CN116295343A - Fusion positioning method and device for automatic driving vehicle and electronic equipment - Google Patents

Fusion positioning method and device for automatic driving vehicle and electronic equipment Download PDF

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Publication number
CN116295343A
CN116295343A CN202310248351.3A CN202310248351A CN116295343A CN 116295343 A CN116295343 A CN 116295343A CN 202310248351 A CN202310248351 A CN 202310248351A CN 116295343 A CN116295343 A CN 116295343A
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positioning data
positioning
fusion
confidence coefficient
laser
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李岩
万如
费再慧
张海强
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Zhidao Network Technology Beijing Co Ltd
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Zhidao Network Technology Beijing Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Electromagnetism (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Navigation (AREA)

Abstract

The application discloses a fusion positioning method, a fusion positioning device and electronic equipment of an automatic driving vehicle, wherein the method comprises the following steps: acquiring positioning data of a plurality of sensors of an automatic driving vehicle, wherein the positioning data comprise satellite positioning data, laser positioning data and visual positioning data; determining a confidence coefficient type corresponding to the satellite positioning data and a confidence coefficient type corresponding to the laser positioning data by using a preset confidence coefficient threshold value condition; determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data and the visual positioning data; and carrying out fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain the fusion positioning result of the automatic driving vehicle. The method and the device perform mutual verification of the confidence coefficient based on the positioning data of the plurality of sensors, adopt different fusion positioning strategies according to verification results, ensure reliable observation input of a fusion positioning algorithm, and improve the stability and positioning accuracy of fusion positioning.

Description

Fusion positioning method and device for automatic driving vehicle and electronic equipment
Technical Field
The application relates to the technical field of automatic driving, in particular to a fusion positioning method and device for an automatic driving vehicle and electronic equipment.
Background
As the cost of the hardware associated with an autonomous vehicle has become lower, high performance sensors such as multi-line lidar, high resolution cameras, high performance on-board computing units, etc. have become increasingly considered and incorporated into the hardware suite of an autonomous vehicle. Correspondingly, the conventional integrated navigation positioning system such as IMU (Inertial Measurement Unit ) +gnss (Global Navigation Satellite System, global satellite navigation system)/RTK (Real-time kinematic) has also been replaced by multiple sensor fusion positioning such as schemes including laser positioning and lane matching positioning, so as to ensure that in the case of RTK failure, the autonomous vehicle has an additional observation source to ensure positioning accuracy and stability.
At present, most multi-sensor fusion positioning schemes can select a scheme of fusing IMU+GNSS/RTK+laser SLAM (Simultaneous Localization And Mapping, synchronous positioning and mapping) by using a laser positioning substitution or auxiliary combined navigation positioning system to optimize an observed value under the condition that RTK positioning signals are not good so as to ensure long-time stability of integral positioning.
However, if an inaccurate positioning confidence provided by the observation source is encountered, for example, at a certain moment, the GNSS/RTK gives a low confidence positioning result with correct position, and the laser positioning gives a high confidence positioning result with incorrect position, direct fusion may deviate the final positioning of the autonomous vehicle from the lane, resulting in reduced positioning accuracy and positioning stability.
Disclosure of Invention
The embodiment of the application provides a fusion positioning method and device for an automatic driving vehicle and electronic equipment, so as to improve the fusion positioning stability and positioning accuracy of the automatic driving vehicle.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a fusion positioning method for an autopilot vehicle, where the method includes:
acquiring positioning data of a plurality of sensors of an autonomous vehicle, wherein the positioning data comprises satellite positioning data, laser positioning data and visual positioning data;
determining a confidence coefficient type corresponding to the satellite positioning data and a confidence coefficient type corresponding to the laser positioning data by using a preset confidence coefficient threshold condition;
determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data and the visual positioning data;
And carrying out fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle.
Optionally, the determining the confidence type corresponding to the satellite positioning data and the confidence type corresponding to the laser positioning data by using a preset confidence threshold condition includes:
comparing the confidence coefficient corresponding to the satellite positioning data with a first preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the satellite positioning data;
and comparing the confidence coefficient corresponding to the laser positioning data with a second preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the laser positioning data.
Optionally, the determining the fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data and the visual positioning data includes:
determining a positioning error between the satellite positioning data and the laser positioning data according to the satellite positioning data and the laser positioning data;
and determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data and the visual positioning data.
Optionally, the determining the fusion positioning strategy of the autopilot vehicle according to the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data, and the visual positioning data includes:
if at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is smaller than a preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data and/or the laser positioning data;
if at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is not smaller than the preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data, the laser positioning data and the visual positioning data;
And if the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data are both low confidence coefficients, determining that the fusion positioning strategy is the fusion positioning strategy realized based on the visual positioning data.
Optionally, the fusion positioning strategy is a fusion positioning strategy implemented based on the satellite positioning data and/or the laser positioning data, and the performing fusion positioning according to the fusion positioning strategy of the automatic driving vehicle, to obtain a fusion positioning result of the automatic driving vehicle includes:
fusion positioning is carried out according to the satellite positioning data; or alternatively, the process may be performed,
fusion positioning is carried out according to the laser positioning data; or alternatively, the process may be performed,
determining the fusion weight of the satellite positioning data according to the confidence coefficient corresponding to the satellite positioning data, determining the fusion weight of the laser positioning data according to the confidence coefficient corresponding to the laser positioning data, and carrying out fusion positioning according to the fusion weight of the satellite positioning data and the fusion weight of the laser positioning data.
Optionally, the fusion positioning strategy is a fusion positioning strategy implemented based on the satellite positioning data, the laser positioning data and the vision positioning data, and the performing fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle includes:
Determining validity of the visual positioning data;
under the condition that the visual positioning data are valid, determining positioning errors of the satellite positioning data and the visual positioning data and positioning errors of the laser positioning data and the visual positioning data respectively;
updating the confidence coefficient corresponding to the satellite positioning data and the confidence coefficient corresponding to the laser positioning data according to the positioning errors of the satellite positioning data and the visual positioning data and the positioning errors of the laser positioning data and the visual positioning data;
and carrying out fusion positioning according to the confidence coefficient corresponding to the updated satellite positioning data and the confidence coefficient corresponding to the updated laser positioning data to obtain a fusion positioning result of the automatic driving vehicle.
Optionally, the visual positioning data includes a lane line lateral positioning position at a current time, and the determining the validity of the visual positioning data includes:
acquiring the course angle change rate of the automatic driving vehicle, and determining a course angle reference value according to the course angle change rate;
determining a course angle at the current moment according to the lane line transverse positioning position at the current moment and the fusion positioning result at the last moment;
And comparing the course angle at the current moment with the course angle reference value, and determining the validity of the visual positioning data according to a comparison result.
In a second aspect, embodiments of the present application further provide a fusion positioning device for an autonomous vehicle, where the device includes:
an acquisition unit for acquiring positioning data of a plurality of sensors of an autonomous vehicle, the positioning data including satellite positioning data, laser positioning data, and visual positioning data;
the first determining unit is used for determining the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data by utilizing a preset confidence coefficient threshold value condition;
the second determining unit is used for determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data and the visual positioning data;
and the fusion positioning unit is used for carrying out fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle.
In a third aspect, embodiments of the present application further provide an electronic device, including:
A processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform any of the methods described hereinbefore.
In a fourth aspect, embodiments of the present application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform any of the methods described above.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect: according to the fusion positioning method of the automatic driving vehicle, positioning data of a plurality of sensors of the automatic driving vehicle are acquired firstly, wherein the positioning data comprise satellite positioning data, laser positioning data and visual positioning data; then determining the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data by using a preset confidence coefficient threshold value condition; then determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data and the visual positioning data; and finally, carrying out fusion positioning according to a fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle. According to the fusion positioning method for the automatic driving vehicle, the mutual verification of the confidence coefficient is carried out based on the positioning data of the plurality of sensors, different fusion positioning strategies are adopted according to the verification result, reliable observation input of a fusion positioning algorithm is guaranteed, and the stability and positioning accuracy of fusion positioning are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a flow chart of a fusion positioning method of an automatic driving vehicle in an embodiment of the application;
FIG. 2 is a schematic structural diagram of a fusion positioning device for an autonomous vehicle according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
The embodiment of the application provides a fusion positioning method of an automatic driving vehicle, as shown in fig. 1, and provides a flow chart of the fusion positioning method of the automatic driving vehicle in the embodiment of the application, where the method at least includes the following steps S110 to S140:
step S110, positioning data of a plurality of sensors of the autonomous vehicle is acquired, the positioning data including satellite positioning data, laser positioning data, and visual positioning data.
When fusion positioning of an automatic driving vehicle is performed, positioning data output by a plurality of sensors of the automatic driving vehicle need to be acquired first, and the positioning data can specifically comprise satellite positioning data output by an IMU+GNSS/RTK observation source, laser positioning data output by a laser SLAM and visual positioning data output by visual.
Because the output frequencies of different sensors are different and are affected by external environments, the positioning results output by a plurality of sensors are not necessarily received at the same time at a certain observation updating moment, and therefore the embodiment of the application can synchronize time and frequency of satellite positioning data, laser positioning data and vision positioning data, and ensures that positioning data of three observation sources enter at the same time at the observation updating moment. Specifically, a low-frequency observation source such as a GNSS/RTK may be used as a reference, and with respect to the other two observation sources, the frequency is low and stable, and it is determined whether there are positioning results of other sensors corresponding to the GNSS/RTK within a preset buffer time range, and if there are positioning results of other sensors corresponding to the GNSS/RTK, the positioning results of other sensors may be obtained by predicting using vehicle speed information and a time interval.
Step S120, determining the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data by using a preset confidence coefficient threshold condition.
The satellite positioning data output by the GNSS/RTK can comprise the positioning confidence coefficient of the satellite positioning data, the laser positioning data output by the laser SLAM can also comprise the positioning confidence coefficient of the laser positioning data, so that the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data, such as high confidence coefficient or low confidence coefficient, can be respectively judged by using different preset confidence coefficient threshold conditions.
Because the GNSS/RTK may output a low confidence positioning result with correct position at some time, and the laser positioning may output a high confidence positioning result with incorrect position, the confidence type corresponding to the satellite positioning data and the confidence type corresponding to the laser positioning data may provide a basis for mutual verification of the confidence of the satellite positioning data and the laser positioning data based on the above determination.
And step S130, determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data and the visual positioning data.
When the reliability of the positioning results of the satellite positioning and the laser SLAM cannot be accurately determined based on the confidence coefficient type of the satellite positioning data and the confidence coefficient type of the laser positioning data, the visual positioning data can be further combined for auxiliary verification, so that the reliability of the positioning results of different sensors is determined according to the verification results, and different fusion positioning strategies are adopted.
And step S140, performing fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle.
The fusion positioning strategy is to perform fusion positioning based on the positioning data of the current reliable sensor, so that the positioning data of the corresponding sensor can be input into a Kalman filter for fusion positioning according to the fusion positioning strategy, and a fusion positioning result of the automatic driving vehicle is obtained.
According to the fusion positioning method for the automatic driving vehicle, the mutual verification of the confidence coefficient is carried out based on the positioning data of the plurality of sensors, different fusion positioning strategies are adopted according to the verification result, reliable observation input of a fusion positioning algorithm is guaranteed, and the stability and positioning accuracy of fusion positioning are improved.
In some embodiments of the present application, the determining the confidence type corresponding to the satellite positioning data and the confidence type corresponding to the laser positioning data using a preset confidence threshold condition includes: comparing the confidence coefficient corresponding to the satellite positioning data with a first preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the satellite positioning data; and comparing the confidence coefficient corresponding to the laser positioning data with a second preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the laser positioning data.
Because the positioning accuracy requirements corresponding to the satellite positioning and the laser SLAM positioning are different, different confidence thresholds can be set respectively, for example, a first preset confidence threshold is set for satellite positioning data, a second preset confidence threshold is set for laser positioning data, and the size of the first preset confidence threshold and the size of the second preset confidence threshold mainly depend on the positioning accuracy requirements, so that the positioning accuracy requirements can be flexibly set according to requirements, and the positioning accuracy requirements are not particularly limited.
Comparing the confidence coefficient of the satellite positioning data with a corresponding first preset confidence coefficient threshold value, if the confidence coefficient of the satellite positioning data is larger than the first preset confidence coefficient threshold value, determining that the confidence coefficient type of the satellite positioning data is high confidence coefficient, otherwise, determining that the confidence coefficient type of the satellite positioning data is low confidence coefficient. Comparing the confidence coefficient of the laser positioning data with a corresponding second preset confidence coefficient threshold value, if the confidence coefficient of the laser positioning data is larger than the second preset confidence coefficient threshold value, determining that the confidence coefficient type of the laser positioning data is high confidence coefficient, otherwise, determining that the confidence coefficient type of the laser positioning data is low confidence coefficient.
In some embodiments of the present application, the determining the fused positioning strategy of the autonomous vehicle according to the confidence level type corresponding to the satellite positioning data and the confidence level type corresponding to the laser positioning data and the visual positioning data includes: determining a positioning error between the satellite positioning data and the laser positioning data according to the satellite positioning data and the laser positioning data; and determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data and the visual positioning data.
When mutual verification is carried out on the confidence coefficient of the satellite positioning data and the laser positioning data, positioning errors between the satellite positioning data and the laser positioning data can be further calculated, if the positioning errors between the satellite positioning data and the laser positioning data are smaller than a certain error threshold value, the positioning results of the satellite positioning and the laser SLAM can be considered to be reliable, namely, the positioning accuracy requirements are met, and the situation that the positioning errors between the satellite positioning data and the laser positioning data are smaller than the error threshold value only occurs in the case that the positioning results of the satellite positioning data and the laser positioning data are accurate can occur.
Therefore, the reliability of the positioning result of each sensor can be finally determined based on the magnitude of the positioning error between the satellite positioning data and the laser positioning data, the confidence type of the satellite positioning data, the confidence type of the laser positioning data and the visual positioning data, and then the corresponding fusion positioning strategy is determined.
In some embodiments of the present application, the determining the fused positioning strategy of the autonomous vehicle according to the confidence level type corresponding to the satellite positioning data and the confidence level type corresponding to the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data, and the visual positioning data includes: if at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is smaller than a preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data and/or the laser positioning data; if at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is not smaller than the preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data, the laser positioning data and the visual positioning data; and if the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data are both low confidence coefficients, determining that the fusion positioning strategy is the fusion positioning strategy realized based on the visual positioning data.
When determining a fusion positioning strategy of an automatic driving vehicle according to a confidence coefficient type corresponding to satellite positioning data, a confidence coefficient type corresponding to laser positioning data, a positioning error between the satellite positioning data and the laser positioning data and visual positioning data, the method can be mainly divided into the following cases:
(1) The confidence coefficient type of the satellite positioning data and the confidence coefficient type of the laser positioning data are both high confidence coefficients, and considering the situation that satellite positioning or laser SLAM positioning can output a high confidence coefficient positioning result with a position error, the method can further judge according to the positioning error between the satellite positioning data and the laser positioning data:
1) The positioning error between the satellite positioning data and the laser positioning data is smaller than a preset positioning error threshold value, the confidence coefficient type of the satellite positioning data and the confidence coefficient type of the laser positioning data can be basically considered to be accurate, and the positioning results of the satellite positioning data and the laser positioning data can be judged to be effective observation results, so that the fusion positioning strategy can be any one of the positioning results or weighted fusion can be carried out according to the confidence coefficient.
2) The positioning error between the satellite positioning data and the laser positioning data is not smaller than a preset positioning error threshold value, which indicates that the confidence degree type of at least one of the satellite positioning data and the laser positioning data is unreliable, and the auxiliary verification can be further carried out by combining with the visual positioning data, so that the corresponding fusion positioning strategy is determined according to the verification result.
(2) One of the confidence coefficient type of the satellite positioning data and the confidence coefficient type of the laser positioning data is low confidence coefficient, and the situation that satellite positioning or laser SLAM positioning possibly outputs a low-confidence coefficient positioning result with correct position or a high-confidence coefficient positioning result with incorrect position is considered, so that the judgment can be further carried out according to the positioning error between the satellite positioning data and the laser positioning data:
1) The positioning error between the satellite positioning data and the laser positioning data is smaller than a preset positioning error threshold value, and the condition basically can be considered that the sensor corresponding to the low confidence type outputs a positioning result with correct position and low confidence, namely the positioning result is reliable, so that the fusion positioning strategy is the same as that of the (1) -1), namely any one of the positioning strategies is selected or weighted fusion is carried out according to the confidence.
2) The positioning error between the satellite positioning data and the laser positioning data is not smaller than the preset positioning error threshold value, the reliability of the satellite positioning data and the laser positioning data cannot be directly determined under the condition, and the auxiliary verification can be further carried out by combining the visual positioning data, so that the corresponding fusion positioning strategy is determined according to the verification result.
(3) The confidence level type of the satellite positioning data and the confidence level type of the laser positioning data are both low confidence levels, the situation can basically consider that the positioning results of the satellite positioning and the laser SLAM positioning are invalid observation results, fusion positioning can be realized based on the visual positioning data, the validity of the visual positioning data can be judged firstly when the fusion positioning is realized based on the visual positioning data, lane keeping is carried out by using the transverse positioning positions matched with the lane lines under the condition that the visual positioning data are valid, the positioning results at the subsequent N moments are judged, and the lane keeping is closed and normal fusion positioning output is carried out when the GNSS/RTK and the laser positioning meet the conditions of (1) or (2).
In some embodiments of the present application, the fusion positioning strategy is a fusion positioning strategy implemented based on the satellite positioning data and/or the laser positioning data, and the performing fusion positioning according to the fusion positioning strategy of the autopilot vehicle, to obtain a fusion positioning result of the autopilot vehicle includes: fusion positioning is carried out according to the satellite positioning data; or carrying out fusion positioning according to the laser positioning data; or determining the fusion weight of the satellite positioning data according to the confidence coefficient corresponding to the satellite positioning data, determining the fusion weight of the laser positioning data according to the confidence coefficient corresponding to the laser positioning data, and carrying out fusion positioning according to the fusion weight of the satellite positioning data and the fusion weight of the laser positioning data.
If the fusion positioning strategy is a fusion positioning strategy realized based on satellite positioning data and/or laser positioning data, the satellite positioning data and the laser positioning data are both effective observation results, so that any one of the satellite positioning data and the laser positioning data can be selected as additional observation information to be input into a Kalman filter for fusion positioning, and corresponding fusion weights can be respectively determined according to the confidence degrees of the satellite positioning data and/or the laser positioning data to carry out weighted fusion, namely, the higher the confidence degree is, the larger the corresponding fusion weight value is.
In some embodiments of the present application, the fusion positioning strategy is a fusion positioning strategy implemented based on the satellite positioning data, the laser positioning data and the vision positioning data, and the performing fusion positioning according to the fusion positioning strategy of the autopilot vehicle, to obtain a fusion positioning result of the autopilot vehicle includes: determining validity of the visual positioning data; under the condition that the visual positioning data are valid, determining positioning errors of the satellite positioning data and the visual positioning data and positioning errors of the laser positioning data and the visual positioning data respectively; updating the confidence coefficient corresponding to the satellite positioning data and the confidence coefficient corresponding to the laser positioning data according to the positioning errors of the satellite positioning data and the visual positioning data and the positioning errors of the laser positioning data and the visual positioning data; and carrying out fusion positioning according to the confidence coefficient corresponding to the updated satellite positioning data and the confidence coefficient corresponding to the updated laser positioning data to obtain a fusion positioning result of the automatic driving vehicle.
If the fusion positioning strategy is a fusion positioning strategy realized based on satellite positioning data, laser positioning data and visual positioning data, because the positioning result in the visual positioning data may also have unreliable conditions, the validity of the visual positioning data can be determined first when the confidence level of satellite positioning and laser SLAM positioning is verified by utilizing the visual positioning data in an auxiliary way, and under the condition that the visual positioning data is valid, the positioning errors of the satellite positioning data and the visual positioning data and the positioning errors of the laser positioning data and the visual positioning data are calculated respectively.
It should be noted that, because the visual positioning data is mainly a lane line transverse positioning position, the satellite positioning data and the laser positioning data can be decomposed into transverse positioning positions, so as to calculate and obtain the transverse positioning errors of the satellite positioning data and the visual positioning data and the transverse positioning errors of the laser positioning data and the visual positioning data.
And comparing the transverse positioning error between the satellite positioning data and the visual positioning data with the transverse positioning error between the laser positioning data and the visual positioning data, determining which one of the satellite positioning data and the visual positioning data is closer to the transverse positioning position of the visual positioning data according to the comparison result, namely, the transverse positioning error is smaller, and the closer instruction positioning data is more reliable, so that the confidence coefficient corresponding to the satellite positioning data and the visual positioning data can be recalculated, namely, the confidence coefficient correction is carried out, and finally, the weighted fusion is carried out according to the recalculated confidence coefficient, thereby ensuring that a fusion positioning algorithm can use more reliable observation input, and improving the fusion positioning precision and the positioning stability.
For ease of understanding, one way to recalculate the confidence is provided herein, which may be expressed, for example, as follows:
Conf_GNSS = 1 - D_GNSS/（D_GNSS + D_lidar）
Conf_lidar = 1 - Conf_GNSS
wherein, d_gnss is a lateral positioning error between satellite positioning data and visual positioning data, d_lidar is a lateral positioning error between laser positioning data and visual positioning data, conf_gnss is a confidence level corresponding to updated satellite positioning data, and conf_lidar is a confidence level corresponding to updated laser positioning data.
Of course, how to recalculate the confidence coefficient specifically, the person skilled in the art can flexibly set according to the actual requirement, so long as the smaller the transverse positioning error is, the higher the corresponding confidence coefficient is.
It should be noted that, although the confidence coefficient corresponding to the updated satellite positioning data and the confidence coefficient corresponding to the updated laser positioning data are calculated based on the transverse positioning position error, the method can be applied to weighted fusion of the longitudinal positioning positions in the fusion positioning process, that is, the transverse positioning positions and the longitudinal positioning positions use the same weight, so as to reduce the probability of lane keeping.
In some embodiments of the present application, the visual positioning data includes a lane line lateral positioning position at a current time, and the determining the validity of the visual positioning data includes: acquiring the course angle change rate of the automatic driving vehicle, and determining a course angle reference value according to the course angle change rate; determining a course angle at the current moment according to the lane line transverse positioning position at the current moment and the fusion positioning result at the last moment; and comparing the course angle at the current moment with the course angle reference value, and determining the validity of the visual positioning data according to a comparison result.
When the validity of the visual positioning data is determined, the change rate (yawrate) of the course angle output by the automatic driving vehicle can be firstly obtained, then the course angle reference value is calculated through integration, then the course angle of the current moment is calculated according to the transverse positioning position of the lane line at the current moment and the transverse positioning position decomposed in the fusion positioning result of the last moment, the course angle of the current moment is compared with the course angle reference value, if the deviation between the two is within a certain deviation range, the fact that the transverse positioning position of the automatic driving vehicle at the current moment does not deviate from the lane is indicated, the transverse positioning position of the lane line is reliable, and the method can be used as the basis for auxiliary verification of the confidence coefficient of the satellite positioning data and the laser positioning data. If the deviation between the two is beyond a certain deviation range, the current transverse positioning position of the lane line is possibly deviated from the lane, the transverse positioning position is unreliable and cannot be used as a basis for auxiliary verification, and at the moment, an alarm can be further triggered, so that manual timely intervention is facilitated.
According to the method and the device for judging the validity of the lane line matching, the course angle change rate is used as additional auxiliary information, so that validity judgment is further conducted on the lane line matching result, and errors caused by the lane line matching errors are reduced.
In some embodiments of the present application, the determining the fused positioning strategy of the autonomous vehicle according to the confidence level type corresponding to the satellite positioning data and the confidence level type corresponding to the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data, and the visual positioning data includes: if the visual positioning data cannot be obtained, the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data are not high confidence coefficients, and the positioning error between the satellite positioning data and the laser positioning data is not smaller than a preset positioning error threshold value, obtaining the course angle change rate of the automatic driving vehicle, and determining a course angle reference value according to the course angle change rate; determining a course angle corresponding to the satellite positioning data at the current moment according to the fusion positioning result of the satellite positioning data and the last moment, and determining a course angle corresponding to the laser positioning data at the current moment according to the fusion positioning result of the laser positioning data and the last moment; and comparing the course angle corresponding to the satellite positioning data at the current moment and the course angle corresponding to the laser positioning data at the current moment with the course angle reference value respectively, and determining the fusion positioning strategy of the automatic driving vehicle according to the comparison result.
In some special scenes such as intersections, rainy days and the like, situations may be encountered in which the lane lines cannot be identified and do not satisfy (1) -1 in the foregoing embodiment, and this situation may select an alarm stop or rely on the change rate of the heading angle for observation selection, for example, one may be selected and only the observation information closest to the change of the heading angle may be used for fusion positioning.
The embodiment of the application further provides a fusion positioning device 200 of an autopilot vehicle, as shown in fig. 2, and a schematic structural diagram of the fusion positioning device of the autopilot vehicle in the embodiment of the application is provided, where the device 200 includes:
an acquisition unit 210 for acquiring positioning data of a plurality of sensors of an autonomous vehicle, the positioning data including satellite positioning data, laser positioning data, and visual positioning data;
a first determining unit 220, configured to determine a confidence type corresponding to the satellite positioning data and a confidence type corresponding to the laser positioning data using a preset confidence threshold condition;
a second determining unit 230, configured to determine a fusion positioning policy of the autonomous vehicle according to the confidence level type corresponding to the satellite positioning data, the confidence level type corresponding to the laser positioning data, and the visual positioning data;
And the fusion positioning unit 240 is configured to perform fusion positioning according to the fusion positioning strategy of the autopilot vehicle, so as to obtain a fusion positioning result of the autopilot vehicle.
In some embodiments of the present application, the first determining unit 220 is specifically configured to: comparing the confidence coefficient corresponding to the satellite positioning data with a first preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the satellite positioning data; and comparing the confidence coefficient corresponding to the laser positioning data with a second preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the laser positioning data.
In some embodiments of the present application, the second determining unit 230 is specifically configured to: determining a positioning error between the satellite positioning data and the laser positioning data according to the satellite positioning data and the laser positioning data; and determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data and the visual positioning data.
In some embodiments of the present application, the second determining unit 230 is specifically configured to: if at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is smaller than a preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data and/or the laser positioning data; if at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is not smaller than the preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data, the laser positioning data and the visual positioning data; and if the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data are both low confidence coefficients, determining that the fusion positioning strategy is the fusion positioning strategy realized based on the visual positioning data.
In some embodiments of the present application, the fused positioning strategy is a fused positioning strategy implemented based on the satellite positioning data and/or the laser positioning data, and the fused positioning unit 240 is specifically configured to: fusion positioning is carried out according to the satellite positioning data; or carrying out fusion positioning according to the laser positioning data; or determining the fusion weight of the satellite positioning data according to the confidence coefficient corresponding to the satellite positioning data, determining the fusion weight of the laser positioning data according to the confidence coefficient corresponding to the laser positioning data, and carrying out fusion positioning according to the fusion weight of the satellite positioning data and the fusion weight of the laser positioning data.
In some embodiments of the present application, the fused positioning strategy is a fused positioning strategy implemented based on the satellite positioning data, the laser positioning data and the visual positioning data, and the fused positioning unit 240 is specifically configured to: determining validity of the visual positioning data; under the condition that the visual positioning data are valid, determining positioning errors of the satellite positioning data and the visual positioning data and positioning errors of the laser positioning data and the visual positioning data respectively; updating the confidence coefficient corresponding to the satellite positioning data and the confidence coefficient corresponding to the laser positioning data according to the positioning errors of the satellite positioning data and the visual positioning data and the positioning errors of the laser positioning data and the visual positioning data; and carrying out fusion positioning according to the confidence coefficient corresponding to the updated satellite positioning data and the confidence coefficient corresponding to the updated laser positioning data to obtain a fusion positioning result of the automatic driving vehicle.
In some embodiments of the present application, the visual positioning data includes a lane line lateral positioning position at the current time, and the fusion positioning unit 240 is specifically configured to: acquiring the course angle change rate of the automatic driving vehicle, and determining a course angle reference value according to the course angle change rate; determining a course angle at the current moment according to the lane line transverse positioning position at the current moment and the fusion positioning result at the last moment; and comparing the course angle at the current moment with the course angle reference value, and determining the validity of the visual positioning data according to a comparison result.
It can be understood that the above-mentioned fusion positioning device for an automatic driving vehicle can implement each step of the fusion positioning method for an automatic driving vehicle provided in the foregoing embodiment, and the relevant explanation about the fusion positioning method for an automatic driving vehicle is applicable to the fusion positioning device for an automatic driving vehicle, which is not described herein again.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 3, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 3, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the fusion positioning device of the automatic driving vehicle on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
acquiring positioning data of a plurality of sensors of an autonomous vehicle, wherein the positioning data comprises satellite positioning data, laser positioning data and visual positioning data;
Determining a confidence coefficient type corresponding to the satellite positioning data and a confidence coefficient type corresponding to the laser positioning data by using a preset confidence coefficient threshold condition;
determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data and the visual positioning data;
and carrying out fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle.
The method performed by the fusion positioning device of the autonomous vehicle disclosed in the embodiment shown in fig. 1 of the present application may be applied to a processor or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The electronic device may further execute the method executed by the fusion positioning device of the autopilot vehicle in fig. 1, and implement the function of the fusion positioning device of the autopilot vehicle in the embodiment shown in fig. 1, which is not described herein.
The embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by an electronic device that includes a plurality of application programs, enable the electronic device to perform a method performed by a fusion positioning device for an autonomous vehicle in the embodiment shown in fig. 1, and specifically are configured to perform:
acquiring positioning data of a plurality of sensors of an autonomous vehicle, wherein the positioning data comprises satellite positioning data, laser positioning data and visual positioning data;
determining a confidence coefficient type corresponding to the satellite positioning data and a confidence coefficient type corresponding to the laser positioning data by using a preset confidence coefficient threshold condition;
determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data and the visual positioning data;
And carrying out fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims (10)

1. A fusion positioning method of an autonomous vehicle, wherein the method comprises:
acquiring positioning data of a plurality of sensors of an autonomous vehicle, wherein the positioning data comprises satellite positioning data, laser positioning data and visual positioning data;
determining a confidence coefficient type corresponding to the satellite positioning data and a confidence coefficient type corresponding to the laser positioning data by using a preset confidence coefficient threshold condition;
Determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data and the visual positioning data;
and carrying out fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle.
2. The method of claim 1, wherein the determining the confidence type for the satellite positioning data and the confidence type for the laser positioning data using a preset confidence threshold condition comprises:
comparing the confidence coefficient corresponding to the satellite positioning data with a first preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the satellite positioning data;
and comparing the confidence coefficient corresponding to the laser positioning data with a second preset confidence coefficient threshold value to obtain a confidence coefficient type corresponding to the laser positioning data.
3. The method of claim 1, wherein the determining a fused positioning strategy for the autonomous vehicle based on the confidence type for the satellite positioning data and the confidence type for the laser positioning data and the visual positioning data comprises:
Determining a positioning error between the satellite positioning data and the laser positioning data according to the satellite positioning data and the laser positioning data;
and determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data and the visual positioning data.
4. The method of claim 3, wherein the determining a fused positioning strategy for the autonomous vehicle based on the confidence type for the satellite positioning data and the confidence type for the laser positioning data, the positioning error between the satellite positioning data and the laser positioning data, and the visual positioning data comprises:
if at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is smaller than a preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data and/or the laser positioning data;
If at least one of the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data is high confidence coefficient, and the positioning error between the satellite positioning data and the laser positioning data is not smaller than the preset positioning error threshold value, determining that the fusion positioning strategy is a fusion positioning strategy realized based on the satellite positioning data, the laser positioning data and the visual positioning data;
and if the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data are both low confidence coefficients, determining that the fusion positioning strategy is the fusion positioning strategy realized based on the visual positioning data.
5. The method of claim 1, wherein the fused positioning strategy is a fused positioning strategy implemented based on the satellite positioning data and/or the laser positioning data, and the performing fused positioning according to the fused positioning strategy of the autonomous vehicle to obtain a fused positioning result of the autonomous vehicle comprises:
fusion positioning is carried out according to the satellite positioning data; or alternatively, the process may be performed,
fusion positioning is carried out according to the laser positioning data; or alternatively, the process may be performed,
Determining the fusion weight of the satellite positioning data according to the confidence coefficient corresponding to the satellite positioning data, determining the fusion weight of the laser positioning data according to the confidence coefficient corresponding to the laser positioning data, and carrying out fusion positioning according to the fusion weight of the satellite positioning data and the fusion weight of the laser positioning data.
6. The method of claim 1, wherein the fused positioning strategy is a fused positioning strategy implemented based on the satellite positioning data, the laser positioning data, and the vision positioning data, and the performing fused positioning according to the fused positioning strategy of the autonomous vehicle, obtaining a fused positioning result of the autonomous vehicle includes:
determining validity of the visual positioning data;
under the condition that the visual positioning data are valid, determining positioning errors of the satellite positioning data and the visual positioning data and positioning errors of the laser positioning data and the visual positioning data respectively;
updating the confidence coefficient corresponding to the satellite positioning data and the confidence coefficient corresponding to the laser positioning data according to the positioning errors of the satellite positioning data and the visual positioning data and the positioning errors of the laser positioning data and the visual positioning data;
And carrying out fusion positioning according to the confidence coefficient corresponding to the updated satellite positioning data and the confidence coefficient corresponding to the updated laser positioning data to obtain a fusion positioning result of the automatic driving vehicle.
7. The method of claim 6, wherein the visual positioning data comprises a lane line lateral positioning location at a current time, and wherein determining the validity of the visual positioning data comprises:
acquiring the course angle change rate of the automatic driving vehicle, and determining a course angle reference value according to the course angle change rate;
determining a course angle at the current moment according to the lane line transverse positioning position at the current moment and the fusion positioning result at the last moment;
and comparing the course angle at the current moment with the course angle reference value, and determining the validity of the visual positioning data according to a comparison result.
8. A fusion positioning device for an autonomous vehicle, wherein the device comprises:
an acquisition unit for acquiring positioning data of a plurality of sensors of an autonomous vehicle, the positioning data including satellite positioning data, laser positioning data, and visual positioning data;
the first determining unit is used for determining the confidence coefficient type corresponding to the satellite positioning data and the confidence coefficient type corresponding to the laser positioning data by utilizing a preset confidence coefficient threshold value condition;
The second determining unit is used for determining a fusion positioning strategy of the automatic driving vehicle according to the confidence coefficient type corresponding to the satellite positioning data, the confidence coefficient type corresponding to the laser positioning data and the visual positioning data;
and the fusion positioning unit is used for carrying out fusion positioning according to the fusion positioning strategy of the automatic driving vehicle to obtain a fusion positioning result of the automatic driving vehicle.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium storing one or more programs, which when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.
CN202310248351.3A 2023-03-15 2023-03-15 Fusion positioning method and device for automatic driving vehicle and electronic equipment Pending CN116295343A (en)

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Application Number Priority Date Filing Date Title
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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310248351.3A CN116295343A (en) 2023-03-15 2023-03-15 Fusion positioning method and device for automatic driving vehicle and electronic equipment

Publications (1)

Publication Number Publication Date
CN116295343A true CN116295343A (en) 2023-06-23

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Country Link
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