CN115195791A - Unmanned driving speed control method and device based on big data - Google Patents
Unmanned driving speed control method and device based on big data 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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
- B60W30/143—Speed control
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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
- B60W2556/00—Input parameters relating to data
- B60W2556/05—Big data
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B60W2556/00—Input parameters relating to data
- B60W2556/10—Historical data
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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
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Abstract
The invention relates to the technical field of vehicle driving control, in particular to a big data-based unmanned speed control method and a big data-based unmanned speed control device, wherein the method is used for acquiring a standard case on a current traveling road section of an unmanned vehicle; acquiring the running data of the unmanned vehicle; screening out a plurality of sections of barrier-free driving fragments, and obtaining a standard fragment corresponding to the barrier-free driving fragment in each standard case; respectively acquiring sequence distances between the barrier-free driving fragments and each standard fragment; the method comprises the steps of obtaining a candidate speed of each standard case, obtaining an overall difference between each standard case and driving data based on sequence distance, obtaining an expected speed of the current moment based on the overall difference and the candidate speed, and carrying out speed regulation and control on the unmanned automobile based on the expected speed. The invention can make the unmanned vehicle self-adaptively control the speed at any moment, can flexibly adjust the driving speed when meeting an emergency and has real-time property.
Description
Technical Field
The invention relates to the technical field of vehicle driving control, in particular to a big data-based unmanned speed control method and device.
Background
The unmanned technology is a comprehensive body of multi-door leading-edge subjects such as sensors, computers, artificial intelligence, communication, navigation positioning, mode recognition, machine vision, intelligent control and the like, and is an important future development direction of the automobile industry. In order to realize the unmanned driving of the automobile, in the prior art, various sensors mounted on an automobile body are generally used to realize the sensing of the unmanned vehicle to the environment, and the decision of the action of the unmanned vehicle is made according to the sensing result, wherein one important decision parameter of the action of the unmanned vehicle is the driving speed.
In the prior art, when a sensor is used for controlling the driving speed, the sensing range is limited, so that obstacles, other vehicles and the like in the front of a vehicle body can be sensed only to control the driving speed, and the driving speed cannot be controlled according to the whole road condition adaptability of a road section where the vehicle runs.
The unmanned technology of traffic big data is introduced, the speed of each position on the current traveling road section is often acquired and set as a speed expected value, and the speed of the unmanned automobile is regulated and controlled.
Disclosure of Invention
The invention provides a big data-based unmanned speed control method and device, which are used for solving the problem that an unmanned vehicle cannot adaptively control the driving speed in the prior art.
The adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a big data-based unmanned speed control method, including the following steps:
acquiring a historical speed sequence of vehicles of the same type running without obstacles on a current running road section of the unmanned vehicle as a standard case; acquiring all speeds of the unmanned vehicle from the time of entering a road section to the current time to form driving data;
screening a plurality of sections of barrier-free driving sections from the driving data based on the driving mode of the unmanned vehicle, and acquiring a standard section corresponding to the barrier-free driving section in each standard case; respectively acquiring sequence distances between the barrier-free driving fragments and each standard fragment;
acquiring a real-time running distance of the unmanned vehicle at the current moment, selecting a speed with the minimum difference with the real-time running distance from each standard case as a candidate speed, and calculating the absolute value of the difference between each candidate speed and the speed of the unmanned vehicle at the current moment;
and acquiring the overall difference between each standard case and the running data based on the sequence distance, calculating the product of the overall difference corresponding to each standard case and the absolute value of the difference, taking the candidate speed of the standard case with the minimum product as the expected speed at the current moment, and regulating the speed of the unmanned automobile based on the expected speed.
Preferably, the standard case is obtained by the following method:
and acquiring the same type of vehicles on the road section, which are the same as the type of the unmanned vehicles, through the big data, and recording the speed of each time in the process from the time of entering the road section to the time of leaving the road section for each vehicle of the same type running without obstacles to form the historical speed sequence, namely the historical speed sequence is the standard case.
Preferably, the screening process of the barrier-free driving segment is as follows:
and forming an obstacle avoidance sequence from all speeds in the process from the start to the stop of an obstacle avoidance control module of the unmanned vehicle, extracting all the obstacle avoidance sequences and removing the obstacle avoidance sequences, so that the running data is cut into a plurality of sequences, and each sequence is the obstacle-free running segment.
Preferably, the step of obtaining the standard fragment comprises:
for each section of barrier-free driving segment, acquiring a corresponding starting time and a corresponding ending time, and acquiring a first driving route of the unmanned vehicle before the starting time and a second driving route before the ending time;
and selecting the moment with the minimum difference with the first driving distance as a standard starting moment and selecting the moment with the minimum difference with the second driving distance as a standard ending moment in each standard case, wherein all speeds between the standard starting moment and the standard ending moment in each standard case form a standard segment corresponding to the barrier-free driving segment.
Preferably, the method for acquiring the first travel distance comprises the following steps:
and performing integral operation on all speeds between the moment of entering the road section and the starting moment, wherein the obtained result is the first driving route.
Preferably, the sequence distance obtaining method includes:
and acquiring the distance between the barrier-free running segment and each standard segment by using a dynamic time warping algorithm as the sequence distance.
Preferably, the method for acquiring the real-time travel distance comprises the following steps:
and performing integral operation on all speeds between the moment of entering the road section and the current moment to obtain a result, namely the real-time driving distance.
Preferably, the method for acquiring the overall difference comprises the following steps:
and for each standard case, acquiring the sequence distances of all the barrier-free driving segments in the standard segments corresponding to the standard case, wherein the sum of all the sequence distances is the overall difference.
In a second aspect, another embodiment of the present invention provides a big data-based unmanned speed control device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the big data-based unmanned speed control method when executing the computer program.
The embodiment of the invention at least has the following beneficial effects:
1. the method comprises the steps of obtaining the expected speed of each moment on a traveling road section of the unmanned vehicle, and carrying out speed regulation and control on the unmanned vehicle based on the expected speed, so that the unmanned vehicle can adaptively regulate and control the speed at any moment, can flexibly regulate the driving speed when meeting an emergency, and has real-time property.
2. The expected speed of the current moment is obtained according to the historical speed data of the same type of vehicles and the running data of the unmanned vehicles, the most suitable expected speed can be obtained based on the actual conditions of different road sections and the actual conditions of self driving, the speed change in the driving process is smooth, the regulating quantity is small, and the riding comfort is better.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of 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 other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart illustrating steps of a big data-based unmanned speed control method according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, structures, features and effects of a big data based unmanned speed control method and device according to the present invention are provided with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the unmanned speed control method and device based on big data in detail with reference to the accompanying drawings.
Referring to fig. 1, a flow chart illustrating steps of a big data based unmanned speed control method according to an embodiment of the present invention is shown, the method includes the following steps:
s001, acquiring a historical speed sequence of vehicles of the same type running without obstacles on a road section where the unmanned vehicle runs currently as a standard case; all speeds of the unmanned vehicle from the moment of entering the road section to the current moment are obtained to form the driven data.
The method comprises the following specific steps:
1. a current road segment to travel is identified.
And manually dividing each road to obtain a starting point and an end point of each road section.
Road segment division is generally to divide a road into four types, namely a straight line segment, a longitudinal slope segment, a flat curve segment and a curved slope combined segment according to safety evaluation. In practical application, the division may be performed according to practical situations, for example, an excessively long straight line segment may also be divided into multiple segments.
2. And acquiring a standard case.
The same type of vehicles on the road section, which are the same as the type of the unmanned vehicles, are obtained through the big data, and for each vehicle of the same type running without obstacles, the speed at each moment is recorded in the process from the moment of entering the road section to the moment of leaving the road section to form a historical speed sequence, namely a standard case.
Based on the support of big data, when the unmanned vehicle enters a road section, other vehicles of the same type as the unmanned vehicle can be obtained through the traffic big data, for example, the unmanned vehicle is a car, and other cars are the same type of the unmanned vehicle; the unmanned vehicles are trucks, and other trucks are similar vehicles of the unmanned vehicles. The sedan can be classified into a mini sedan, a common sedan, a middle and high sedan, a high class sedan and the like according to the types of the sedans; the trucks are classified into mini trucks, light trucks, medium trucks, heavy trucks and the like according to the types, and the specific classification method is determined according to actual conditions.
Obtaining a historical speed sequence of the same type of unmanned vehicles which always run without obstacles on the road section, wherein the obstacle-free running refers to the speed-time sequence when the vehicles pass through the whole road section when obstacle avoidance operations such as obstacle avoidance and overtaking are not carried out on the current road section. Taking the ith vehicle of the same type as an example, the historical speed sequence is expressed as follows:
wherein the content of the first and second substances,
representing a historical speed sequence of an ith vehicle of the same type,
indicating the initial moment when the ith vehicle of the same type enters the road segment,
indicating the speed of the ith vehicle of the same type at the initial time,
the next time instant representing the initial time instant,
representing the speed of the ith vehicle of the same type at a time next to the initial time,
indicating the final moment when the ith vehicle of the same type is driven out of the road segment,
representing the final time speed of the ith vehicle of the same type.
It should be noted that the time intervals between adjacent time instants are the same, and for real-time control, the time intervals may be as short as possible, for example, between 1 second and 3 seconds, and in the embodiment of the present invention, the time interval is 1 second.
Each historical speed sequence isA standard case for obtaining M vehicles of the same type and recording the standard cases
，
。
3. The driven data of the unmanned vehicle is acquired.
All speeds of the unmanned vehicle from the moment of entering the road section to the current moment are obtained to form the driven data.
At the present moment
To show, the unmanned vehicle is shown in the following manner
Travel data at time:
wherein the content of the first and second substances,
indicating that the unmanned vehicle is
The data of the already-driven vehicle at the time,
indicating the moment of unmanned vehicle entering road section
The speed of the time of flight or flight of the flight,
indicating that the unmanned vehicle is
The speed of the moment in time is,
indicating that the unmanned vehicle is
The speed of the moment in time.
S002, screening a plurality of sections of barrier-free driving sections from the driving data based on the driving mode of the unmanned vehicle, and acquiring a standard section corresponding to the barrier-free driving section in each standard case; and respectively acquiring the sequence distance between the barrier-free driving segment and each standard segment.
The method comprises the following specific steps:
1. and screening the barrier-free driving fragments.
Forming an obstacle avoidance sequence from all speeds in the process from the start to the stop of an obstacle avoidance control module of the unmanned vehicle, extracting all the obstacle avoidance sequences to remove, and cutting the driving data into a plurality of sequences, wherein each sequence is an obstacle-free driving segment.
The method comprises the steps that an obstacle avoidance control module starts to work in the process of driving the unmanned vehicle, the obstacle avoidance control module starts to work from the moment until the obstacle avoidance module stops working, and the speeds of all the moments collected between the two moments form an obstacle avoidance sequence, so that the unmanned vehicle can form obstacle avoidance sequence at the driving data
And removing all obstacle avoidance sequences, so that the driving data is divided into a plurality of sequences, each sequence is an obstacle-free driving segment, and K obstacle-free driving segments are obtained.
For example, in the case of already-driven data
The obstacle avoidance sequence is removed, the original driving data is broken into two sequences, namely a sequence before the obstacle avoidance sequence and a sequence after the obstacle avoidance sequence, and the two sequences are obstacle-free driving fragments.
2. And acquiring a standard segment of the barrier-free driving segment.
For each section of barrier-free driving segment, acquiring a corresponding starting time and a corresponding ending time, and acquiring a first driving route of the unmanned vehicle before the starting time and a second driving route before the ending time; and selecting the moment with the minimum difference from the first driving distance as a standard starting moment and selecting the moment with the minimum difference from the second driving distance as a standard ending moment in each standard case, wherein all speeds between the standard starting moment and the standard ending moment in each standard case form a standard segment corresponding to the barrier-free driving segment.
To be provided with
Representing one obstacle-free travel segment in the traveled data as an example, the data is represented as:
wherein the content of the first and second substances,
indicating that the unmanned vehicle is at the start time
The speed of the time of flight or flight of the flight,
indicating that the unmanned vehicle is
The speed of the time-of-flight,
indicating that the unmanned vehicle is at the end time
The velocity of the time.
At the starting moment
The position of the driverless vehicle in the route section is the distance traveled by the driverless vehicle, i.e. the time of entry into the route section
To the starting moment
Integral operation is carried out on all the speeds, and the obtained result is the first driving distance, so that
And (4) showing.
In the same way, for the moment of entering the road section
To the end of time
Integral operation is carried out on all the speeds to obtain a second driving distance
。
For the ith standard case
Find the AND therein
Corresponding segment
The specific process is as follows:
calculating the distance of the ith vehicle of the same type at each moment by the same method as the first driving distance, and selecting the first driving distance
The time corresponding to the distance with the smallest difference
And a second driving distance
The time corresponding to the distance with the smallest difference
Then the barrier-free driving section
In the ith Standard case
The standard fragment of (A) is
。
It should be noted that, in the following description,
length of the sequence of
The length of the sequence(s) can be different, and two sequences with different lengths are likely to be in the actual calculation process.
The same method obtains the barrier-free driving segment
Standard fraction on each standard case, each barrier-free driving sheetThe segments correspond to M standard fragments.
3. And acquiring the sequence distance between the barrier-free driving segment and each standard segment.
And acquiring the distance between the barrier-free driving segment and each standard segment by using a dynamic time warping algorithm as a sequence distance.
Also with unobstructed driving segments
For example, the calculation is performed by using a dynamic time warping algorithm (DTW)
The DTW distance from each corresponding standard fragment is taken as the sequence distance.
The shorter the sequence distance, the more similar the two sequences are.
It should be noted that the sequence distance is a difference distance between two sequences with different lengths, and other methods for calculating a difference between two sequences with different lengths may also be used, such as existing algorithms of DDTW, WDTW, etc. for improving DTW.
And S003, acquiring the real-time running distance of the unmanned vehicle at the current moment, selecting the speed with the minimum difference with the real-time running distance as a candidate speed in each standard case, and calculating the absolute value of the difference between each candidate speed and the speed of the unmanned vehicle at the current moment.
The method comprises the following specific steps:
1. and acquiring the real-time driving distance of the unmanned vehicle at the current moment.
At the current moment
The position of the driverless vehicle in the route section should be the distance traveled by the driverless vehicle, i.e. the time of entry into the route section
To the current moment
Integral operation is carried out on all the speeds, and the obtained result is the real-time driving distance and is recorded as
。
2. And acquiring the candidate speed in each standard case.
In each standard case, the current time is searched
The time of the most similar walking section, namely, the selected real-time driving distance in each standard case
The moment corresponding to the distance with the smallest difference
In the ith standard case
Corresponding speed
I.e. the candidate speed.
All candidate speeds are obtained in the same way:
，
there are M candidate speeds at the current time.
3. The absolute value of the difference between each candidate speed and the speed of the unmanned vehicle at the current time is calculated.
Calculating the speed of each candidate speed and the unmanned vehicle at the current moment
The absolute value of the difference between them is recorded as
， 
。
The smaller the absolute value of the difference is, the closer the candidate speed is to the speed at the current moment, and the candidate speed is taken as the expected speed, so that the speed amount required to be regulated and controlled by the unmanned vehicle is less.
And step S004, acquiring the overall difference between each standard case and the driving data based on the sequence distance, calculating the product of the overall difference corresponding to each standard case and the absolute value of the difference, taking the candidate speed of the standard case with the minimum product as the expected speed at the current moment, and regulating and controlling the speed of the unmanned automobile based on the expected speed.
The method comprises the following specific steps:
1. the overall difference between each standard case and the traveled data is calculated.
For each standard case, acquiring the sequence distances of all barrier-free driving segments in the standard segments corresponding to the standard case, wherein the sum of all the sequence distances is the overall difference.
The DTW distance between the Kth barrier-free driving section and the ith standard section is recorded as
Then, the overall difference of the ith standard case is:
and K is the number of the barrier-free driving segments.
Global difference
The smaller the standard case is, the more similar the corresponding standard case is to the obstacle-free driving section on the driving data, and the standard case is used for indicating the similarityThe candidate speed of the quasi-case is used as the expected speed, so that the unmanned vehicle can be better kept to continue to run without obstacles.
2. And acquiring the expected speed at the current moment, and regulating and controlling the speed of the unmanned automobile based on the expected speed.
Computing
，
. The candidate speed corresponding to the minimum product value is selected as the desired speed at the current time, and in this case, the speed of the vehicle can be adjusted as little as possible while maintaining the traveling state of the vehicle.
According to the expected speed of the unmanned vehicle at the current moment and the actual speed of the unmanned vehicle at the current moment
And carrying out fuzzy PID control on the speed of the unmanned vehicle, controlling the acceleration of the unmanned vehicle by using a fuzzy PID method, and regulating the speed of the unmanned vehicle to an expected speed.
If the speed of the unmanned vehicle is not regulated to the expected speed in the regulation process, the next time is changed into a new current time when the next time is reached, the new expected speed of the new current time is recalculated according to the actual speed at the time, the speed of the unmanned vehicle is controlled to the new expected speed, the control process is repeated until the unmanned vehicle exits the road section and enters the next road section, the road section database is updated, and the speed control of the unmanned vehicle in the new road section is carried out again.
In summary, in the embodiment of the present invention, on a road section where an unmanned vehicle is currently traveling, a historical speed sequence of vehicles of the same type traveling without obstacles is obtained as a standard case; acquiring all speeds of the unmanned vehicle from the time of entering a road section to the current time to form driving data; screening a plurality of sections of barrier-free driving sections from the driving data based on the driving mode of the unmanned vehicle, and acquiring a standard section corresponding to the barrier-free driving section in each standard case; respectively obtaining sequence distances between the barrier-free driving fragments and each standard fragment; acquiring a real-time running distance of the unmanned vehicle at the current moment, selecting a speed with the minimum difference with the real-time running distance from each standard case as a candidate speed, and calculating the absolute value of the difference between each candidate speed and the speed of the unmanned vehicle at the current moment; and acquiring the overall difference between each standard case and the running data based on the sequence distance, calculating the product of the overall difference corresponding to each standard case and the absolute value of the difference, taking the candidate speed of the standard case with the minimum product as the expected speed at the current moment, and regulating the speed of the unmanned automobile based on the expected speed. The embodiment of the invention can enable the unmanned vehicle to adaptively control the speed at any moment, can flexibly adjust the driving speed in case of emergency, and has real-time property.
The embodiment of the invention also provides a big data-based unmanned speed control device, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps when executing the computer program. Since the detailed description of the unmanned speed control method based on big data is given above, the detailed description is omitted.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts in the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; modifications of the technical solutions described in the foregoing embodiments, or equivalents of some technical features thereof, are not essential to the spirit of the technical solutions of the embodiments of the present application, and are all included in the scope of the present application.
Claims (9)
1. An unmanned speed control method based on big data is characterized by comprising the following steps:
acquiring a historical speed sequence of vehicles of the same type running without obstacles on a current running road section of the unmanned vehicle as a standard case; acquiring all speeds of the unmanned vehicle from the moment of entering a road section to the current moment to form driving data;
screening a plurality of sections of barrier-free driving sections from the driving data based on the driving mode of the unmanned vehicle, and acquiring a standard section corresponding to the barrier-free driving section in each standard case; respectively obtaining sequence distances between the barrier-free driving fragments and each standard fragment;
acquiring a real-time running distance of the unmanned vehicle at the current moment, selecting a speed with the minimum difference with the real-time running distance from each standard case as a candidate speed, and calculating the absolute value of the difference between each candidate speed and the speed of the unmanned vehicle at the current moment;
and acquiring the overall difference between each standard case and the running data based on the sequence distance, calculating the product of the overall difference corresponding to each standard case and the absolute value of the difference, taking the candidate speed of the standard case with the minimum product as the expected speed at the current moment, and regulating the speed of the unmanned automobile based on the expected speed.
2. The big data-based unmanned speed control method as claimed in claim 1, wherein the standard case is obtained by:
and acquiring the same type of vehicles on the road section, which are the same as the type of the unmanned vehicles, through the big data, and recording the speed of each time in the process from the time of entering the road section to the time of leaving the road section for each vehicle of the same type running without obstacles to form the historical speed sequence, namely the historical speed sequence is the standard case.
3. The big data-based unmanned speed control method according to claim 1, wherein the screening process of the barrier-free driving segment is:
and forming an obstacle avoidance sequence from all speeds in the process from the start to the stop of an obstacle avoidance control module of the unmanned vehicle, extracting all the obstacle avoidance sequences and removing the obstacle avoidance sequences, so that the running data is cut into a plurality of sequences, and each sequence is the obstacle-free running segment.
4. The big data-based unmanned speed control method according to claim 1, wherein the step of obtaining the standard segment comprises:
for each section of barrier-free driving segment, acquiring a corresponding starting time and a corresponding ending time, and acquiring a first driving route of the unmanned vehicle before the starting time and a second driving route before the ending time;
and selecting the moment with the minimum difference with the first driving distance as a standard starting moment and selecting the moment with the minimum difference with the second driving distance as a standard ending moment in each standard case, wherein all speeds between the standard starting moment and the standard ending moment in each standard case form a standard segment corresponding to the barrier-free driving segment.
5. The big data-based unmanned speed control method according to claim 4, wherein the first travel distance is obtained by:
and performing integral operation on all speeds between the moment of entering the road section and the starting moment, wherein the obtained result is the first driving route.
6. The big data-based unmanned speed control method according to claim 1, wherein the sequence distance is obtained by:
and acquiring the distance between the barrier-free running segment and each standard segment by using a dynamic time warping algorithm as the sequence distance.
7. The big data-based unmanned speed control method according to claim 1, wherein the real-time driving distance is obtained by:
and performing integral operation on all the speeds between the road entering moment and the current moment to obtain a result, namely the real-time driving distance.
8. The big data-based unmanned speed control method according to claim 1, wherein the overall difference is obtained by:
and for each standard case, acquiring the sequence distances of all the barrier-free driving segments in the standard segments corresponding to the standard case, wherein the sum of all the sequence distances is the overall difference.
9. A big-data based unmanned speed control device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, wherein said processor when executing said computer program implements the steps of a big-data based unmanned speed control method as claimed in any of claims 1~8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211133821.3A CN115195791B (en) | 2022-09-19 | 2022-09-19 | Unmanned driving speed control method and device based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
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