meanwhile, the road environment sensing result of the current intersection is determined according to the abnormal road condition identification results corresponding to the weak learning classifiers, and finally the road environment sensing result is sent to the current intersection and other intersections, and if the internet protocol address is not in the preset trust list and the preset trust list, whether the attack equipment is required to access the road environment data according to the danger coefficient and the target equipment or not is judged according to the road environment. Therefore, the road environment sensing device is independently deployed at the cloud end, the road safety sensing capability can be provided for the roadside, so that equipment can be prevented from being additionally deployed at the roadside, the construction cost is reduced, the reliability of the road environment sensing result can be guaranteed by the independently deployed road environment sensing device through big data and AI analysis, the road environment sensing result can be forwarded to different intersections, meanwhile, the road environment sensing device is independently deployed at the cloud end, the road environment sensing capability is provided for the roadside and the vehicle end, the large-scale and standardized deployment and operation of the automatic driving cloud control basic platform can be supported, and in addition, the safe access of the system can be guaranteed by setting the preset trust list and the preset attack list.
The above description is only an overview of the technical solutions of the present application, and the present application may be implemented in accordance with the content of the description so as to make the technical means of the present application more clearly understood, and the detailed description of the present application will be given below in order to make the above and other objects, features, and advantages of the present application more clearly understood.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a road environment sensing method provided by an embodiment of the invention;
FIG. 2 is a schematic flow chart of another road environment sensing method provided by the embodiment of the invention;
fig. 3 is a schematic structural diagram of a road environment sensing device provided by an embodiment of the invention;
FIG. 4 is a schematic structural diagram of another road environment sensing device provided by an embodiment of the invention;
fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the embodiments and drawings of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The road environment perception decision-making mode is realized at the roadside, equipment needs to be additionally arranged at the roadside, the construction cost is high, the construction period is long, the road environment perception capability can not be provided for a road without the additionally arranged equipment, meanwhile, the calculation capability of the roadside equipment is generally limited, the reliability of the road environment perception result can not be guaranteed, the road environment perception result of the current intersection can not be forwarded to other intersections, and therefore the whole road condition can not be known by other intersections.
In order to solve the above problem, an embodiment of the present invention provides a road environment sensing method, which is applied to a road environment sensing device independently deployed in a cloud, where the road environment sensing device is configured with a standardized interface, as shown in fig. 1, the method includes:
step 101, when a target device calls the standardized interface to send road environment data of a current intersection to the road environment sensing device, acquiring an internet protocol address of the target device.
The road environment sensing device is independently deployed at the edge cloud of the automatic driving cloud control basic platform, the target device is any device which sends road environment data to the independently deployed road environment sensing device, such as road side devices, message gateways, vehicles and the like, and the road environment data comprises: BSM messages (Basic Safety messages), RSI messages (Road Side Information), SPAT messages (Signal phase and timing messages), MAP messages (MAP messages), radar data, camera data and the like, and BSM messages specifically include speed, steering, braking, double flashing, position and the like, and are mostly used in V2V scenes, namely lane change warning, blind area warning, intersection collision warning and the like; the RSI message is used for reporting and issuing events, integrating RSUs at the road side, issuing platforms, and is mostly used for V2I scenes, namely road construction, speed limit signs, overspeed early warning, bus lane early warning and the like; the SPAT message is used for vehicle speed guidance, green wave pushing scenes and the like, and a roadside RSU integrated annunciator or the annunciator is transmitted to the platform in a UU mode; the MAP message is used for describing an intersection and a lane, and has a corresponding relation with the traffic light of the intersection; the camera data specifically includes video image frames of the intersection.
The embodiment of the invention is mainly suitable for scenes that the road environment sensing device which is independently deployed is used for providing road safety sensing capability for road sides and vehicle ends. The execution main body of the embodiment of the invention is a road environment sensing device independently deployed at the cloud end.
The independently deployed road environment sensing device in the embodiment of the invention is provided with a standardized software and hardware interface, provides a standardized data interface for the outside, and can be used for data integration with other systems. The following describes in detail the standardized interfaces of the road environment sensing device that are independently deployed in the embodiment of the present invention.
In a specific application scenario, when the standardized interface is a user authentication interface, the method includes: receiving a user authentication request sent by a user through the user authentication interface, wherein the user authentication request carries a first user identifier and a first password; and generating an authentication token corresponding to the user according to the first user identifier and the first password, and feeding back the authentication token to the user. The first user identifier may specifically be a user name. The core code for invoking the user authentication interface is as follows:
Figure BDA0003976636910000071
Figure BDA0003976636910000081
wherein, the meaning of the parameters involved in the codes is as follows:
serial number Parameter(s) Meaning of a field Type (B) Whether or not to fill
1 User_name User name string Is that
2 User_pwd User password string Is that
For the embodiment of the invention, in order to ensure the communication safety between the independently deployed road environment sensing device and other product devices, the road environment sensing device generates corresponding token tokens according to user names and passwords configured by other product devices and feeds the token tokens back to other product devices, and the other product devices can communicate with the road environment sensing device according to the token tokens, so that the communication safety between the product devices can be ensured.
In a specific application scenario, when the standardized interface is a message gateway communication configuration interface, the method further includes: receiving a communication configuration request sent by a message gateway by calling the communication configuration interface, wherein the communication configuration request carries a second user identifier and a second password; performing communication configuration with the message gateway based on the second user identification and the second password; and when the communication configuration with the message gateway is successful, feeding back a communication configuration success message to the message gateway. The second user identifier may specifically be a user name. The core code for calling the communication configuration interface of the message gateway is as follows:
Figure BDA0003976636910000082
Figure BDA0003976636910000091
the meaning of the parameters carried by the communication configuration request in the code is as follows:
serial number Parameter(s) Meaning of a field Types of Description of field Whether or not to fill
1 Broker MQ server string tcp://ip:port Is that
2 User_name User name string User of MQBorker Is that
3 User_pwd Cipher code decimal User of MQBorker Is that
The meaning of the parameter related to the return value in the code is as follows:
serial number Parameter(s) Meaning of a field Type (B) Whether or not to fill
1 Code Status code string Is that
2 Msg Message string Whether or not
Therefore, through the message gateway communication configuration interface, the MQ message gateway can be configured, so that the road environment perception device can acquire messages from the MQ message gateway or send messages to the MQ message gateway.
In a specific application scenario, the road environment sensing device can also acquire BSM (base station management) messages, RSI (remote side information) messages, SPAT (space information), radar data, camera data and the like through a standardized interface, and then provides road environment sensing capability for road sides or vehicle ends through big data analysis or AI (analog to digital) analysis. Specifically, the occurring V2X safety events can be identified through big data analysis or AI analysis, and typical V2X safety event events specifically include abnormal road conditions, abnormal vehicle conditions, severe weather, traffic lights, warning signs, automatic driving warnings, and the like. The abnormal road conditions comprise traffic accidents, road congestion, pedestrian recognition, bicycle recognition, animal recognition and the like, the traffic lights comprise red light reminding and green light reminding, the abnormal vehicle conditions comprise vehicle overspeed, vehicle slow running, vehicle stopping, vehicle backward running, large truck recognition and the like, severe weather comprises rain, hail, wind, fog, snow, haze, sand storm and the like, the warning signs comprise sharp turns, continuous downhill, attention falling rocks, attention crosswind, tunnels, attention dangers, road construction, attention ahead vehicle queuing, no passing, road speed limit, toll station forenotice without an ETC lane, toll station forenotice with an ETC lane, service area forenotice and the like, and the automatic driving forewarning comprises forward collision forewarning, intersection collision forewarning, left turn assisting, blind zone forewarning or lane changing forewarning, reverse overtaking forewarning, emergency braking forewarning, abnormal vehicle reminding, vehicle warning, vehicle out of control warning, road danger condition prompting, speed limit forewarning, weak traffic participant collision forewarning, green wave vehicle guiding, vehicle interior signs, vehicle congestion ahead reminding and the emergency vehicle reminding. It should be noted that the V2X security event according to the embodiment of the present invention is not limited to the above-mentioned events, and may include other events.
By carrying out big data analysis or AI analysis on the BSM message, the RSI message, the SPAT message, the radar data and the camera data, the V2X safety event occurring at the current intersection can be determined, and then the road environment perception result of the current intersection can be determined. For big data analysis, flink stream processing can be adopted to realize vehicle reverse running, speed limitation and the like; for AI analysis, the identification of the above-mentioned V2X security incidents may be implemented based on a strong learner and a weak learner, for example, using an ABC Boost (Adaptive Base Class Boost) model to identify abnormal road conditions such as pedestrian identification, bicycle identification, or animal identification, and using the ABC Boost model to identify abnormal road conditions.
It should be noted that, in the embodiment of the present invention, the standardized interfaces related to the road environment sensing device are not limited to the above interfaces, and may also include other types of standardized interfaces.
Meanwhile, the independently deployed road environment sensing device in the embodiment of the invention can also automatically perform data synchronization with other systems, wherein the other systems can be specifically message gateways.
In order to realize the safe access of the system, the embodiment of the invention is provided with a preset trust list and a preset attack list of the road environment sensing device, the preset trust list stores the internet protocol addresses allowed to be accessed, and the preset attack list stores the internet protocol addresses forbidden to be accessed. In order to avoid malicious attack on the road environment sensing device by the access device, when the target device calls the standardized interface to send road environment data to the independently deployed road environment sensing device, the internet protocol address of the target device needs to be acquired, the internet protocol address of the target device is compared with the internet protocol addresses in the preset trust list and the preset attack list, and whether the road environment data is received or not is judged according to the comparison result.
And 102, if the internet protocol address is in a preset trust list, receiving the road environment data of the current intersection.
For the embodiment of the invention, if the internet protocol address of the target equipment is in the preset trust list, the target equipment does not cause malicious attack to the road environment sensing device, so that the road environment sensing device can receive the road environment data sent by the target equipment.
Step 103, inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers to identify abnormal road conditions, and obtaining abnormal road condition identification results corresponding to the weak learning classifiers respectively.
The strong learning classifier may be specifically an ABC Boost model, the weak learning classifiers respectively represent mapping relationships between the road environment data and the abnormal road condition identification result, and the abnormal road condition identification result includes that an abnormal road condition exists at the current intersection and an abnormal road condition does not exist at the current intersection.
For the embodiment of the invention, after acquiring the road environment data of the current intersection, the road environment sensing device can identify the V2X safety event of the current intersection based on the road environment data, specifically can input the road environment data into the ABC Boost model to identify abnormal road conditions, and when identifying that the current intersection has objects such as pedestrians, bicycles or animals, the abnormal road conditions at the current intersection are determined; and when the current intersection is identified to have no objects such as pedestrians, bicycles or animals, determining that the current intersection has no abnormal road conditions. The ABC Boost model is composed of a plurality of weak learning classifiers, so that road environment data of the current intersection can be respectively input into the weak learning classifiers for abnormal road condition identification during specific identification, and road abnormal identification results respectively corresponding to the weak learning classifiers are obtained.
And step 104, determining a road environment perception result of the current intersection according to the abnormal road condition identification results respectively corresponding to the weak learning classifiers.
For the embodiment of the invention, after the abnormal road condition identification results respectively corresponding to the weak learning classifiers are obtained, the road abnormal identification results respectively corresponding to the weak learning classifiers can be synthesized to obtain a final road abnormal identification result, namely a road environment sensing result of the current intersection, wherein the road environment sensing result is an identified V2X safety event, and the V2X safety event specifically comprises abnormal road conditions, abnormal vehicle conditions, severe weather, traffic lights, warning signs, automatic driving early warning and the like. The specific events included in the abnormal road condition, the abnormal vehicle condition, the severe weather, the traffic lights, the warning sign, and the automatic driving warning are completely the same as those described in step 101, and are not described herein again.
And 105, transmitting the road environment sensing result to the current intersection and other intersections.
For the embodiment of the invention, after the road environment sensing device obtains the road environment sensing result of the current intersection through analysis, the road environment sensing device can issue the road environment sensing result to the current intersection and other intersections, thereby providing road environment sensing capability for the road side.
Compared with the mode that the road environment sensing result cannot be forwarded in the prior art, the road environment sensing device in the embodiment of the invention can issue the road environment sensing result to different intersections.
And 106, if the internet protocol address is not in the preset trust list and the preset attack list, judging whether to receive the road environment data according to the danger coefficient and the historical access frequency corresponding to the target equipment.
The danger coefficient of the target equipment is related to the type of the called standardized interface, and the higher the safety level of the called standardized interface is, the higher the corresponding danger coefficient is; conversely, the lower the security level of the invoked standardized interface, the lower its corresponding risk factor.
For the embodiment of the present invention, when the internet protocol address is not in the preset trust list nor in the preset attack list, it is further evaluated whether the access device (target device) has a malicious attack according to the risk coefficient and the historical access frequency corresponding to the access device (target device), and if the access device (target device) has a malicious attack, the road environment data is refused to be accepted, and the internet protocol address of the access device (target device) is added to the preset attack list.
According to the road environment sensing method provided by the embodiment of the invention, the road environment sensing device is independently deployed at the cloud end, so that the road safety sensing capability can be provided for the roadside, the additional equipment deployment at the roadside can be avoided, the construction cost is reduced, the reliability of the road environment sensing result can be ensured by performing big data and AI analysis on the independently deployed road environment sensing device, and the road environment sensing result can be forwarded to different intersections.
Further, as a refinement and an extension of the above embodiment, an embodiment of the present invention provides another road environment sensing method, as shown in fig. 2, the method includes:
step 201, when the target device calls the standardized interface to send road environment data of the current intersection to the road environment sensing device, acquiring an internet protocol address of the target device.
For the embodiment of the present invention, in order to ensure the secure access of the system, when the target device sends the road environment data to the road environment sensing apparatus, the internet protocol address of the target device needs to be obtained, and whether to accept the road environment data sent by the target device is determined according to the internet protocol address, the preset trust list and the preset attack list.
Step 202, if the internet protocol address is in a preset trust list, receiving the road environment data of the current intersection.
For the embodiment of the invention, if the internet protocol address of the target equipment is in the preset trust list, the target equipment does not cause malicious attack to the road environment sensing device, so that the road environment data sent by the target equipment can be received.
Step 203, inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers to identify abnormal road conditions, and obtaining abnormal road condition identification results corresponding to the weak learning classifiers respectively.
The strong learning classifier may be specifically an ABC Boost model, the ABC Boost model includes a plurality of weak learning classifiers, and the weak learning classifiers respectively represent mapping relationships between the road environment data and the abnormal road condition identification result.
In the embodiment of the invention, the essence of the abnormal road condition identification is the identification of a target object, the target object can be specifically foreign matters such as pedestrians, bicycles or animals, and when the target object is identified to exist at the current intersection, the abnormal road condition existing at the current intersection is determined; and when the target object at the current intersection is not identified, determining that the abnormal road condition does not exist at the current intersection. Based on this, 203 specifically includes: inputting the road environment data into the strong learning classifier consisting of a plurality of weak learning classifiers for target object identification; aiming at any weak learning classifier in the weak learning classifiers, if the target object exists at the current intersection, determining that the abnormal road condition identification result corresponding to the weak learning classifier is the abnormal road condition existing at the current intersection; if the target object at the current intersection is not identified by any classifier, determining that the abnormal road condition identification result corresponding to any weak learning classifier is that the abnormal road condition does not exist at the current intersection.
Specifically, any weak learning classifier in the embodiment of the present invention may perform two classifications, and the classification result includes the absence of a target object (a pedestrian, a bicycle, an animal, etc.) and the presence of a target object (a pedestrian, a bicycle, an animal, etc.). When the road environment data are specifically identified, the road environment data can be respectively input into a plurality of weak learning classifiers for identifying the target object, each weak learning classifier can output a first probability value that the target object does not exist at the current intersection and a second probability value that the target object exists at the current intersection, and when the first probability value is greater than the second probability value, the weak learning classifier is determined not to identify that the target object exists at the current intersection, namely, the identification result of the abnormal road condition corresponding to the weak learning classifier is determined that the abnormal road condition does not exist at the current intersection; and when the first probability value is smaller than or equal to the second probability value, determining that the weak learning classifier identifies that the target object exists at the current intersection, namely determining that the abnormal road condition identification result corresponding to the weak learning classifier is that the abnormal road condition exists at the current intersection. Therefore, the abnormal road condition identification result corresponding to each weak learning classifier can be obtained according to the method.
And 204, determining a road environment perception result of the current intersection according to the abnormal road condition identification results respectively corresponding to the weak learning classifiers.
The road environment sensing result comprises a V2X safety event, the road environment sensing result is the identified V2X safety event, and the V2X safety event specifically comprises abnormal road conditions, abnormal vehicle conditions, severe weather, traffic lights, warning signs, automatic driving early warning and the like. The specific events included in the abnormal road condition, the abnormal vehicle condition, the severe weather, the traffic lights, the warning sign, and the automatic driving warning are completely the same as those described in step 101, and are not described herein again.
For the embodiment of the present invention, in order to determine the road environment sensing result of the current intersection, step 204 specifically includes: and according to the weight values respectively corresponding to the weak learning classifiers, synthesizing the abnormal road condition identification results respectively corresponding to the weak learning classifiers to obtain the road environment perception result of the current intersection.
For example, when the abnormal road condition recognition result of the weak learning classifier is that no abnormal road condition exists at the current intersection, the abnormal road condition recognition result is represented by-1; and when the abnormal road condition identification result of the weak learning classifier is that the road condition of the current intersection is abnormal, the +1 is used for representing. Further, the abnormal road condition recognition results corresponding to the weak learning classifiers are multiplied by the corresponding weight values to obtain a final abnormal road condition recognition result, if the final calculated value is 0.9, the final abnormal road condition recognition result is that a target object exists at the current intersection and the road condition is abnormal because the final abnormal road condition recognition result is close to 1, and therefore the road environment perception result of the current intersection can be determined.
Further, before the abnormal road condition identification is performed by using the strong learning classifier (ABCBoost model), it needs to be trained in advance, and as an optional implementation manner, the method includes: collecting road environment sample data, constructing a sample training set, and determining initial weight distribution corresponding to the sample training set; training a first weak learning classifier according to the sample training set and the corresponding initial weight distribution; calculating a classification error rate corresponding to the first weak learning classifier according to the abnormal road condition identification result output by the first weak learning classifier and the actual road condition corresponding to the sample training set; calculating a weight value corresponding to the first weak learning classifier based on the classification error rate; updating the initial weight distribution based on the weight value of the first weak learning classifier to obtain the updated weight distribution of the sample training set; and continuing to train a second weak learning classifier according to the sample training set and the updated weight distribution, repeating the training process of the weak learning classifier until reaching a preset training frequency, and adding the trained weak learning classifiers according to the corresponding weight values to obtain the strong learning classifier.
Specifically, a sample training set T = { (x) is first constructed 1 ,y 1 ),(x 2 ,y 2 ),…,(x m ,y m ) And determining the training times of the strong learning classifier as K +1, wherein x m As road environment data, y m Is-1 or 1. Then initializing the weight distribution of the training set of the initial sample, D (1) = (w) 11 ,w 12 ,…,w 1m )；w 1i =1/m; i =1,2, \ 8230;, m, then train the first weakly learned classifier G using the initial weight distribution 1 (X) and calculating the classification error corresponding to the first weak learning classifierDifference e 1 Further, based on the classification error rate e 1 Calculating a first weak learning classifier G 1 (X) weight value a 1 Finally based on the first weak learning classifier G 1 (X) weight value a 1 Updating the initial weight distribution D (1) to obtain the updated weight distribution of the sample training set, and repeating the above process to train the second weak learning classifier G 2 (X)。
G for kth training k (X) the weight distribution thereof is D (k) = (w) k1 ,w k2 ,…,w km ) Calculating weak learning classifier G k (X) corresponding Classification error Rate e k Comprises the following steps:
Figure BDA0003976636910000161
wherein, w ki Classifier G for weak learning k Weight distribution of (X), G k (x i ) Classifier G for weak learning k And (X) outputting an abnormal road condition identification result, wherein yi is the actual road condition.
Further, weak learning classifier G is calculated k (X) weight value a k The concrete formula is as follows:
Figure BDA0003976636910000162
further, updating the weight distribution of the sample training set, wherein the specific formula is as follows:
Figure BDA0003976636910000163
Figure BDA0003976636910000164
wherein, w k+1,i For the updated weight distribution, z k Is a normalization factor. Further, a sample training set may be utilizedUpdated weight distribution w k+1,i Training weak learning classifier G k+1 (X), finally, according to the weight values corresponding to the trained weak learning classifiers, adding the weak learning classifiers to obtain a strong learning classifier:
Figure BDA0003976636910000165
therefore, according to the formula, the strong learning classifier can be trained, and the identification of abnormal road conditions is realized by using the strong learning classifier.
And 205, issuing the road environment perception result to the current intersection and other intersections.
For the embodiment of the invention, the road environment sensing result determined by the road environment sensing device can be issued not only to the current intersection, but also to other intersections.
And step 206, if the internet protocol address is not in the preset trust list and the preset attack list, determining a risk coefficient corresponding to the target device according to the standardized interface type called by the target device.
For the embodiment of the invention, the safety levels corresponding to different types of standardized interfaces are different, and when the safety level of the standardized interface called by the target equipment is higher, the corresponding danger coefficient is higher; conversely, the lower the security level of the standardized interface called by the target device, the lower the risk factor corresponding to the lower the security level.
And step 207, refusing to accept the road environment data when the danger coefficient reaches a preset danger coefficient or the historical access frequency reaches a preset access frequency.
For example, the preset access frequency is 30 times/min, if the historical access frequency of the target device exceeds 30 times/min, it is determined that malicious attacks exist in the target device, the road environment data is refused to be accepted, and the internet protocol address of the target device is added into a preset attack list. For another example, the preset risk factor is 0.5, and if the risk factor of the target device reaches 0.6, the road environment data is rejected, and the internet protocol address of the target device is added to the preset attack list. Therefore, the safety access of the system can be ensured through the setting of the preset trust list and the preset attack list.
And 208, when the danger coefficient does not reach the preset danger coefficient and the historical access frequency does not reach the preset access frequency, receiving the road environment data.
In order to further ensure the security of the system, the communication data in the embodiment of the present invention may adopt a symmetric encryption manner. Based on this, the method further comprises: and when other equipment calls the standardized interface to carry out data communication with the road environment sensing device, symmetrically encrypting the transmission data.
Specifically, for the data encryption process, firstly, original data is converted into a byte stream, then, an RSA public key is adopted to encrypt the byte stream, and then, base64 coding is carried out to obtain final encrypted data; for the data decryption process, firstly, base64 decoding is carried out on the encrypted data, then, the decoded data is decrypted by utilizing an RSA private key to obtain a byte stream, and finally, decrypted data, namely original data, is obtained.
Therefore, the embodiment of the invention can ensure the safe access of the system by means of the preset trust list, the preset attack list, the multi-factor identity authentication (password, short message verification code, true random code), the asymmetric encryption and the like.
According to the road environment sensing method provided by the embodiment of the invention, the road environment sensing device is independently deployed at the cloud end, so that the road safety sensing capability can be provided for the roadside, the additional equipment can be prevented from being deployed at the roadside, the construction cost is reduced, the reliability of the road environment sensing result can be ensured by performing big data and AI analysis on the independently deployed road environment sensing device, and the road environment sensing result can be forwarded to different intersections.
Further, as a specific implementation of fig. 1, an embodiment of the present invention provides a road environment sensing apparatus, as shown in fig. 3, the apparatus includes: an acquisition unit 31, an acceptance unit 32, a recognition unit 33, a determination unit 34, a distribution unit 35, and a determination unit 36.
The obtaining unit 31 may be configured to obtain an internet protocol address of the target device when the target device calls the standardized interface to send the road environment data of the current intersection to the road environment sensing apparatus.
The receiving unit 32 may be configured to receive the road environment data of the current intersection if the internet protocol address is in a preset trust list.
The identification unit 33 may be configured to input the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers, and identify abnormal road conditions, so as to obtain abnormal road condition identification results corresponding to the weak learning classifiers, where the weak learning classifiers respectively represent mapping relationships between the road environment data and the abnormal road condition identification results.
The determining unit 34 may be configured to determine a road environment sensing result of the current intersection according to the abnormal road condition identification results corresponding to the weak learning classifiers, where the road environment sensing result includes a V2X safety event.
The issuing unit 35 may be configured to issue the road environment sensing result to the current intersection and other intersections.
The determining unit 36 may be configured to determine whether to accept the road environment data according to a risk coefficient and a historical access frequency corresponding to the target device if the internet protocol address is not in the preset trust list and the preset attack list.
In a specific application scenario, as shown in fig. 4, the identifying unit 33 includes: an identification module 331 and a determination module 332.
The identification module 331 may be configured to input the road environment data into the strong learning classifier composed of a plurality of weak learning classifiers for target object identification.
The determining module 332 may be configured to determine, for any weak learning classifier of the weak learning classifiers, that, if the target object exists at the current intersection, the abnormal road condition identification result corresponding to the weak learning classifier is that the abnormal road condition exists at the current intersection.
The determining module 332 may be further configured to determine that the abnormal road condition identification result corresponding to any weak learning classifier is that the abnormal road condition does not exist at the current intersection if the target object does not exist at the current intersection.
In a specific application scenario, the determining unit 34 may be specifically configured to synthesize the abnormal road condition recognition results corresponding to the weak learning classifiers according to the weight values corresponding to the weak learning classifiers, respectively, to obtain a road environment sensing result of the current intersection.
In a specific application scenario, the apparatus further includes: a training unit 37.
The training unit 37 may be configured to collect road environment sample data, construct a sample training set, and determine initial weight distribution corresponding to the sample training set; training a first weak learning classifier according to the sample training set and the corresponding initial weight distribution; calculating a classification error rate corresponding to the first weak learning classifier according to the abnormal road condition identification result output by the first weak learning classifier and the actual road condition corresponding to the sample training set; calculating a weight value corresponding to the first weak learning classifier based on the classification error rate; updating the initial weight distribution based on the weight value of the first weak learning classifier to obtain the updated weight distribution of the sample training set; and continuing to train a second weak learning classifier according to the sample training set and the updated weight distribution, repeating the training process of the weak learning classifier until reaching a preset training frequency, and adding the trained weak learning classifiers according to the corresponding weight values to obtain the preset strong learning classifier.
In a specific application scenario, the determining unit 34 may be further configured to determine a risk coefficient corresponding to the target device according to a standardized interface type called by the target device.
In a specific application scenario, the determining unit 36 may be specifically configured to refuse to accept the road environment data when the risk coefficient reaches a preset risk coefficient, or the historical access frequency reaches a preset access frequency; and when the danger coefficient does not reach the preset danger coefficient and the historical access frequency does not reach the preset access frequency, receiving the road environment data.
In a specific application scenario, the standardized interface includes a user authentication interface, and the apparatus further includes: a generating unit 38.
The generating unit 38 may be configured to receive a user authentication request sent by a user through the user authentication interface, where the user authentication request carries a first user identifier and a first password; and generating an authentication token corresponding to the user according to the first user identifier and the first password, and feeding back the authentication token to the user.
In a specific application scenario, the standardized interface includes a message gateway communication configuration interface, and the apparatus further includes: a configuration unit 39.
The configuration unit 39 may be configured to receive a communication configuration request sent by the message gateway by invoking the communication configuration interface, where the communication configuration request carries a second user identifier and a second password; performing communication configuration with the message gateway based on the second user identification and the second password; and when the communication configuration with the message gateway is successful, feeding back a communication configuration success message to the message gateway.
It should be noted that other corresponding descriptions of the functional modules related to the road environment sensing device provided in the embodiment of the present invention may refer to the corresponding description of the method shown in fig. 1, and are not described herein again.
Based on the method shown in fig. 1, correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the following steps: when the target equipment calls the standardized interface to send road environment data of the current intersection to the road environment sensing device, acquiring an internet protocol address of the target equipment; if the internet protocol address is in a preset trust list, receiving road environment data of the current intersection; inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers for identifying abnormal road conditions to obtain abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results; determining a road environment perception result of the current intersection according to the abnormal road condition identification results respectively corresponding to the weak learning classifiers, wherein the road environment perception result comprises a V2X safety event; the road environment perception result is sent to the current intersection and other intersections; and if the internet protocol address is not in the preset trust list and the preset attack list, judging whether to accept the road environment data or not according to the danger coefficient and the historical access frequency corresponding to the target equipment.
Based on the above embodiments of the method shown in fig. 1 and the apparatus shown in fig. 3, an embodiment of the present invention further provides an entity structure diagram of an electronic device, as shown in fig. 5, where the electronic device includes: a processor 41, a memory 42, and a computer program stored on the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are both arranged on a bus 43 such that when the processor 41 executes the program, the following steps are performed: when the target equipment calls the standardized interface to send road environment data of the current intersection to the road environment sensing device, acquiring an internet protocol address of the target equipment; if the internet protocol address is in a preset trust list, receiving the road environment data of the current intersection; inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers for identifying abnormal road conditions to obtain abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results; determining a road environment sensing result of the current intersection according to abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the road environment sensing result comprises a V2X safety event; the road environment perception result is sent to the current intersection and other intersections; and if the internet protocol address is not in the preset trust list and the preset attack list, judging whether to accept the road environment data or not according to the danger coefficient and the historical access frequency corresponding to the target equipment.
According to the embodiment of the invention, the road environment sensing device is independently deployed at the cloud end, so that the road safety sensing capability can be provided for the road side, the additional equipment can be prevented from being deployed at the road side, the construction cost is reduced, the reliability of the road environment sensing result can be ensured by carrying out big data and AI analysis on the independently deployed road environment sensing device, and the road environment sensing result can be forwarded to different intersections.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. The road environment perception method is characterized by being applied to a road environment perception device independently deployed at the cloud end, wherein the road environment perception device is provided with a standardized interface, and the road environment perception method comprises the following steps:
when the target equipment calls the standardized interface to send road environment data of the current intersection to the road environment sensing device, acquiring an internet protocol address of the target equipment;
if the internet protocol address is in a preset trust list, receiving the road environment data of the current intersection;
inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers for identifying abnormal road conditions to obtain abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results;
determining a road environment perception result of the current intersection according to the abnormal road condition identification results respectively corresponding to the weak learning classifiers, wherein the road environment perception result comprises a V2X safety event;
the road environment perception result is sent to the current intersection and other intersections;
and if the internet protocol address is not in the preset trust list and the preset attack list, judging whether to accept the road environment data or not according to the danger coefficient and the historical access frequency corresponding to the target equipment.
2. The method as claimed in claim 1, wherein the inputting the road environment data into a strong learning classifier composed of a plurality of weak learning classifiers for abnormal road condition identification to obtain the abnormal road condition identification results corresponding to the weak learning classifiers respectively comprises:
inputting the road environment data into the strong learning classifier composed of a plurality of weak learning classifiers for target object identification;
aiming at any weak learning classifier in the weak learning classifiers, if the target object exists at the current intersection, determining that the abnormal road condition identification result corresponding to the weak learning classifier is the abnormal road condition existing at the current intersection;
if the target object at the current intersection is not identified by any classifier, determining that the abnormal road condition identification result corresponding to any weak learning classifier is that the abnormal road condition does not exist at the current intersection.
3. The method according to claim 1, wherein the determining the road environment perception result of the current intersection according to the abnormal road condition recognition results respectively corresponding to the weak learning classifiers comprises:
and according to the weight values respectively corresponding to the weak learning classifiers, synthesizing the abnormal road condition identification results respectively corresponding to the weak learning classifiers to obtain the road environment perception result of the current intersection.
4. The method according to claim 1, wherein before the road environment data is input into a strong learning classifier composed of a plurality of weak learning classifiers for abnormal road condition identification, and the abnormal road condition identification results corresponding to the weak learning classifiers are obtained, the method further comprises:
collecting road environment sample data, constructing a sample training set, and determining initial weight distribution corresponding to the sample training set;
training a first weak learning classifier according to the sample training set and the corresponding initial weight distribution;
calculating a classification error rate corresponding to the first weak learning classifier according to an abnormal road condition identification result output by the first weak learning classifier and an actual road condition corresponding to the sample training set;
calculating a weight value corresponding to the first weak learning classifier based on the classification error rate;
updating the initial weight distribution based on the weight value of the first weak learning classifier to obtain the updated weight distribution of the sample training set;
and continuing to train a second weak learning classifier according to the sample training set and the updated weight distribution, repeating the training process of the weak learning classifier until reaching a preset training frequency, and adding the trained weak learning classifiers according to the corresponding weight values to obtain the strong learning classifier.
5. The method according to any one of claims 1-4, wherein before said determining whether to accept the road environment data according to the risk factor and the historical access frequency corresponding to the target device, the method further comprises:
determining a danger coefficient corresponding to the target equipment according to the standard interface type called by the target equipment;
the determining whether to accept the road environment data according to the danger coefficient and the historical access frequency corresponding to the target device includes:
refusing to accept the road environment data when the danger coefficient reaches a preset danger coefficient or the historical access frequency reaches a preset access frequency;
and when the danger coefficient does not reach the preset danger coefficient and the historical access frequency does not reach the preset access frequency, receiving the road environment data.
6. The method of any of claims 1-4, wherein the standardized interface comprises a user authentication interface, the method further comprising:
receiving a user authentication request sent by a user through the user authentication interface, wherein the user authentication request carries a first user identifier and a first password;
and generating an authentication token corresponding to the user according to the first user identifier and the first password, and feeding back the authentication token to the user.
7. The method of any of claims 1-4, wherein the standardized interface comprises a message gateway communication configuration interface, the method further comprising:
receiving a communication configuration request sent by a message gateway by calling the communication configuration interface, wherein the communication configuration request carries a second user identifier and a second password;
performing communication configuration with the message gateway based on the second user identification and the second password;
and when the communication configuration with the message gateway is successful, feeding back a communication configuration success message to the message gateway.
8. A road environment sensing device, characterized in that the road environment sensing device is configured with a standardized interface, comprising:
the acquisition unit is used for acquiring the internet protocol address of the target equipment when the target equipment calls the standardized interface to send the road environment data of the current intersection to the road environment sensing device;
the receiving unit is used for receiving the road environment data of the current intersection if the internet protocol address is in a preset trust list;
the identification unit is used for inputting the road environment data into a strong learning classifier consisting of a plurality of weak learning classifiers to identify abnormal road conditions, and obtaining abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the weak learning classifiers respectively represent mapping relations between the road environment data and the abnormal road condition identification results;
the determining unit is used for determining a road environment sensing result of the current intersection according to the abnormal road condition identification results corresponding to the weak learning classifiers respectively, wherein the road environment sensing result comprises a V2X safety event;
the issuing unit is used for issuing the road environment sensing result to the current intersection and other intersections;
and the judging unit is used for judging whether to accept the road environment data according to the danger coefficient and the historical access frequency corresponding to the target equipment if the internet protocol address is not in the preset trust list and the preset attack list.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the computer program realizes the steps of the method of any of claims 1 to 7 when executed by the processor.
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