CN114670841A - Road condition complexity judging method for advanced auxiliary driving and readable storage medium - Google Patents
Road condition complexity judging method for advanced auxiliary driving and readable storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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Abstract
The invention relates to the technical field of advanced assistant driving functions, in particular to a road condition complexity judging method and a readable storage medium for advanced assistant driving. The method comprises the following steps: acquiring road condition monitoring data of a current running road condition with a high-grade auxiliary driving function; extracting road condition judgment elements which meet the current running road condition from the road condition monitoring data; calculating the complexity integral of the current running road condition based on the influence factors corresponding to the road condition judgment elements; and judging whether the current running road condition of the advanced auxiliary driving function is complex or not through complexity integration, and further generating a corresponding road condition complexity judgment result. The invention also discloses a readable storage medium. The invention can meet the requirement of complexity judgment of the running road condition of the advanced assistant driving function, and can ensure the accuracy and effectiveness of the complexity judgment of the road condition, thereby assisting the application of the advanced assistant driving function.
Description
Technical Field
The invention relates to the technical field of advanced assistant driving functions, in particular to a road condition complexity judging method and a readable storage medium for advanced assistant driving.
Background
Along with the development and popularization of the advanced driving assistance function of the automobile, the market and enterprises are greatly promoting the landing of the advanced driving assistance function and improving the customer acceptance. The highest guarantee of user experience of the advanced auxiliary driving function on the premise of guaranteeing safety and reliability is the most important link for improving the customer acceptance. The complex shape judgment of the advanced assistant driving function running road condition is an important part in the balance between the optimization function safety and the driving experience, and provides support for the boundary division of the operable use range of the advanced assistant driving function.
To solve the problem of road condition complexity identification and judgment, chinese patent publication No. CN104036638A discloses "a real-time road condition monitoring method and real-time road condition monitoring device", and the method includes: acquiring driving record information of a vehicle fed back by a vehicle-mounted terminal; determining and recording the current road condition information of the vehicle according to the driving record information of the vehicle; if the current driving speed of the vehicle is lower than the preset threshold value, judging whether the road condition information of N other vehicles meeting the conditions is recorded, and if the road condition information of N other vehicles meeting the conditions is recorded, judging that the current road section of the vehicle is in a congestion state.
The road condition monitoring method in the prior art is also a road condition complexity judging method, and realizes real-time road condition monitoring according to the driving record information, so that whether the road condition is complex or not can be judged. However, the applicant finds that the above-mentioned existing solutions only judge the complexity of the road condition according to the current road section, the current driving direction, the current driving speed and other conditions. On one hand, the judgment elements (conditions) in the existing scheme are not associated with the advanced assistant driving function, so that the requirement of the advanced assistant driving function on road condition complexity judgment is difficult to be completely met, and the application effect of the advanced assistant driving function is poor. On the other hand, the existing scheme has no clear indexes and requirements for judging the road condition complexity, so that the existing scheme is difficult to accurately and effectively judge whether the road condition is complex or not, which can also cause poor application effect of the advanced auxiliary driving function. Therefore, how to design a method capable of meeting the requirement of complexity judgment of the running road condition of the advanced assistant driving function and ensuring the accuracy and effectiveness of the complexity judgment of the road condition is an urgent technical problem to be solved.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: how to provide a road condition complexity judging method for advanced assistant driving so as to meet the requirement of road condition complexity judgment of advanced assistant driving function operation, and ensure the accuracy and effectiveness of road condition complexity judgment, thereby assisting the application of advanced assistant driving function.
In order to solve the technical problems, the invention adopts the following technical scheme:
the road condition complexity judging method for advanced assistant driving comprises the following steps of:
s1: acquiring road condition monitoring data of a current running road condition with a high-grade auxiliary driving function;
s2: extracting road condition judgment elements which meet the current running road condition from the road condition monitoring data; the road condition judgment element is associated with the road condition complexity judgment requirement of the advanced auxiliary driving function;
s3: calculating the complexity integral of the current running road condition based on the influence factors corresponding to the road condition judgment elements; the influence factor is associated with the influence degree of the road condition judgment element on the road condition complexity;
s4: and judging whether the current running road condition of the advanced auxiliary driving function is complex or not through complexity integration, and further generating a corresponding road condition complexity judgment result.
Preferably, in step S2, the types of the road condition determining elements include, but are not limited to, a lane line status class, a vehicle speed status class, an advanced driving assistance function status class, a road environment status class, and a deceleration target status class.
Preferably, the lane line status category includes the following road condition judgment elements:
a1, subtracting the vehicle width from the distance between the left lane line and the right lane line of the current vehicle by less than 0.5m and keeping the time longer than 5 s;
a2, the center line of the current vehicle is in a superposition state with the left lane line or the right lane line, and the duration time is more than 1 s;
a3, identifying the current lane line at the left side of the vehicle as a no-lane line or a guardrail, wherein the duration time is more than 1 s;
a4, judging whether the current lane line recognition state on the right side of the vehicle is a lane line or a guardrail and the duration is more than 1 s;
a5, the state of the lane line satisfies any one of the following items:
in A501 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are more than or equal to 14, and the accumulated time in other states is more than or equal to 3 s;
in A502 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 14 but more than or equal to 12, and the accumulated time in other states is more than or equal to 4.5 s;
in A503 s and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 12 but more than or equal to 10, and the accumulated time in other states is more than or equal to 4.8 s;
in A504 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 10 but more than or equal to 8, and the accumulated time in other states is more than or equal to 5.1 s;
a505, the weather condition is snowing and the duration is more than 30 s.
Preferably, the vehicle speed status category includes the following road condition judgment elements:
a6, the vehicle speed state satisfies any one of the following items:
a601, the current vehicle speed is greater than the set cruising speed by 20kph, the set cruising speed is less than or equal to 70kph, and the duration is more than 3 s;
a602, the current vehicle speed is greater than a set cruising speed value, the ratio of the current vehicle speed to the set cruising speed is greater than or equal to 30%, the set cruising speed is greater than 70kph, and the duration is greater than 3 s;
a603, setting the current vehicle speed to be greater than the road speed limit value by 20kph, setting the cruising speed to be less than or equal to 70kph and the duration to be more than 3 s;
a604, the current vehicle speed is greater than the road speed limit value, the ratio of the current vehicle speed to the road speed limit value is greater than or equal to 30%, the set cruising speed is greater than 70kph, and the duration is greater than 3 s.
Preferably, the advanced driving assistance function state includes the following road condition judgment elements:
a7, the transverse control state of the current advanced auxiliary driving function of the vehicle is overrunning, the current hand torque of the driver does not exceed a set threshold value, and the duration is more than 1 s;
and in A8 and 10s, the number of times of switching acceleration and deceleration is triggered to be more than 4 times by the longitudinal control of the current advanced vehicle driving assistance function.
Preferably, the road environment status category includes the following road condition judgment elements:
a9, judging that the current road traffic flow state is congestion or serious congestion;
a10, judging whether the current road type is an expressway or an urban expressway and the road type at the position 600m ahead is unknown or an urban main road or a provincial road or a national road, wherein the duration time is more than 3 s;
a11, detecting the existence of pedestrians in the driving lane and the lanes on the two sides;
a12, judging whether the working state of the vehicle dipped headlight is on or not and whether the current illumination intensity is at night or is lower than a preset value or not by ambient light detection;
a13, the time for the current position to reach the lane number change point in front according to the current speed is less than 15s, or the distance between the current position and the lane number change point is less than 100 m;
and A14, the time of reaching the front confluence point at the current position according to the current speed is less than 15s, or the distance between the current position and the front confluence point is less than 100 m.
Preferably, the deceleration target state class includes the following road condition judgment elements:
a15, the time of arriving at the front toll station at the current position according to the current speed is less than 15s or the distance between the current position and the front toll station is less than 100 m;
a16, the time of arriving at the front ramp at the current position according to the current speed is less than 15s or the distance between the current position and the front ramp is less than 100m, and the map navigation information is as follows: the vehicle enters a high speed through a ramp or exits the high speed through the ramp;
a17, the time of reaching the front construction area at the current position according to the current speed is less than 15s, or the distance between the current position and the front construction area is less than 100m, or the driving distance after passing through the construction point is less than 500 m;
a18, the time of arriving at the front traffic accident point at the current position according to the current speed is less than 15s, or the distance between the current position and the front traffic accident point is less than 100m, or the driving distance passing through the traffic accident point is less than 500 m;
a19, the time of arriving at the front service area at the current position according to the current speed is less than 15s or the distance between the current position and the front service area is less than 100 m;
a20, detecting obstacles on the right side or two sides of the lane line on the left side of the current vehicle;
a21, detecting the obstacle on the left side or two sides of the lane line on the right side of the current vehicle.
Preferably, in step S2, the influence factor corresponding to the road condition determining element is generated based on the subjective performance evaluation performance of the advanced driver assistance function in each scene of the road condition determining element and the occurrence probability of the unpredictable situation.
Preferably, in step S3, the complexity integral is calculated by the following formula:
in the formula: k represents a complexity integral; n represents the number of satisfied road condition judgment elements; a. theiAnd representing the influence factors corresponding to the road condition judging elements.
The invention also discloses a readable storage medium, which stores a computer management program, and the computer management program realizes the steps of the road condition complexity judging method for advanced assistant driving when being executed by a processor.
Compared with the prior art, the road condition complexity judging method for advanced assistant driving has the following beneficial effects:
the method and the device acquire road condition monitoring data of the current running road condition, extract road condition judgment elements which are satisfied by the current running road condition from the road condition monitoring data, and further calculate complexity integral of the current running road condition based on influence factors corresponding to the road condition judgment elements so as to judge whether the current running road condition is complex or not. On one hand, the road condition judging element is associated with the road condition complexity judging requirement of the advanced assistant driving function, so that whether the current running road condition of the advanced assistant driving function is complex or not can be effectively judged based on the road condition judging element, that is, the requirement of the advanced assistant driving function on the running road condition complexity judgment can be met, and the application of the advanced assistant driving function of the vehicle can be assisted. On the other hand, the influence factor of the road condition judgment element is related to the influence degree of the road condition judgment element on the road condition complexity, so that the complexity integral used for judging the complexity of the running road condition of the advanced assistant driving function can be accurately calculated based on the road condition judgment element and the influence factor, the accuracy quantification of the complexity of the running road condition can be realized, the accuracy and the effectiveness of the road condition complexity judgment can be further ensured, and the application of the advanced assistant driving function can be better assisted.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
fig. 1 is a logic block diagram of a road condition complexity determination method for advanced assistant driving.
Detailed Description
The following is further detailed by the specific embodiments:
the first embodiment is as follows:
the embodiment discloses a road condition complexity judging method for advanced assistant driving.
As shown in fig. 1, the method for determining complexity of road conditions for advanced assistant driving includes the following steps:
s1: acquiring road condition monitoring data of a current running road condition with a high-grade auxiliary driving function;
in this embodiment, road condition monitoring data including, but not limited to, map navigation data, vehicle driving state data (including vehicle speed and various settings of the vehicle), front road and surrounding video image data, lane line and other sensing data, vehicle and pedestrian sensing data, and light brightness and other sensing data are acquired by using existing mature means, and specific means are not described herein again.
S2: extracting road condition judgment elements which meet the current running road condition from the road condition monitoring data; the road condition judgment element is associated with the road condition complexity judgment requirement of the advanced auxiliary driving function;
s3: calculating the complexity integral of the current running road condition based on the influence factors corresponding to the road condition judgment elements; the influence factor is associated with the influence degree of the road condition judgment element on the road condition complexity;
s4: and judging whether the current running road condition of the advanced auxiliary driving function is complex or not through complexity integration, and further generating a corresponding road condition complexity judgment result.
It should be noted that the road condition complexity determining method for advanced assistant driving in the present invention can generate corresponding software codes or software services in a program programming manner, and further can be run and implemented on a server and a computer.
The method and the device acquire road condition monitoring data of the current running road condition, extract road condition judgment elements which are satisfied by the current running road condition from the road condition monitoring data, and further calculate complexity integral of the current running road condition based on influence factors corresponding to the road condition judgment elements so as to judge whether the current running road condition is complex or not. On one hand, the road condition judging element is associated with the road condition complexity judging requirement of the advanced assistant driving function, so that whether the current running road condition of the advanced assistant driving function is complex or not can be effectively judged based on the road condition judging element, that is, the requirement of the advanced assistant driving function on the running road condition complexity judgment can be met, and the application of the advanced assistant driving function of the vehicle can be assisted. On the other hand, the influence factor of the road condition judgment element is related to the influence degree of the road condition judgment element on the road condition complexity, so that the complexity integral used for judging the complexity of the running road condition of the advanced assistant driving function can be accurately calculated based on the road condition judgment element and the influence factor, the accuracy quantification of the complexity of the running road condition can be realized, the accuracy and the effectiveness of the road condition complexity judgment can be further ensured, and the application of the advanced assistant driving function can be better assisted.
In the specific implementation process, the types of the road condition determining elements include, but are not limited to, a lane line state, a vehicle speed state, an advanced driver assistance function state, a road environment state and a deceleration target state.
The lane line state includes the following road condition judgment elements:
a1, subtracting the vehicle width from the distance between the left lane line and the right lane line of the current vehicle by less than 0.5m and keeping the time longer than 5 s;
a2, the center line of the current vehicle is in a superposition state with the left lane line or the right lane line, and the duration time is more than 1 s;
a3, identifying the left lane line of the current vehicle as a lane line or a guardrail, wherein the duration time is more than 1 s;
a4, judging whether the current lane line recognition state on the right side of the vehicle is a lane line or a guardrail and the duration is more than 1 s;
a5, the state of the lane line satisfies any one of the following items:
in A501 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are more than or equal to 14, and the accumulated time in other states is more than or equal to 3 s;
in A502 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 14 but more than or equal to 12, and the accumulated time in other states is more than or equal to 4.5 s;
in A503 s and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 12 but more than or equal to 10, and the accumulated time in other states is more than or equal to 4.8 s;
in A504 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 10 but more than or equal to 8, and the accumulated time in other states is more than or equal to 5.1 s;
a505, the weather condition is snowing and the duration is more than 30 s.
In the present embodiment, the detection and determination of the lane line correlation can be realized based on functions such as lane departure assistance that are mounted on an existing automobile.
The vehicle speed state class comprises the following road condition judgment elements:
a6, the vehicle speed state satisfies any one of the following items:
a601, the current vehicle speed is greater than the set cruising speed by 20kph, the set cruising speed is less than or equal to 70kph, and the duration is more than 3 s;
a602, the current vehicle speed is greater than a set cruising speed value, the ratio of the current vehicle speed to the set cruising speed is greater than or equal to 30%, the set cruising speed is greater than 70kph, and the duration is greater than 3 s;
a603, setting the current vehicle speed to be greater than the road speed limit value by 20kph, setting the cruising speed to be less than or equal to 70kph and the duration to be more than 3 s;
a604, the current vehicle speed is greater than the road speed limit value, the ratio of the current vehicle speed to the road speed limit value is greater than or equal to 30%, the set cruising speed is greater than 70kph, and the duration is greater than 3 s.
In the present embodiment, the detection and determination of the vehicle speed can be realized based on functions such as adaptive cruise, which are mounted on an existing automobile.
The advanced driving assistance function state comprises the following road condition judgment elements:
a7, the transverse control state of the current advanced auxiliary driving function of the vehicle is overrunning, the current hand torque of the driver does not exceed a set threshold value, and the duration is more than 1 s;
and in A8 and 10s, the number of times of switching acceleration and deceleration is triggered to be more than 4 times by the longitudinal control of the current advanced vehicle driving assistance function.
The road environment status category includes the following road condition judgment elements:
a9, judging that the current road traffic flow state is congestion or serious congestion;
a10, judging whether the current road type is an expressway or an urban expressway and the road type at the position 600m ahead is unknown or an urban main road or a provincial road or a national road, wherein the duration time is more than 3 s;
a11, detecting the existence of pedestrians in the driving lane and the lanes on the two sides;
a12, judging whether the working state of the vehicle dipped headlight is on or not and whether the current illumination intensity is at night or is lower than a preset value or not by ambient light detection;
a13, the time of the current position reaching the lane number change point in front according to the current speed is less than 15s or the distance between the current position and the lane number change point is less than 100 m;
a14, the time of the current position reaching the front confluence point according to the current speed is less than 15s, or the distance between the current position and the front confluence point is less than 100 m.
In this embodiment, the relevant detection and judgment of the road environment can be realized based on the map navigation information in combination with the functions of lane departure warning, pedestrian protection, adaptive headlights and the like carried on the existing automobile.
The deceleration target state class includes the following road condition judgment elements:
a15, the time of arriving at the front toll station at the current position according to the current speed is less than 15s or the distance between the current position and the front toll station is less than 100 m;
a16, the time of arriving at the front ramp at the current position according to the current speed is less than 15s or the distance between the current position and the front ramp is less than 100m, and the map navigation information is as follows: the vehicle enters the high speed through the ramp or exits the high speed through the ramp;
a17, the time of reaching the front construction area at the current position according to the current speed is less than 15s, or the distance between the current position and the front construction area is less than 100m, or the driving distance after passing through the construction point is less than 500 m;
a18, the time of arriving at the front traffic accident point at the current position according to the current speed is less than 15s, or the distance between the current position and the front traffic accident point is less than 100m, or the driving distance passing through the traffic accident point is less than 500 m;
a19, the time of the current position reaching the front service area according to the current speed is less than 15s or the distance between the current position and the front service area is less than 100 m;
a20, detecting obstacles (such as cone) on the right side or two sides of the lane line on the left side of the current vehicle;
a21, an obstacle (cone or the like) is detected on the left side or both sides of the lane line on the right side of the current vehicle.
In this embodiment, the relevant detection and determination of the deceleration target can be implemented based on map navigation information in combination with functions such as automatic brake warning and the like mounted on the existing automobile.
According to the invention, through the road condition judging elements of the types of lane line state, vehicle speed state, advanced assistant driving function state, road environment state, deceleration target state and the like, whether the current running road condition of the advanced assistant driving function is complex can be comprehensively judged, and the road condition judging element is associated with the road condition complexity judging requirement of the advanced assistant driving function, so that whether the current running road condition of the advanced assistant driving function is complex can be effectively judged based on the road condition judging element, and the requirement of the complexity judgment of the running road condition of the advanced assistant driving function can be better met.
In the specific implementation process, the influence factors corresponding to the road condition judgment elements are generated based on the subjective performance evaluation performance of the advanced assistant driving function in each road condition judgment element scene and the occurrence probability of unpredictable situations.
In this embodiment, the influence factors corresponding to the road condition determining elements are shown in table 1.
TABLE 1
According to the invention, the influence factor of the road condition judgment element is associated with the influence degree of the road condition judgment element on the road condition complexity, and the subjective performance evaluation performance and the occurrence probability of unpredictable situations are generated based on the advanced assistant driving function under each road condition judgment element scene, so that the complexity integral used for judging the complexity of the advanced assistant driving function running road condition can be accurately calculated based on the road condition judgment element and the influence factor, the accuracy quantification of the complexity of the running road condition can be realized, and the road condition complexity judgment accuracy and the effectiveness can be further improved.
In a specific implementation process, in step S3, the complexity integral is calculated by the following formula:
in the formula: k represents a complexity integral; n represents fullThe number of the road condition judging elements is enough; a. theiAnd representing the influence factors corresponding to the road condition judging elements.
In this embodiment, when the complexity integral K is greater than or equal to 40%, or K is less than 40% and the influence factor of any one road condition determining element of the N road condition determining elements satisfying the condition is greater than or equal to 6, it is determined that the current operating road condition with the advanced driver assistance function is complex.
According to the invention, the complexity integral used for judging the complexity of the running road condition with the advanced assistant driving function is accurately calculated through the road condition judging element and the influence factor, so that the accuracy quantification and comparison of the complexity of the running road condition can be realized, and the accuracy and the effectiveness of the judgment of the complexity of the road condition can be further improved.
Example two:
disclosed in the present embodiment is a readable storage medium.
A readable storage medium, on which a computer management-like program is stored, which when executed by a processor implements the steps of the road condition complexity judgment method for advanced assistant driving of the present invention. The readable storage medium can be a device with readable storage function such as a U disk or a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the technical solutions, and those skilled in the art should understand that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.
Claims (10)
1. The road condition complexity judging method for advanced assistant driving is characterized by comprising the following steps of:
s1: acquiring road condition monitoring data of a current running road condition with a high-grade auxiliary driving function;
s2: extracting road condition judgment elements which meet the current running road condition from the road condition monitoring data; the road condition judgment element is associated with the road condition complexity judgment requirement of the advanced auxiliary driving function;
s3: calculating the complexity integral of the current running road condition based on the influence factors corresponding to the road condition judgment elements; the influence factor is associated with the influence degree of the road condition judgment element on the road condition complexity;
s4: and judging whether the current running road condition of the advanced auxiliary driving function is complex or not through complexity integration, and further generating a corresponding road condition complexity judgment result.
2. The road condition complexity judging method for advanced assistive driving as claimed in claim 1, characterized in that: in step S2, the types of the road condition determination elements include, but are not limited to, a lane line status, a vehicle speed status, an advanced driver assistance function status, a road environment status, and a deceleration target status.
3. The road condition complexity judgment method for advanced assistive driving as claimed in claim 2, wherein: the lane line state includes the following road condition judgment elements:
a1, subtracting the vehicle width from the distance between the left lane line and the right lane line of the current vehicle by less than 0.5m and keeping the time longer than 5 s;
a2, the center line of the current vehicle is in a superposition state with the left lane line or the right lane line, and the duration time is more than 1 s;
a3, identifying the left lane line of the current vehicle as a lane line or a guardrail, wherein the duration time is more than 1 s;
a4, identifying the current lane line at the right side of the vehicle as a no-lane line or a guardrail, wherein the duration time is more than 1 s;
a5, the state of the lane line satisfies any one of the following items:
in A501 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are more than or equal to 14, and the accumulated time in other states is more than or equal to 3 s;
in A502 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 14 but more than or equal to 12, and the accumulated time in other states is more than or equal to 4.5 s;
in A503 s and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 12 but more than or equal to 10, and the accumulated time in other states is more than or equal to 4.8 s;
in A504 and 10s, the jumping times of the lane line identification results on the two sides of the current vehicle between the double lane lines and other states are less than 10 but more than or equal to 8, and the accumulated time in other states is more than or equal to 5.1 s;
a505, the weather condition is snowing and the duration is more than 30 s.
4. The road condition complexity judgment method for advanced assistive driving as claimed in claim 2, wherein: the vehicle speed state class comprises the following road condition judgment elements:
a6, the vehicle speed state satisfies any one of the following items:
a601, the current vehicle speed is greater than the set cruising speed by 20kph, the set cruising speed is less than or equal to 70kph, and the duration is more than 3 s;
a602, the current vehicle speed is greater than a set cruising speed value, the ratio of the current vehicle speed to the set cruising speed is greater than or equal to 30%, the set cruising speed is greater than 70kph, and the duration is greater than 3 s;
a603, setting the current vehicle speed to be greater than the road speed limit value by 20kph, setting the cruising speed to be less than or equal to 70kph and the duration to be more than 3 s;
a604, the current vehicle speed is greater than the road speed limit value, the ratio of the current vehicle speed to the road speed limit value is greater than or equal to 30%, the set cruising speed is greater than 70kph, and the duration is greater than 3 s.
5. The road condition complexity judgment method for advanced assistive driving as claimed in claim 2, wherein: the advanced driving assistance function state comprises the following road condition judgment elements:
a7, the transverse control state of the current advanced auxiliary driving function of the vehicle is overrunning, the current hand torque of the driver does not exceed a set threshold value, and the duration is more than 1 s;
and in A8 and 10s, the number of times of switching acceleration and deceleration is triggered to be more than 4 times by the longitudinal control of the current advanced vehicle driving assistance function.
6. The road condition complexity judging method for advanced assistant driving as claimed in claim 2, characterized in that: the road environment status category includes the following road condition judgment elements:
a9, judging that the current road traffic flow state is congestion or serious congestion;
a10, judging whether the current road type is an expressway or an urban expressway and the road type at the position 600m ahead is unknown or an urban main road or a provincial road or a national road, wherein the duration time is more than 3 s;
a11, detecting the existence of pedestrians in the driving lane and the lanes on the two sides;
a12, judging whether the working state of the vehicle dipped headlight is on or not and whether the current illumination intensity is at night or is lower than a preset value or not by ambient light detection;
a13, the time for the current position to reach the lane number change point in front according to the current speed is less than 15s, or the distance between the current position and the lane number change point is less than 100 m;
and A14, the time of reaching the front confluence point at the current position according to the current speed is less than 15s, or the distance between the current position and the front confluence point is less than 100 m.
7. The road condition complexity judgment method for advanced assistive driving as claimed in claim 2, wherein: the deceleration target state class includes the following road condition judgment elements:
a15, the time of arriving at the front toll station at the current position according to the current speed is less than 15s or the distance between the current position and the front toll station is less than 100 m;
a16, the time of arriving at the front ramp at the current position according to the current speed is less than 15s or the distance between the current position and the front ramp is less than 100m, and the map navigation information is as follows: the vehicle enters a high speed through a ramp or exits the high speed through the ramp;
a17, the time of reaching the front construction area at the current position according to the current speed is less than 15s, or the distance between the current position and the front construction area is less than 100m, or the driving distance after passing through the construction point is less than 500 m;
a18, the time of arriving at the front traffic accident point at the current position according to the current speed is less than 15s, or the distance between the current position and the front traffic accident point is less than 100m, or the driving distance passing through the traffic accident point is less than 500 m;
a19, the time of arriving at the front service area at the current position according to the current speed is less than 15s or the distance between the current position and the front service area is less than 100 m;
a20, detecting obstacles on the right side or two sides of the lane line on the left side of the current vehicle;
a21, detecting the obstacle on the left side or two sides of the lane line on the right side of the current vehicle.
8. The road condition complexity judging method for advanced assistive driving as claimed in claim 1, characterized in that: in step S2, the influence factor corresponding to the road condition determining element is generated based on the subjective performance evaluation performance of the advanced driver assistance function in each road condition determining element scene and the occurrence probability of the unpredictable situation.
9. The road condition complexity judging method for advanced assistive driving as claimed in claim 1, characterized in that: in step S3, the complexity integral is calculated by the following formula:
in the formula: k represents the complexity integral; n represents the number of satisfied road condition judgment elements; a. theiAnd representing the influence factors corresponding to the road condition judging elements.
10. A readable storage medium, having a computer management-like program stored thereon, wherein the computer management-like program, when executed by a processor, implements the steps of the road condition complexity determination method for advanced assistant driving according to any one of claims 1 to 9.
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