False track identification method, system and related equipment based on millimeter wave radar

Abstract

The embodiments of the present application provide a method, system and related equipment for identifying false tracks based on millimeter-wave radar, which relate to the field of radar detection technology, wherein the method includes: determining a target identification area according to the driving state of the target vehicle and / or the driving road conditions when determining that the target time complexity is less than or equal to a first threshold value, and / or the target spatial complexity is less than or equal to a second threshold value; identifying the target false track according to the target track characteristics corresponding to the target identification area; wherein the false track ratio corresponding to the target identification area is greater than or equal to a third threshold value. The present application can improve the applicability to situations with low time complexity and / or spatial complexity, and is conducive to improving the recognition accuracy and efficiency of false tracks by retaining the original information of the data.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of radar detection technology, and in particular to a false track identification method, system, and related equipment based on millimeter-wave radar. Background Art

[0002] Millimeter-wave radar is widely used in environmental perception during vehicle driving and is very important for detecting and tracking road targets.

[0003] In related technologies, the identification of false tracks is mostly based on multi-sensor data fusion combined with confidence algorithms. The above method is prone to loss of original data information, resulting in a high risk of false track misjudgment.

[0004] Therefore, a new technical solution is urgently needed to solve the above technical problems. Summary of the Invention

[0005] According to the embodiments of the present application, a method, system and related equipment for false track identification based on millimeter-wave radar are provided, which can improve the applicability to scenarios with low time complexity and / or low spatial complexity, and is conducive to improving the recognition accuracy and efficiency of false tracks by retaining the original information of the data.

[0006] In a first aspect of the present application, a method for identifying false tracks based on millimeter-wave radar is provided, which is applicable to a single sensor and includes:

[0007] When it is determined that the target time complexity is less than or equal to the first threshold, and / or the target space complexity is less than or equal to the second threshold,

[0008] Determining a target recognition area based on the driving state of the target vehicle and / or the driving road conditions;

[0009] Identify the target's false track based on the target track characteristics corresponding to the target identification area;

[0010] The false track ratio corresponding to the target identification area is greater than or equal to a third threshold.

[0011] In some feasible implementations, determining the target recognition area based on the driving state of the target vehicle and / or the driving road conditions includes:

[0012] In the case where the driving state corresponds to a straight-ahead state, determining that the target recognition area includes: a first target area, and / or a second target area;

[0013] The first target area corresponds to a spatial area where the probability of velocity distortion of the first direction track is greater than or equal to a fourth threshold, and the second target area corresponds to a spatial area where the probability of mirror image of the second direction track is greater than or equal to a fifth threshold;

[0014] and / or,

[0015] In the case where the driving state corresponds to a turning state, determining the target recognition area includes: a third target area;

[0016] The third target area corresponds to a spatial area centered on the target vehicle, with a distance radius less than or equal to a preset distance radius, and a probability of misjudging a stationary target as a dynamic target greater than or equal to a sixth threshold.

[0017] In some feasible implementations, the target track characteristics include:

[0018] Target duration characteristics, target signal-to-noise ratio characteristics, average radial velocity characteristics of target associated points, target heading angle characteristics, and / or target scattering cross-sectional area characteristics.

[0019] In some feasible implementations, identifying a false target track based on target track features corresponding to the target identification area includes:

[0020] Determining a first interval probability mapping sequence according to target duration characteristics corresponding to target track test data;

[0021] determining a second interval probability mapping sequence according to target signal-to-noise ratio characteristics corresponding to the target track test data;

[0022] and / or,

[0023] A third interval probability mapping sequence is determined according to the average value characteristics of the radial velocity of the target associated point corresponding to the target track test data.

[0024] In some feasible implementations, the identifying of false target tracks based on target track features corresponding to the target identification area further includes:

[0025] Determining the falsity of the target frame according to the target duration feature corresponding to the target frame and the first interval probability mapping sequence;

[0026] Determine the falsity of the target frame according to the target signal-to-noise ratio feature corresponding to the target frame and the second interval probability mapping sequence;

[0027] and / or,

[0028] The falsity of the target frame is determined according to the average value characteristics of the radial velocity of the target associated point corresponding to the target frame and the third interval probability mapping sequence.

[0029] In some feasible implementations, the above method further includes:

[0030] Determining a first target probability and a second target probability according to a target duration feature corresponding to the target frame and a first interval probability mapping sequence;

[0031] Determine a third target probability and a fourth target probability according to a target signal-to-noise ratio feature corresponding to the target frame and a second interval probability mapping sequence;

[0032] determining a fifth target probability and a sixth target probability according to an average value characteristic of the radial velocity of the target associated point corresponding to the target frame and a third interval probability mapping;

[0033] When the product of the first target probability, the third target probability, the fifth target probability and the false track ratio corresponding to the target identification area is greater than the product of the second target probability, the fourth target probability, the sixth target probability and the true track ratio corresponding to the target identification area, the target frame is determined to be a false frame.

[0034] In some feasible implementations, the above method further includes:

[0035] When it is determined that the target track has a plurality of continuous false frames and the number of the false frames is greater than or equal to a preset number, the target track is determined to be a target false track.

[0036] In a second aspect of the present application, a millimeter-wave radar-based false track identification system is provided, comprising:

[0037] a determination unit, configured to determine a target recognition area according to a driving state of the target vehicle and / or a driving road condition, when it is determined that the target time complexity is less than or equal to a first threshold value and / or the target spatial complexity is less than or equal to a second threshold value;

[0038] an identification unit, configured to identify a false target track based on target track characteristics corresponding to a target identification area;

[0039] The false track ratio corresponding to the target identification area is greater than or equal to a third threshold.

[0040] In a third aspect of the present application, an electronic device is provided, comprising a processor and a memory, wherein the memory stores computer program instructions, which are used by the processor to execute the false track identification method based on millimeter-wave radar as described in any one of the above items when the processor is running.

[0041] In a fourth aspect of the present application, a storage medium is provided, on which program instructions are stored. The program instructions are used to execute the false track identification method based on millimeter-wave radar as described in any of the above items when running.

[0042] The millimeter-wave radar-based false track identification method, system and related equipment provided in the embodiments of the present application are applicable to a single sensor, wherein the method includes: determining a target identification area according to the driving state of the target vehicle and / or the driving road conditions when determining that the target time complexity is less than or equal to a first threshold value, and / or the target spatial complexity is less than or equal to a second threshold value; identifying the target false track according to the target track characteristics corresponding to the target identification area; wherein the false track ratio corresponding to the target identification area is greater than or equal to a third threshold value. The present application can improve the applicability to situations with low time complexity and / or spatial complexity, and is conducive to improving the recognition accuracy and efficiency of false tracks by retaining the original information of the data.

[0043] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0045] Figure 1 A schematic diagram of the process of a false track identification method based on millimeter wave radar provided in an embodiment of the present application;

[0046] Figure 2 A structural schematic diagram of a false track identification system based on millimeter-wave radar provided in an embodiment of the present application;

[0047] Figure 3 A structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0049] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0050] Millimeter-wave radar is widely used in environmental perception during vehicle driving and is very important for detecting and tracking road targets.

[0051] In the existing technology, the identification of false tracks is mostly based on multi-sensor data fusion combined with confidence algorithms. The data collected by multiple sensors in this way are heterogeneous, and the use of feature-level fusion or decision-level fusion is prone to loss of original data information, resulting in a high risk of false track misjudgment. In addition, the above method also has problems such as high computing power requirements and high hardware costs.

[0052] Based on this, the present application provides a false track identification method based on millimeter wave radar, which is suitable for a single sensor. Figure 1 The present invention provides a flow chart of a method 100 for identifying false tracks based on millimeter wave radar. Figure 1 As shown, the method 100 includes:

[0053] Step S1: When it is determined that the target time complexity is less than or equal to a first threshold, and / or the target space complexity is less than or equal to a second threshold, the target recognition area is determined according to the driving state of the target vehicle and / or the driving road conditions.

[0054] It should be noted that the first threshold corresponds to a preset time complexity threshold, and the second threshold corresponds to a preset space complexity threshold. The first and second thresholds are negatively correlated with the required false track identification accuracy. That is, the higher the false track identification accuracy requirement, the smaller the first and second thresholds.

[0055] Exemplarily, when it is determined that the target time complexity corresponding to the target scenario is less than or equal to a first threshold, and / or the target spatial complexity is less than or equal to a second threshold, the target recognition area is determined according to the driving state of the target vehicle and / or the driving road conditions.

[0056] It should be noted that, by determining the target identification area based on the driving state of the target vehicle and / or the road conditions when determining that the target time complexity is less than or equal to the first threshold, it is beneficial to improve the operating efficiency and response speed of the present method, reduce the delay rate of the present method, and achieve the completion of the determination of the above-mentioned target identification area in a shorter time. And / or, by determining the target identification area based on the driving state of the target vehicle and / or the road conditions when determining that the target spatial complexity is less than or equal to the second threshold, it is beneficial to reduce the memory resources required during the execution of the present method, thereby reducing the hardware cost during the execution of the present method.

[0057] It should be noted that the false track ratio corresponding to the target identification area is greater than or equal to the third threshold, meaning that the target identification area is a spatial region with a high frequency of false tracks, as determined based on statistics from massive drive test data. The scope of this spatial region can be determined based on the installation location of the millimeter-wave radar. The massive drive test data may include test data with a mileage of greater than or equal to 1000 km.

[0058] The false track ratio corresponding to the target identification area can be determined based on the ratio of the sum of the duration of each false track in the target space area to the total driving time of the vehicle.

[0059] The third threshold may be determined based on the false track identification accuracy requirement. The third threshold is negatively correlated with the false track identification accuracy requirement, that is, the higher the false track identification accuracy requirement, the smaller the third threshold.

[0060] In some feasible embodiments, the above-mentioned determination of the target identification area based on the driving state and / or the driving road condition of the target vehicle includes: when the driving state corresponds to a straight-ahead state, determining the target identification area includes: a first target area, and / or a second target area; wherein the first target area corresponds to a spatial area where the probability of speed distortion of the first-direction track is greater than or equal to a fourth threshold, and the second target area corresponds to a spatial area where the probability of mirroring of the second-direction track is greater than or equal to a fifth threshold.

[0061] It should be noted that the fourth threshold and the fifth threshold are negatively correlated with the false track identification accuracy requirement, that is, the higher the false track identification accuracy requirement, the smaller the fourth threshold and the fifth threshold are.

[0062] For example, when it is determined that the driving state of the target vehicle corresponds to a straight-ahead state, it can be determined that the target identification area includes: a first target area, i.e., a spatial area in front of the target vehicle where false tracks with abnormal speeds are prone to occur, and / or a second target area, i.e., a spatial area to the side of the target vehicle where mirror-image false tracks are prone to occur.

[0063] It should be noted that the first target area may be a rectangular space area, and the second target area may be a strip space area.

[0064] In some feasible embodiments, the target identification area is determined based on the driving state of the target vehicle and / or the driving road conditions, and / or further includes: when the driving state corresponds to a turning state, determining that the target identification area includes: a third target area; wherein the third target area corresponds to a spatial area centered on the target vehicle, with a distance radius less than or equal to a preset distance radius, and a probability of misjudging a stationary target as a dynamic target greater than or equal to a sixth threshold.

[0065] It should be noted that the sixth threshold is negatively correlated with the required accuracy for identifying false tracks, that is, the higher the required accuracy for identifying false tracks, the smaller the sixth threshold. The preset distance radius can be determined based on the installation location of the millimeter wave radar.

[0066] Exemplarily, when it is determined that the driving state of the target vehicle corresponds to a turning state, the above-mentioned target identification area is determined to include: the third target area, that is, the spatial area with the target vehicle as the center, the distance radius is less than or equal to the preset distance radius, and the stationary target error change occurs.

[0067] It should be noted that the third target area may be a fan-shaped space area.

[0068] It should be noted that, when it is determined that the driving condition of the target vehicle corresponds to the urban interchange condition or the rural mountainous condition, the above-mentioned target recognition area can be determined by itself according to the actual situation.

[0069] Therefore, the above method can realize the accurate and automatic determination of the target identification area according to the driving state of the target vehicle and / or the driving road conditions, so as to identify the target false tracks, which is beneficial to improve the recognition accuracy and efficiency of the target false tracks by improving the matching between the target identification area and the driving state of the target vehicle and / or the driving road conditions.

[0070] Step S2: Identify the target false track according to the target track characteristics corresponding to the target identification area.

[0071] For example, based on the principle of electromagnetic wave scattering, target track features corresponding to multiple target identification areas may be determined to identify target false tracks, wherein the target track features corresponding to the multiple target identification areas may be different.

[0072] Specifically, the recognition operation may be performed on all target recognition areas until each track within the target recognition area is completely recognized and processed.

[0073] In some feasible implementations, the target track characteristics include: target duration characteristics, target signal-to-noise ratio characteristics, average radial velocity characteristics of target associated points, target heading angle characteristics, and / or target scattering cross-sectional area characteristics.

[0074] For example, target false tracks can be identified based on target duration characteristics, target signal-to-noise ratio characteristics, average radial velocity characteristics of target associated points, target heading angle characteristics, and / or target scattering cross-sectional area characteristics corresponding to the target identification area.

[0075] It should be noted that the main causes of false tracks include: insufficient angular resolution, strongly reflective objects, multipath and reflection, etc. Therefore, by identifying target false tracks based on the target duration characteristics, target signal-to-noise ratio characteristics, average radial velocity characteristics of target associated points, target heading angle characteristics, and / or target scattering cross-sectional area characteristics corresponding to the target identification area, it is beneficial to further improve the recognition accuracy and efficiency of target false tracks.

[0076] Based on this, the millimeter-wave radar-based false track identification method provided in the embodiment of the present application is applicable to a single sensor, including: automatically and accurately determining the target identification area according to the driving state of the target vehicle and / or the driving road conditions when determining that the target time complexity is less than or equal to the first threshold, and / or the target spatial complexity is less than or equal to the second threshold; automatically and accurately identifying the target false track according to the target track characteristics corresponding to the target identification area; wherein the false track ratio corresponding to the target identification area is greater than or equal to the third threshold. The present application can improve the applicability for scenarios with low time complexity and / or spatial complexity. The present application is conducive to avoiding heterogeneity between data without performing additional fusion operations on the data. It is conducive to accurately retaining the original information of the data while reducing hardware costs and computing power requirements, thereby improving the recognition accuracy and efficiency of false tracks.

[0077] In some feasible implementations, step S2: identifying the target false track based on the target track characteristics corresponding to the target identification area includes:

[0078] Step S2a: Determine a first interval probability mapping sequence according to the target duration characteristics corresponding to the target track test data.

[0079] Exemplarily, the target duration feature may include: a duration feature before the track starts.

[0080] Exemplarily, a first interval sequence may be generated by dividing according to the target duration feature, and a first probability sequence may be determined based on the first interval sequence to generate a first interval probability mapping sequence.

[0081] Specifically, it can be generated based on the target duration characteristics. Different intervals are used to generate the above-mentioned first interval sequence, and the probability sequence of false tracks and the probability sequence of real tracks corresponding to the first interval sequence are determined based on the Bayesian algorithm. According to the probability sequence of false tracks and the probability sequence of real tracks corresponding to the first interval sequence, the first probability sequence is determined. According to the above-mentioned The mapping relationship between different intervals and the above-mentioned first probability sequence is used to determine the first interval probability mapping sequence.

[0082] The probability sequence of false tracks corresponding to the first interval sequence can be expressed as: (1)

[0083] The probability sequence of the occurrence of the real track corresponding to the first interval sequence can be expressed as: (2)

[0084] It should be noted that the sum of the probability sequence of false track occurrence corresponding to the first interval sequence and the corresponding element pairs of the first interval sequence is 1.

[0085] Step S2b: Determine a second interval probability mapping sequence according to the target signal-to-noise ratio characteristics corresponding to the target track test data.

[0086] Exemplarily, a second interval sequence may be generated by dividing according to a target signal-to-noise ratio feature, and a second probability sequence may be determined based on the second interval sequence to generate a second interval probability mapping sequence.

[0087] Specifically, it can be generated based on the target duration characteristics. Different intervals are used to generate the above-mentioned second interval sequence, and the probability sequence of the occurrence of false tracks and the probability sequence of the occurrence of real tracks corresponding to the second interval sequence are determined based on the Bayesian algorithm. According to the probability sequence of the occurrence of false tracks and the probability sequence of the occurrence of real tracks corresponding to the second interval sequence, the second probability sequence is determined. According to the above-mentioned The mapping relationship between different intervals and the above second probability sequence is used to determine the second interval probability mapping sequence.

[0088] The probability sequence of false tracks corresponding to the second interval sequence can be expressed as: (3)

[0089] The probability sequence of the occurrence of the real track corresponding to the second interval sequence can be expressed as: (4)

[0090] It should be noted that the sum of the probability sequence of false tracks corresponding to the second interval sequence and the corresponding element pairs of the second interval sequence is 1. and the above The sum of is 1.

[0091] And / or, step S2c: determining a third interval probability mapping sequence based on the average value characteristics of the radial velocity of the target associated point corresponding to the target track test data.

[0092] It should be noted that the individual traces output by the millimeter-wave radar cannot be confirmed to belong to the same target. Therefore, the target association points can be determined based on a preset algorithm to determine the third interval probability mapping sequence. The preset algorithms include the DBSCAN algorithm and the Nearest Neighbor (NN) algorithm.

[0093] For example, a target merged adjacent point track can be determined based on the DBSCAN algorithm to determine the initial value of the target track. The initial value of the target track is used as the starting point of the target track, and the target association point is determined based on the nearest neighbor algorithm to determine the third interval probability mapping sequence. The target association point corresponds to a point track whose Euclidean distance to the target track is less than a preset distance. It is understood that the target association point corresponds to the target track, and the preset distance can be defined based on the actual scenario.

[0094] Exemplarily, a third interval sequence may be generated by dividing according to a target signal-to-noise ratio feature, and a third probability sequence may be determined based on the third interval sequence to generate a third interval probability mapping sequence.

[0095] Specifically, it can be generated based on the target duration characteristics. Different intervals are used to generate the third interval sequence. The probability sequence of false tracks and the probability sequence of real tracks corresponding to the third interval sequence are determined based on the Bayesian algorithm. The third probability sequence is determined based on the probability sequence of false tracks and the probability sequence of real tracks corresponding to the third interval sequence. The mapping relationship between different intervals and the third probability sequence is used to determine the third interval probability mapping sequence.

[0096] The probability sequence of false tracks corresponding to the third interval sequence can be expressed as: (5)

[0097] The probability sequence of the occurrence of the real track corresponding to the third interval sequence can be expressed as: (6)

[0098] It should be noted that the sum of the probability sequence of false tracks corresponding to the third interval sequence and the corresponding element pairs of the third interval sequence is 1. and the above The sum of is 1.

[0099] Therefore, the above method can accurately and automatically determine the first interval probability mapping sequence according to the target duration characteristics; accurately and automatically determine the second interval probability mapping sequence according to the target signal-to-noise ratio characteristics; accurately and automatically determine the third interval probability mapping sequence according to the average value characteristics of the radial velocity of the target associated point, providing data support for accurately identifying the target false track based on the first interval probability mapping sequence, the second interval probability mapping sequence, and / or the third interval probability mapping sequence.

[0100] In some feasible implementations, the above step S2: identifying the target false track according to the target track characteristics corresponding to the target identification area further includes:

[0101] Step S2d: Determine the falsity of the target frame based on the target duration feature corresponding to the target frame and the first interval probability mapping sequence.

[0102] For example, the target duration feature corresponding to the target frame is located at In the case of intervals, the probability of the target frame being a false frame can be determined based on the first interval probability mapping sequence: , the probability that the target frame is a non-false frame, that is, a real frame is , the probability of determining that the target frame is a false frame is The probability that the target frame is a non-false frame, that is, a real frame, is In the case of , it can be determined that the target frame is a false frame. The probability of determining that the target frame is a false frame is The probability that the target frame is less than or equal to the target frame is a non-false frame, that is, a real frame is In this case, it can be determined that the target frame is a non-false frame, that is, a real frame.

[0103] Specifically, the existence duration feature before the start of the target frame is located at In the case of intervals, the probability of the target frame being a false frame can be determined based on the first interval probability mapping sequence: , the probability that the target frame is a non-false frame, that is, a real frame is , the probability of determining that the target frame is a false frame is The probability that the target frame is a non-false frame, that is, a real frame, is In the case of , it can be determined that the target frame is a false frame. The probability of determining that the target frame is a false frame is The probability that the target frame is less than or equal to the target frame is a non-false frame, that is, a real frame is In this case, it can be determined that the target frame is a non-false frame, that is, a real frame.

[0104] Step S2e: Determine the falsity of the target frame based on the target signal-to-noise ratio feature corresponding to the target frame and the second interval probability mapping sequence.

[0105] For example, the target signal-to-noise ratio feature corresponding to the target frame is located at In the case of intervals, the probability of the target frame being a false frame can be determined based on the second interval probability mapping sequence: , the probability that the target frame is a non-false frame, that is, a real frame is , the probability of determining that the target frame is a false frame is The probability that the target frame is a non-false frame, that is, a real frame, is In the case of , it can be determined that the target frame is a false frame. The probability of determining that the target frame is a false frame is The probability that the target frame is less than or equal to the target frame is a non-false frame, that is, a real frame is In this case, it can be determined that the target frame is a non-false frame, that is, a real frame.

[0106] and / or,

[0107] Step S2f: Determine the falsity of the target frame based on the average value characteristics of the radial velocity of the target associated point corresponding to the target frame and the third interval probability mapping sequence.

[0108] For example, the average radial velocity feature of the target associated point corresponding to the target frame is located at In the case of intervals, the probability of the target frame being a false frame can be determined based on the third interval probability mapping sequence: , the probability that the target frame is a non-false frame, that is, a real frame is , the probability of determining that the target frame is a false frame is The probability that the target frame is a non-false frame, that is, a real frame, is In the case of , it can be determined that the target frame is a false frame. The probability of determining that the target frame is a false frame is The probability that the target frame is less than or equal to the target frame is a non-false frame, that is, a real frame is In this case, it can be determined that the target frame is a non-false frame, that is, a real frame.

[0109] Therefore, the above method can realize the accurate and automatic identification of false frames in the target track based on the target duration characteristics and the first interval probability mapping sequence corresponding to the target frame, the target signal-to-noise ratio characteristics and the second interval probability mapping sequence corresponding to the target frame, and / or the average value characteristics of the radial velocity of the target associated point corresponding to the target frame and the third interval probability mapping sequence, providing data support for the accurate identification of false target tracks.

[0110] In some feasible implementations, the above method further includes:

[0111] Step S2g: Determine the first target probability and the second target probability according to the target duration feature corresponding to the target frame and the first interval probability mapping sequence.

[0112] For example, the target duration feature corresponding to the target frame is located at In the case of intervals, the first target probability, that is, the probability that the target frame is a false frame, can be determined based on the first interval probability mapping sequence. , the second target probability, that is, the probability that the target frame is a real frame is .

[0113] Step S2h: Determine the third target probability and the fourth target probability according to the target signal-to-noise ratio feature corresponding to the target frame and the second interval probability mapping sequence.

[0114] For example, the target duration feature corresponding to the target frame is located at In the case of intervals, the third target probability, that is, the probability that the target frame is a false frame, can be determined based on the second interval probability mapping sequence: , the second target probability, that is, the probability that the target frame is a real frame is .

[0115] Step S2i: Determine the fifth target probability and the sixth target probability based on the average value characteristics of the radial velocity of the target associated point corresponding to the target frame and the third interval probability mapping.

[0116] For example, the average radial velocity feature of the target associated point corresponding to the target frame is located at In the case of intervals, the fifth target probability, that is, the probability that the target frame is a false frame, can be determined based on the third interval probability mapping sequence. , the sixth target probability, that is, the probability that the target frame is a real frame, is .

[0117] Step S2j: When the product of the first target probability, the third target probability, the fifth target probability and the false track ratio corresponding to the target identification area is greater than the product of the second target probability, the fourth target probability, the sixth target probability and the true track ratio corresponding to the target identification area, the target frame is determined to be a false frame.

[0118] It should be noted that the false track ratio corresponding to the target identification area can be determined by the ratio of the sum of the duration of each false track in the target space area to the total driving time of the vehicle. The false track ratio corresponding to the target identification area can be calculated by The real track ratio corresponding to the target identification area can be determined based on the ratio of the sum of the duration of each real track in the target space area to the total driving time of the car. The real track ratio corresponding to the target identification area can be determined by Indicates. Among them, the false track ratio corresponding to the above target identification area The actual track ratio corresponding to the above target identification area The sum is 1.

[0119] For example, the falsity of the target frame can be determined according to the following formula: (7)

[0120] in, is the first target probability, is the third target probability, is the fifth target probability, is the false track ratio corresponding to the target identification area, is the second target probability, is the fourth target probability, is the sixth target probability, is the actual track ratio corresponding to the target identification area.

[0121] Therefore, the above method can realize the accurate and automatic identification of false frames in the target track based on the first target probability, the second target probability, the third target probability, the fourth target probability, the fifth target probability, the sixth target probability, the false track ratio corresponding to the target identification area, and the true track ratio corresponding to the target identification area, thereby providing data support for the accurate identification of false target tracks.

[0122] It should be noted that steps S2a through S2j described above can be implemented based on a Bayesian classifier, thereby achieving stable classification of the target frame based on target duration characteristics, target signal-to-noise ratio characteristics, and / or average radial velocity characteristics of target-related points to determine the falsity of the target frame. The Bayesian classifier can calculate the posterior probability of the target frame using the Bayesian formula based on the prior probability of the target frame, thereby determining the probability of the target frame being a false frame or a true frame, and selecting the corresponding category with the maximum posterior probability as the category to which the target frame belongs.

[0123] In some feasible implementations, the above method further includes:

[0124] Step S2k: When it is determined that the target track has multiple false frames continuously and the number of false frames is greater than or equal to a preset number, the target track is determined to be a target false track.

[0125] It should be noted that the above-mentioned preset number is negatively correlated with the required accuracy for identifying false tracks, that is, the higher the required accuracy for identifying false tracks, the smaller the above-mentioned preset number.

[0126] Exemplarily, when it is determined that a target track has a plurality of consecutive false frames, and the number of the false frames is greater than or equal to 3, the target track is determined to be a target false track.

[0127] Therefore, the above method can accurately identify the target false track according to the number of false frames, thereby improving the recognition accuracy and efficiency of the target false track.

[0128] In some feasible implementations, the above method further includes: step S3: when it is determined that the target track is a false target track, canceling the alarm signal and / or deleting the target track.

[0129] It should be noted that, when the target track is determined to be a false target track, the alarm signal is controlled to be released at the current moment, and / or, when the target track is determined to be a false target track, the target track is deleted and is not at the start.

[0130] Therefore, the above method can accurately reduce the false alarm probability of the intelligent driving system.

[0131] It should be noted that, when the target track is determined to be the true target track, the alarm signal is not released, and / or, when the target track is determined to be the true target track, the target track is controlled to start and output normally.

[0132] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0133] The above is an introduction to the method embodiment. The following is a system embodiment to further illustrate the solution described in this application.

[0134] According to a second aspect of the present application, the present application provides a false track identification system based on millimeter wave radar. Figure 2 A structural diagram of a false track identification system 200 based on millimeter wave radar is provided in an embodiment of the present application, as shown in FIG. Figure 2 The system 200 shown includes a determination unit 210 and an identification unit 220 .

[0135] A determination unit 210 is configured to determine a target recognition area based on a driving state of the target vehicle and / or a driving road condition when it is determined that the target time complexity is less than or equal to a first threshold and / or the target spatial complexity is less than or equal to a second threshold;

[0136] An identification unit 220 is configured to identify a false target track based on target track characteristics corresponding to the target identification area;

[0137] The false track ratio corresponding to the target identification area is greater than or equal to a third threshold.

[0138] For example, Figure 3 A schematic diagram of the structure of a terminal device or server suitable for implementing an embodiment of the present application is shown.

[0139] like Figure 3 As shown, the terminal device or server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 into the random access memory (RAM) 303. Various programs and data required for the operation of the terminal device or server are also stored in the RAM 303. The CPU 301, ROM 302 and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0140] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, and the like; an output section 307 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 308 including a hard disk; and a communication section 309 including a network interface card such as a LAN card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read therefrom can be installed into the storage section 308 as needed.

[0141] In particular, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program device, which includes a computer program carried on a machine-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above-mentioned functions defined in the system of the present application are executed.

[0142] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program devices according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0144] The units or modules involved in the embodiments described in this application may be implemented in software or hardware. The units or modules described may also be provided in a processor. The names of these units or modules do not, in certain circumstances, constitute limitations on the units or modules themselves.

[0145] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable storage medium stores one or more programs, which, when used by one or more processors, execute the method described in the present application.

[0146] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A method for identifying false tracks based on millimeter wave radar, characterized in that: For single sensors, including: When it is determined that the target time complexity is less than or equal to the first threshold, and / or the target space complexity is less than or equal to the second threshold, Determining a target recognition area based on the driving state of the target vehicle and / or the driving road conditions; Identifying a false target track based on target track characteristics corresponding to the target identification area; wherein the false track ratio corresponding to the target identification area is greater than or equal to a third threshold; Determining the target recognition area according to the driving state of the target vehicle and / or the driving road condition includes: In a case where the driving state corresponds to a straight-ahead state, determining that the target recognition area includes: a first target area, and / or a second target area; The first target area corresponds to a spatial area where the probability of velocity distortion of the first direction track is greater than or equal to a fourth threshold, and the second target area corresponds to a spatial area where the probability of mirror image of the second direction track is greater than or equal to a fifth threshold; and / or, In a case where the driving state corresponds to a turning state, determining that the target recognition area includes: a third target area; The third target area corresponds to a spatial area centered on the target vehicle, with a distance radius less than or equal to a preset distance radius, and a probability of misjudging a stationary target as a dynamic target greater than or equal to a sixth threshold.

2. The method for identifying false tracks based on millimeter-wave radar according to claim 1, characterized in that: The target track characteristics include: Target duration characteristics, target signal-to-noise ratio characteristics, average radial velocity characteristics of target associated points, target heading angle characteristics, and / or target scattering cross-sectional area characteristics.

3. The method for identifying false tracks based on millimeter wave radar according to claim 2, characterized in that: The identifying of the target false track according to the target track characteristics corresponding to the target identification area includes: Determining a first interval probability mapping sequence according to target duration characteristics corresponding to target track test data; determining a second interval probability mapping sequence according to a target signal-to-noise ratio characteristic corresponding to the target track test data; and / or, A third interval probability mapping sequence is determined according to the average value characteristics of the radial velocity of the target associated point corresponding to the target track test data.

4. The method for identifying false tracks based on millimeter wave radar according to claim 3, characterized in that: The identifying the target false track according to the target track characteristics corresponding to the target identification area further includes: Determining the falsity of the target frame according to the target duration feature corresponding to the target frame and the first interval probability mapping sequence; Determine the falsity of the target frame according to the target signal-to-noise ratio feature corresponding to the target frame and the second interval probability mapping sequence; and / or, The falsity of the target frame is determined according to the average value characteristics of the radial velocity of the target associated point corresponding to the target frame and the third interval probability mapping sequence.

5. The method for identifying false tracks based on millimeter wave radar according to claim 4, characterized in that: Also includes: Determining a first target probability and a second target probability according to a target duration feature corresponding to the target frame and a first interval probability mapping sequence; Determine a third target probability and a fourth target probability according to a target signal-to-noise ratio feature corresponding to the target frame and a second interval probability mapping sequence; determining a fifth target probability and a sixth target probability according to an average value characteristic of the radial velocity of the target associated point corresponding to the target frame and a third interval probability mapping; When the product of the first target probability, the third target probability, the fifth target probability and the false track ratio corresponding to the target identification area is greater than the product of the second target probability, the fourth target probability, the sixth target probability and the true track ratio corresponding to the target identification area, the target frame is determined to be a false frame.

6. The method for identifying false tracks based on millimeter-wave radar according to claim 5, characterized in that: Also includes: If it is determined that the target track has a plurality of continuous false frames and the number of the false frames is greater than or equal to a preset number, the target track is determined to be a target false track.

7. A false track identification system based on millimeter wave radar, characterized in that: include: a determination unit, configured to determine a target recognition area according to a driving state of the target vehicle and / or a driving road condition, when it is determined that the target time complexity is less than or equal to a first threshold value and / or the target spatial complexity is less than or equal to a second threshold value; an identification unit, configured to identify a false target track according to target track characteristics corresponding to the target identification area; wherein the false track ratio corresponding to the target identification area is greater than or equal to a third threshold; Determining the target recognition area according to the driving state of the target vehicle and / or the driving road condition includes: In a case where the driving state corresponds to a straight-ahead state, determining that the target recognition area includes: a first target area, and / or a second target area; The first target area corresponds to a spatial area where the probability of velocity distortion of the first direction track is greater than or equal to a fourth threshold, and the second target area corresponds to a spatial area where the probability of mirror image of the second direction track is greater than or equal to a fifth threshold; and / or, In a case where the driving state corresponds to a turning state, determining that the target recognition area includes: a third target area; The third target area corresponds to a spatial area centered on the target vehicle, with a distance radius less than or equal to a preset distance radius, and a probability of misjudging a stationary target as a dynamic target greater than or equal to a sixth threshold.

8. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the false track identification method based on millimeter-wave radar according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the false track identification method based on millimeter-wave radar is implemented as described in any one of claims 1 to 6.

Images