Dynamic search methods, devices, equipment, and storage media for intersections ahead of vehicles

Abstract

This application provides a dynamic search method, apparatus, device, and storage medium for intersections ahead of a vehicle, relating to the fields of intelligent transportation and vehicle navigation technology. The method includes: acquiring the vehicle's current position and heading angle, determining an initial fan-shaped search area accordingly, and extracting all potential intersections within that area; for each potential intersection, determining the straight-line distance between the vehicle and the intersection; dynamically determining an individualized fan-shaped angle threshold for filtering potential intersections based on the straight-line distance and a preset distance-angle mapping relationship; determining whether the intersection is located within a dynamic fan-shaped area with the angle defined by the angle threshold, and if so, marking the intersection as a candidate intersection; further, selecting the candidate intersection with the smallest straight-line distance from the vehicle from all candidate intersections as the target intersection ahead, thereby effectively reducing missed detections and false detections caused by fixed angles, and significantly enhancing the accuracy and reliability of intersection recognition ahead of the vehicle.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation and vehicle navigation technology, and in particular to a method, device, equipment and storage medium for dynamic search of intersections ahead of a vehicle. Background Technology

[0002] With the rapid development of intelligent transportation systems, autonomous driving, and high-precision navigation technologies, real-time and accurate perception of the driving environment has become a core technological requirement for vehicles. Accurate identification and matching of intersection information ahead is crucial for lane-level guidance, route planning, traffic signal prediction, and autonomous driving decision-making. Typically, this technology relies on real-time pose information acquired by onboard sensors such as GPS or IMU, combined with high-precision electronic map data for spatial matching. However, urban road networks are complex and varied, with dense and irregular intersection distribution, especially in areas such as overpasses, ramps, and roundabouts. Efficiently and accurately filtering or determining the target intersection a vehicle is about to enter from a wealth of geographic information has always been a technological challenge for the industry.

[0003] In related technologies, a common approach is to use static matching based on a fixed geometric region for intersection search. Specifically, this involves using the vehicle's current position as the vertex and the direction of travel (heading angle) as the central axis, pre-setting a fan-shaped region with a fixed angle (e.g., 60 degrees) and a fixed maximum search radius (e.g., 500 meters), and then extracting or searching all intersections within this region as potential targets. However, this approach suffers from missed or false intersection detections, resulting in low accuracy and reliability in intersection recognition. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for dynamic search of intersections ahead of a vehicle, in order to improve the problem that the use of related technologies results in missed or false intersection detections, leading to low accuracy and reliability of intersection recognition.

[0005] Firstly, this application provides a dynamic search method for intersections ahead of a vehicle, including:

[0006] Obtain the vehicle's current position and heading angle;

[0007] Based on the current location and heading angle, determine the initial sector search area and extract all potential intersections within the initial sector search area;

[0008] For each potential intersection, determine the straight-line distance between the vehicle and the potential intersection;

[0009] Based on the straight-line distance and according to the preset distance-angle mapping relationship, the individualized sector angle threshold for screening potential intersections is dynamically determined; wherein, the distance-angle mapping relationship is configured such that the individualized sector angle threshold increases as the straight-line distance decreases;

[0010] Based on the individualized sector angle threshold, it is determined whether the potential intersection is located within a dynamic sector area with the heading angle as the central axis and the individualized sector angle threshold as the angle. If so, the potential intersection is marked as a candidate intersection.

[0011] From all candidate intersections, the one with the shortest straight-line distance to the vehicle is selected as the target intersection ahead.

[0012] In one possible implementation, determining whether a potential intersection is located within a dynamic sector area with the heading angle as the central axis and an individualized sector angle threshold as the angle includes: determining the azimuth angle between the potential intersection's orientation relative to the vehicle and the heading angle; and determining whether the absolute value of the azimuth angle is less than or equal to half of the individualized sector angle threshold.

[0013] In one possible implementation, based on the straight-line distance and a preset distance-angle mapping relationship, an individualized sector angle threshold for screening potential intersections is dynamically determined, including: determining a first difference between the maximum search distance and the straight-line distance, where the maximum search distance is the radius of the initial sector search area; multiplying the first difference by a preset proportional coefficient to obtain a product; adding the product to a preset minimum angle threshold, and determining the result of the addition as the individualized sector angle threshold; wherein the proportional coefficient is determined based on the maximum angle threshold, the minimum angle threshold, and the maximum search distance, and the maximum angle threshold is the sector angle of the initial sector search area.

[0014] In one possible implementation, the scaling factor is determined by: determining a second difference between the maximum angle threshold and the minimum angle threshold; dividing the second difference by the maximum search distance, and determining the resulting quotient as the scaling factor.

[0015] In one possible implementation, the parameters upon which the preset distance-angle mapping relationship depends are configured based on the intersection distribution characteristics of the vehicle's current environment.

[0016] In one possible implementation, configuration is performed based on the intersection distribution characteristics of the vehicle's current environment, including: identifying whether the road type the vehicle is currently on is a high-density road network or a low-density road network; and configuring with pre-stored parameters corresponding to the road type; wherein the maximum search distance configured for high-density road network is less than the maximum search distance configured for low-density road network.

[0017] In one possible implementation, in addition to using a pre-stored parameter configuration corresponding to the road type, the method further includes: dynamically adjusting the minimum angle threshold and / or maximum search distance in the pre-stored parameter configuration based on the number of all potential intersections extracted within the initial sector search area.

[0018] In one possible implementation, the dynamic search method for the intersection ahead of the vehicle further includes: estimating the estimated time to reach the target intersection ahead based on the vehicle's real-time speed and straight-line distance before outputting the target intersection ahead to the navigation or autonomous driving system.

[0019] Secondly, this application provides a dynamic search device for intersections ahead of a vehicle, comprising:

[0020] The positioning module is used to obtain the vehicle's current position and heading angle;

[0021] The potential intersection filtering module is used to determine the initial sector search area based on the current location and heading angle, and extract all potential intersections within the initial sector search area;

[0022] The distance calculation module is used to determine the straight-line distance between a vehicle and a potential intersection for each potential intersection.

[0023] The sector angle adjustment module is used to dynamically determine the individualized sector angle threshold for screening potential intersections based on the straight-line distance and a preset distance-angle mapping relationship; wherein, the distance-angle mapping relationship is configured such that the individualized sector angle threshold increases as the straight-line distance decreases;

[0024] The precise filtering module is used to determine whether a potential intersection is located within a dynamic sector area with the heading angle as the central axis and the individualized sector angle threshold as the angle, based on an individualized sector angle threshold. If so, the potential intersection is marked as a candidate intersection.

[0025] The target determination module is used to select the candidate intersection with the smallest straight-line distance from the vehicle from all candidate intersections as the target intersection ahead.

[0026] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0027] Memory is used to store instructions executed by the computer;

[0028] A processor for executing computer-executable instructions stored in memory to implement any of the methods of the first aspect.

[0029] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the method of any one of the first aspects.

[0030] Fifthly, this application provides a computer program product, including a computer program that, when executed, implements the method of any one of the first aspects.

[0031] The method, apparatus, device, and storage medium for dynamically searching for intersections ahead of a vehicle provided in this application acquire the vehicle's current position and heading angle. Based on the current position and heading angle, an initial fan-shaped search area is determined, and all potential intersections within the initial fan-shaped search area are extracted. For each potential intersection, the straight-line distance between the vehicle and the potential intersection is determined. Based on the straight-line distance and a preset distance-angle mapping relationship, an individualized fan-shaped angle threshold for filtering potential intersections is dynamically determined. The distance-angle mapping relationship is configured such that the individualized fan-shaped angle threshold increases as the straight-line distance decreases. Based on the individualized fan-shaped angle threshold, it is determined whether the potential intersection is located within a dynamic fan-shaped area with the heading angle as the central axis and the individualized fan-shaped angle threshold as the angle. If so, the potential intersection is marked as a candidate intersection. From all candidate intersections, the candidate intersection with the smallest straight-line distance to the vehicle is determined as the target intersection ahead.

[0032] In this process, by introducing a preset distance-angle mapping relationship, for each potential intersection, an individualized sector angle threshold that is inversely proportional to the straight-line distance from the vehicle is dynamically calculated and applied, thus constructing an adaptive dynamic filtering mechanism. This mechanism is based on the intelligent adjustment principle of "larger for closer objects and smaller for farther objects": when the vehicle approaches the intersection, the angle threshold is automatically increased to form a wider judgment range to accommodate positioning and heading errors, thereby effectively reducing missed detections caused by fixed angles; when the vehicle moves away from the intersection, the angle threshold is automatically decreased to form a narrower judgment range to focus on the area directly in front of the vehicle, thereby significantly reducing interference from irrelevant intersections at a distant lateral distance and fundamentally suppressing false detections. Through this dynamic adaptive process, while improving the accuracy of intersection search, it further combines with the explicit filtering strategy of "retaining the nearest intersection" to ensure that the most relevant and urgent target intersection ahead is always locked, thus significantly enhancing the accuracy, real-time performance, and environmental adaptability of intersection recognition ahead of the vehicle, and providing a more reliable and robust perception input for path planning and decision-making of autonomous driving and intelligent navigation systems. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0034] Figure 1 A flowchart illustrating a dynamic search method for intersections ahead of a vehicle, provided as an exemplary embodiment of this application;

[0035] Figure 2 Another flowchart illustrating the dynamic search method for intersections ahead of a vehicle provided as an exemplary embodiment of this application;

[0036] Figure 3 A schematic diagram of a dynamic search device for a vehicle at an intersection ahead, provided as an exemplary embodiment of this application;

[0037] Figure 4 A schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this application.

[0038] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0039] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0040] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0041] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0042] In related technologies, when using intersection search schemes based on static matching of fixed geometric regions, the search angle is fixed. When the vehicle is far from the intersection, an excessively large fixed sector angle will include a large number of irrelevant lateral or reverse intersections, causing interference and mismatches. Conversely, when the vehicle is close to the intersection, an excessively small fixed sector angle may miss the true target intersection due to minor errors in vehicle position or heading, resulting in missed or false intersection detections. Secondly, the selection logic is unclear: when multiple intersections appear in a fixed area (especially in complex intersection clusters), related technologies often lack an effective priority determination mechanism, which may incorrectly select non-target intersections, affecting the accuracy and real-time performance of navigation or autonomous driving systems.

[0043] To address the aforementioned issues, this application proposes a dynamic search scheme for intersections ahead of vehicles. For each potential intersection, based on its real-time straight-line distance to the vehicle and a preset distance-angle mapping relationship, a personalized sector angle threshold inversely proportional to the straight-line distance is dynamically generated. A dynamic filtering region is then constructed based on this threshold. This "nearer-larger, farther-smaller" adjustment mechanism, which increases the personalized sector angle threshold as the distance decreases, automatically widens the angle tolerance to reduce missed detections when the vehicle approaches the intersection and automatically narrows the angle to reduce false detections caused by lateral interference when the vehicle moves away from the intersection, thus fundamentally improving search accuracy. Furthermore, by implementing an explicit "retain nearest intersection" logic on the filtered candidate intersections, the system ensures that the focus remains on the most relevant target intersection ahead, significantly improving the accuracy, reliability, and robustness of intersection recognition.

[0044] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0045] Figure 1 This is a flowchart illustrating a dynamic search method for an intersection ahead of a vehicle, provided as an exemplary embodiment of this application. Figure 1 As shown, the dynamic search method for the intersection ahead of the vehicle includes the following steps:

[0046] S101. Obtain the vehicle's current position and heading angle.

[0047] For example, the vehicle's real-time status is obtained through onboard positioning and attitude sensors. For instance, latitude and longitude coordinates (e.g., 116.407°E, 39.904°N) are obtained using a Global Positioning System (GPS) / BeiDou receiver and used as the current location. Simultaneously, angular velocity and acceleration data output by the Inertial Measurement Unit (IMU) are combined with dead reckoning or sensor fusion algorithms to calculate the angle between the vehicle's longitudinal axis (i.e., direction of travel) and geographic north. This angle ranges from 0° to 360° (where 0° or 360° represents true north, 90° represents true east, and so on). For example, if the calculated angle is 120°, this angle is the heading angle, used to characterize the vehicle's instantaneous direction of travel.

[0048] S102. Based on the current position and heading angle, determine the initial sector search area and extract all potential intersections within the initial sector search area.

[0049] For example, using the vehicle's current location (e.g., 116.407°E, 39.904°N) obtained in the previous step as the vertex and the heading angle (e.g., 120°) as the central axis, a search area with fixed geometric parameters is defined on the electronic map, called the initial sector search area. This area is defined by two preset parameters: the maximum search distance (e.g., 500 meters) as the sector radius, and the maximum sector angle (e.g., 60°) as the sector angle. Accordingly, the electronic map database containing intersection coordinates and topological information is queried to extract all road intersections whose center point or any feature point is located within the geographical range of this initial sector search area, forming an initial set of potential intersections. This step aims to quickly reduce the data processing scale by using a preset fixed geometric range, providing a preliminary set of candidate targets for subsequent dynamic filtering.

[0050] S103. For each potential intersection, determine the straight-line distance between the vehicle and the potential intersection.

[0051] In this step, for each potential intersection in the set of potential intersections, its coordinates (such as the coordinates of the intersection center point from an electronic map) are obtained, and the straight-line distance (or geometric distance) between it and the vehicle's current position is calculated using spatial geometric formulas (such as the Euclidean distance formula based on the projected coordinate system). For example, it is calculated that intersection A is 150 meters away from the vehicle, intersection B is 80 meters away from the vehicle, and intersection C is 200 meters away from the vehicle.

[0052] For example, suppose the first The coordinates of the potential intersections are ( The vehicle's current location coordinates are ( ), the first Straight-line distance between potential intersections and vehicles Satisfying the formula: .

[0053] S104. Based on the straight-line distance and according to the preset distance-angle mapping relationship, dynamically determine the individualized sector angle threshold for screening potential intersections; wherein, the distance-angle mapping relationship is configured such that the individualized sector angle threshold increases as the straight-line distance decreases.

[0054] For example, for each potential intersection, based on the straight-line distance obtained in step S103, a predefined distance-angle mapping relationship is invoked to calculate an individualized sector angle threshold specific to that intersection. This mapping relationship is designed to satisfy the core rule: the individualized sector angle threshold is inversely correlated with the straight-line distance of the intersection; that is, the smaller the straight-line distance, the larger the individualized sector angle threshold; and the larger the straight-line distance, the smaller the individualized sector angle threshold.

[0055] In practice, the preset distance-angle mapping relationship can be functional, tabular, or rule-based. For example, the system can store a table of distance-angle correspondences, allowing direct lookup of the corresponding angle threshold based on the straight-line distance; alternatively, the system can have a built-in monotonically decreasing function (such as a linear or piecewise function) that takes the straight-line distance as input and outputs the individualized sector angle threshold for the intersection. Regardless of the specific implementation, this mapping relationship ensures that larger angle thresholds are assigned to nearby intersections and smaller angle thresholds to distant intersections, thus providing a dynamically adaptable basis for subsequent filtering steps.

[0056] S105. Based on the individualized sector angle threshold, determine whether the potential intersection is located within a dynamic sector area with the heading angle as the central axis and the individualized sector angle threshold as the angle. If so, mark the potential intersection as a candidate intersection.

[0057] For example, for each potential intersection, the system constructs a virtual dynamic sector region based on an individualized sector angle threshold calculated for that intersection. This region has the vehicle's current position as its vertex, the heading angle as its central axis, and the individualized sector angle threshold as its sector angle. Then, spatial relationship judgment is performed. Specifically, based on electronic map data, the azimuth angle of the potential intersection relative to the vehicle's current position is determined. The obtained azimuth angle is geometrically compared with the heading angle to determine whether the intersection's position falls within the spatial range of the dynamic sector region defined by its own threshold. Correspondingly, if the judgment result indicates that the potential intersection is located within its corresponding dynamic sector region, it is marked as a candidate intersection and included in the candidate set for subsequent processing; otherwise, the potential intersection is excluded and will not participate in further screening.

[0058] S106. From all candidate intersections, determine the one with the smallest straight-line distance to the vehicle as the target intersection ahead.

[0059] For example, after dynamically filtering all potential intersections, a candidate intersection set is obtained, which contains all intersections that meet the dynamic sector area conditions in step S105. By comparing the straight-line distance values ​​of all candidate intersections, the candidate intersection with the smallest straight-line distance value is identified and selected as the target intersection ahead. For example, if the candidate intersection set contains intersection A (150 meters away), intersection B (80 meters away), and intersection C (200 meters away), and the straight-line distance (80 meters) of intersection B is determined to be the smallest, then intersection B is identified as the target intersection ahead in this search. This intersection is the most likely and urgent critical intersection that the vehicle will enter, and it will be provided as output to subsequent modules such as navigation path planning, autonomous driving decision-making, or human-machine interface.

[0060] The dynamic search method for intersections ahead of a vehicle provided in this application introduces a preset distance-angle mapping relationship. For each potential intersection, it dynamically calculates and applies an individualized sector angle threshold that is inversely proportional to the straight-line distance from the vehicle, thereby constructing an adaptive dynamic filtering mechanism. This mechanism is based on the intelligent adjustment principle of "nearer, larger; farther, smaller": when the vehicle approaches the intersection, the angle threshold is automatically increased to form a wider judgment range to accommodate positioning and heading errors, thus effectively reducing missed detections caused by fixed angles; when the vehicle moves away from the intersection, the angle threshold is automatically decreased to form a narrower judgment range to focus on the area directly in front of the vehicle, thus significantly reducing interference from irrelevant intersections at a distant lateral distance, fundamentally suppressing false detections. Through this dynamic adaptive process, while improving the accuracy of intersection search, it further combines a clear filtering strategy of "retaining the nearest intersection" to ensure that the most relevant and urgent target intersection ahead of the vehicle is always locked, thereby significantly enhancing the accuracy, real-time performance, and environmental adaptability of intersection recognition ahead of the vehicle, and thus providing a more reliable and robust perception input for path planning and decision-making in autonomous driving and intelligent navigation systems.

[0061] In some embodiments, determining whether a potential intersection is located within a dynamic sector area with the heading angle as the central axis and an individualized sector angle threshold as the angle includes: determining the azimuth angle between the potential intersection's orientation relative to the vehicle and the heading angle; and determining whether the absolute value of the azimuth angle is less than or equal to half of the individualized sector angle threshold.

[0062] For example, based on the coordinates of the potential intersection in the electronic map data and the vehicle's current position coordinates, the azimuth angle of the potential intersection relative to the vehicle is calculated. Specifically, the direction of the intersection relative to the vehicle's position is determined using spatial geometric methods (such as calculation based on the tangent function of the projected coordinate system), with geographic north as the 0° reference; the absolute value of the difference between this azimuth angle and the vehicle's current heading angle θ is calculated to obtain the first... The azimuth angle of each potential intersection (Right now =|azimuth angle - θ|).

[0063] Accordingly, the absolute value of the azimuth angle | | and the individualized sector angle threshold dynamically determined in step S104 for the intersection. half (i.e.) ) for comparison; if | |≤ Then, the potential intersection is determined to be located with the heading angle θ as the central axis and an individualized threshold value. Within a dynamic sector-shaped area with a sector angle, intersections that meet the filtering criteria are marked as candidate intersections; if | |> If the intersection is located outside the dynamic sector area, it is determined that the condition is not met and will not be marked.

[0064] This application's embodiments achieve refined and adaptive spatial filtering by accurately calculating the azimuth angle of each potential intersection relative to the vehicle's heading and comparing it with half of the intersection's individualized sector angle threshold. This dynamically defines the acceptable azimuth deviation range for intersections at different distances. This mechanism ensures that intersections closer to the vehicle (with a larger threshold) are allowed greater azimuth tolerance, effectively accommodating unavoidable errors in positioning and heading estimation, and significantly reducing the risk of missing key intersections due to overly strict angle limits. Simultaneously, for intersections farther away (with a smaller threshold), the criterion requires them to be strictly in front of the vehicle to be accepted, thus efficiently filtering out interference from laterally irrelevant distant intersections, significantly reducing false detections, and effectively improving the accuracy and robustness of forward intersection searches.

[0065] In some embodiments, based on the straight-line distance and a preset distance-angle mapping relationship, an individualized sector angle threshold for screening potential intersections is dynamically determined, including: determining a first difference between the maximum search distance and the straight-line distance, where the maximum search distance is the radius of the initial sector search area; multiplying the first difference by a preset proportional coefficient to obtain a product; adding the product to a preset minimum angle threshold, and determining the result of the addition as the individualized sector angle threshold; wherein the proportional coefficient is determined based on the maximum angle threshold, the minimum angle threshold, and the maximum search distance, and the maximum angle threshold is the sector angle of the initial sector search area.

[0066] For example, the preset distance-angle mapping relationship is a linear equation, specifically satisfying the following formula:

[0067]

[0068] in, The maximum search distance; For the first The straight-line distance between a potential intersection and a vehicle; The minimum angle threshold; This is the proportionality coefficient. Determined based on the maximum angle threshold, minimum angle threshold, and maximum search distance.

[0069] Accordingly, the maximum search distance is taken from the radius of the initial sector search area defined in step S102 (e.g., 500 meters); the minimum angle threshold is a preset system parameter (e.g., 10°), representing the minimum allowed sector angle during dynamic adjustment; the straight-line distance is taken from the straight-line distance between the currently processed intersection and the vehicle calculated in step S103 (e.g., intersection A is 150 meters). Correspondingly, it is assumed that the preset scaling factor is... =0.1° / meter. For intersection A with a straight-line distance of 150 meters, the individualized sector angle threshold can be calculated using the above formula. It is 45°.

[0070] In this embodiment, by introducing a preset proportional coefficient, the mathematical basis for dynamic adjustment is clear and controllable, enabling the output individualized sector angle threshold to form an inherent logical unity and smooth transition with the geometric range (radius and angle) initially set by the system. This implementation method not only ensures that the threshold changes monotonically and continuously with distance, achieving the core adjustment goal of "nearer is larger and farther is smaller", effectively addressing the problems of missed detection and false detection, but also possesses high configurability and adaptability due to its parameterized design, significantly improving the practicality and engineering feasibility of the solution.

[0071] In some embodiments, the scaling factor is determined by: determining a second difference between the maximum angle threshold and the minimum angle threshold; dividing the second difference by the maximum search distance, and determining the resulting quotient as the scaling factor.

[0072] For example, the scaling factor satisfies the following formula:

[0073]

[0074] in, This is the maximum angle threshold.

[0075] Accordingly, the maximum angle threshold is taken from the sector angle of the initial sector search region defined in step S102 (e.g., Based on the above proportionality coefficient formula, the following is calculated: It is 0.1° / meter.

[0076] Based on the above embodiments, in some embodiments, the parameters on which the preset distance-angle mapping relationship depends are configured according to the intersection distribution characteristics of the current environment of the vehicle.

[0077] For example, the system can perceive or infer the intersection distribution characteristics of the vehicle's current environment and adaptively configure the parameters upon which the distance-angle mapping relationship depends based on these characteristics. This can be achieved through one or more of the following methods: In one implementation, the system receives road attribute labels (such as "urban expressway" or "urban branch road") from a high-precision map. These labels implicitly contain typical intersection distribution density information, and the system calls the corresponding preset parameter set based on the labels. In another implementation, the system dynamically analyzes historical or real-time road network topology data within a preset radius (such as a 1-kilometer radius) around the vehicle based on its real-time positioning data. By calculating indicators such as the number of road intersections per unit area, it directly quantifies the intersection distribution density of the current environment and dynamically calculates the appropriate parameter values ​​based on this density value using predefined rules or functions. In yet another implementation, the system receives scene classification results (such as "dense intersection area" or "main road corridor area") processed by onboard environmental perception sensors (such as cameras or lidar) or a cloud-based traffic information service platform, and selects the corresponding parameter configuration strategy based on these classification results.

[0078] By using the above methods, the key parameters on which the core search algorithm relies (such as the maximum search distance and the minimum angle threshold) are no longer fixed values, but can be intelligently adjusted according to the actual road environment in which the vehicle is located, thereby enhancing the adaptability and robustness of the solution under different road conditions (such as complex urban areas with dense intersections and open highways with sparse intersections).

[0079] In some embodiments, the configuration is based on the intersection distribution characteristics of the vehicle's current environment, including: identifying whether the road type the vehicle is currently on is a high-density road network or a low-density road network; and configuring with pre-stored parameters corresponding to the road type; wherein the maximum search distance configured for high-density road network is less than the maximum search distance configured for low-density road network.

[0080] For example, the system determines the type of the current road based on electronic map data. High-density road networks typically refer to urban roads, characterized by small intersection spacing and complex road networks. For this type of road, the pre-configured parameter settings set the maximum search distance to a relatively small value (e.g., 300 meters) to reduce the inclusion of too many irrelevant distant intersections, thereby reducing processing burden and the probability of false detections. Low-density road networks typically refer to highways or suburban roads, characterized by large intersection spacing and clear road directionality. For this type of road, the pre-configured parameter settings set the maximum search distance to a relatively large value (e.g., 800 meters) to ensure that key exits or intersections in the distance can be detected in advance.

[0081] Correspondingly, after determining the road type, the corresponding pre-stored parameter configuration is automatically loaded and applied to the subsequent initial sector delineation and dynamic mapping relationship calculation.

[0082] In some embodiments, in addition to using pre-stored parameter configurations corresponding to road types, the method further includes: dynamically adjusting the minimum angle threshold and / or maximum search distance in the pre-stored parameter configurations based on the number of intersections of all potential intersections extracted within the initial sector search area.

[0083] For example, after extracting the initial potential intersection set in step S102, the system counts the number of intersections in the set in real time and uses this number as a feedback signal to dynamically adjust the loaded type-based parameters. Specifically, if the number of intersections is significantly more than the typical expectation for that road type (e.g., initially finding 15 intersections on urban roads, while the typical value is 8), the current maximum search distance can be appropriately reduced, or the current minimum angle threshold can be increased to further tighten the criteria in subsequent dynamic filtering, focusing on closer and more forward intersections; if the number of intersections is significantly less than expected (e.g., only finding 1 intersection on suburban roads), the current maximum search distance can be appropriately increased to expand the search range and reduce intersection omissions. This dynamic adjustment mechanism enables the system to perform secondary optimization based on the number of intersections in the real-time scenario, helping to improve adaptability in complex or atypical road conditions.

[0084] In some embodiments, the dynamic search method for intersections ahead of the vehicle further includes estimating the estimated time to reach the target intersection based on the vehicle's real-time speed and straight-line distance before outputting the target intersection to the navigation or autonomous driving system.

[0085] For example, after determining the target intersection and its straight-line distance, the system obtains the vehicle's current real-time speed, which can be obtained through a Controller Area Network (CAN) bus or IMU, for example. The estimated time is calculated using a formula such as Estimated Time = Straight-line Distance / Real-time Speed. For instance, if the straight-line distance between the target intersection and the vehicle is 200 meters and the vehicle's current speed is 20 meters per second (72 kilometers per hour), the estimated time to reach the target intersection is approximately 10 seconds. The system can output the corresponding estimated time along with the target intersection information, thus providing the navigation system with a time basis for determining the timing of voice prompts and the autonomous driving system with a time basis for lane changing or deceleration planning, significantly enhancing the practical value of the output and the system's decision support capabilities.

[0086] Figure 2 Another flowchart illustrating the dynamic search method for an intersection ahead of a vehicle, provided as an exemplary embodiment of this application. (See diagram below.) Figure 2 As shown, the dynamic search method for the intersection ahead of the vehicle includes the following steps:

[0087] S201. Obtain the vehicle's current position and heading angle.

[0088] S202. Based on the intersection distribution characteristics of the current environment of the vehicle, configure the parameters on which the preset distance-angle mapping relationship depends. The parameters include the maximum search distance, the minimum angle threshold, and the maximum angle threshold.

[0089] S203. Based on the current position and heading angle, combined with the maximum search distance and maximum angle threshold, determine the initial sector search area and extract all potential intersections within the initial sector search area.

[0090] S204. For each potential intersection, determine the straight-line distance between the vehicle and the potential intersection.

[0091] S205. Based on the straight-line distance and the distance-angle mapping relationship, dynamically determine the individualized sector angle threshold for screening potential intersections; wherein, the distance-angle mapping relationship is configured such that the individualized sector angle threshold increases as the straight-line distance decreases.

[0092] S206. Based on the individualized sector angle threshold, determine whether the potential intersection is located within a dynamic sector area with the heading angle as the central axis and the individualized sector angle threshold as the angle. If so, mark the potential intersection as a candidate intersection.

[0093] Conversely, if not, the potential intersection will be excluded and will no longer participate in subsequent screening.

[0094] S207. From all candidate intersections, determine the one with the smallest straight-line distance to the vehicle as the target intersection ahead.

[0095] S208. Based on the vehicle's real-time speed and straight-line distance, estimate the estimated time to reach the intersection ahead of the target.

[0096] S209. Output the target intersection ahead to the navigation or automatic driving system.

[0097] It should be noted that the dynamic search method for intersections ahead of the vehicle in the above embodiments is generally applicable to scenarios where the vehicle is in a cruising state. The "cruising state" referred to here generally refers to a driving phase where the vehicle maintains a relatively stable speed and direction over a long road section, without frequent maneuvers such as turning, changing lanes, or accelerating / decelerating. In this state, the vehicle's heading angle changes gradually, and its driving intention is relatively clear, making the prediction and search for intersections ahead based on the current position and heading angle more representative and reliable. Of course, those skilled in the art will understand that, provided it conforms to the technical principles, this method can be adaptively adjusted and extended to other vehicle operating scenarios with a clear driving direction.

[0098] In summary, this application has at least the following advantages:

[0099] I. By introducing a preset distance-angle mapping relationship (such as a linear equation), an individualized sector angle threshold is dynamically determined for each potential intersection based on its real-time straight-line distance to the vehicle. Following the intelligent adjustment principle that "the closer the straight-line distance, the larger the individualized sector angle threshold; the farther the straight-line distance, the smaller the individualized sector angle threshold," this effectively reduces the inherent defects caused by fixed search angles in related technologies. As the vehicle approaches the intersection, the automatically increasing individualized sector angle threshold creates a wider acceptance range, effectively accommodating positioning and heading errors, thus significantly reducing missed detections at critical intersections. As the vehicle moves away from the intersection, the automatically decreasing individualized sector angle threshold creates a narrower focusing field of view, effectively eliminating interference from laterally irrelevant intersections, thereby greatly reducing false detections. This dynamic angle adjustment strategy significantly improves the accuracy and reliability of searching for intersections ahead.

[0100] Second, after completing the dynamic filtering based on individualized sector angle thresholds, a clear and efficient filtering logic is further executed. Specifically, from all candidate intersections that meet the conditions, the intersection closest to the vehicle in a straight line is selected as the final target intersection. This strategy ensures that the navigation or autonomous driving system can discard redundant information and always focus on the next critical intersection that the vehicle is most urgent, relevant, and likely to enter, even in complex road conditions. This efficient filtering mechanism not only effectively improves the clarity and real-time performance of system decisions but also provides a unique and clear input for subsequent path planning, speed control, or prompt timing calculations, effectively enhancing the collaborative efficiency of the entire system chain.

[0101] Third, by intelligently configuring or fine-tuning the system based on the intersection distribution characteristics of the vehicle's environment (such as dense urban road networks or sparse suburban road networks), the system's universality and robustness are further enhanced. This ensures stable and efficient operation on both densely packed and structurally complex urban roads and sparsely packed and open suburban highways, continuously providing accurate intersection recognition results. This provides a solid technical guarantee for the safe and reliable operation of intelligent navigation and autonomous driving systems in various real-world driving scenarios.

[0102] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0103] Figure 3 A schematic diagram of a dynamic search device for an intersection ahead of a vehicle, provided as an exemplary embodiment of this application. Figure 3 As shown, the dynamic search device 30 for the intersection ahead of the vehicle includes a positioning module 31, a potential intersection screening module 32, a distance calculation module 33, a sector angle adjustment module 34, a precise screening module 35, and a target determination module 36, wherein:

[0104] The positioning module 31 is used to obtain the vehicle's current position and heading angle;

[0105] The potential intersection screening module 32 is used to determine the initial sector search area based on the current position and heading angle, and extract all potential intersections within the initial sector search area;

[0106] The distance calculation module 33 is used to determine the straight-line distance between the vehicle and the potential intersection for each potential intersection;

[0107] The sector angle adjustment module 34 is used to dynamically determine the individualized sector angle threshold for screening potential intersections based on the straight-line distance and according to the preset distance-angle mapping relationship; wherein, the distance-angle mapping relationship is configured such that the individualized sector angle threshold increases as the straight-line distance decreases;

[0108] The precise filtering module 35 is used to determine whether a potential intersection is located within a dynamic sector area with the heading angle as the central axis and the individualized sector angle threshold as the angle, based on the individualized sector angle threshold. If so, the potential intersection is marked as a candidate intersection.

[0109] The target determination module 36 is used to determine the candidate intersection with the smallest straight-line distance to the vehicle from all candidate intersections as the target intersection ahead.

[0110] In one possible implementation, the precise screening module 35 can be specifically used to: determine the azimuth angle between the potential intersection and the vehicle's heading angle; and determine whether the absolute value of the azimuth angle is less than or equal to half of the individualized sector angle threshold.

[0111] In one possible implementation, the sector angle adjustment module 34 can be specifically used to: determine a first difference between the maximum search distance and the straight-line distance, where the maximum search distance is the radius of the initial sector search area; multiply the first difference by a preset proportional coefficient to obtain a product; add the product to a preset minimum angle threshold, and determine the result of the addition as an individualized sector angle threshold; wherein the proportional coefficient is determined based on the maximum angle threshold, the minimum angle threshold, and the maximum search distance, and the maximum angle threshold is the sector angle of the initial sector search area.

[0112] In one possible implementation, the scaling factor is determined by: determining a second difference between the maximum angle threshold and the minimum angle threshold; dividing the second difference by the maximum search distance, and determining the resulting quotient as the scaling factor.

[0113] In one possible implementation, the parameters upon which the preset distance-angle mapping relationship depends are configured based on the intersection distribution characteristics of the vehicle's current environment.

[0114] In one possible implementation, the sector angle adjustment module 34 can also be used to: identify whether the road type currently in which the vehicle is located is a high-density road network or a low-density road network; and configure pre-stored parameters corresponding to the road type; wherein the maximum search distance configured for high-density road network is less than the maximum search distance configured for low-density road network.

[0115] In one possible implementation, based on the pre-stored parameter configuration corresponding to the road type, the sector angle adjustment module 34 can also be used to: dynamically adjust the minimum angle threshold and / or maximum search distance in the pre-stored parameter configuration according to the number of all potential intersections extracted within the initial sector search area.

[0116] In one possible implementation, the target determination module 36 may be specifically used to: estimate the estimated time to reach the target intersection based on the vehicle's real-time speed and straight-line distance before outputting the target intersection to the navigation or autonomous driving system.

[0117] The dynamic search device for intersections ahead of vehicles provided in this application embodiment can execute the technical solution shown in the above-described dynamic search method embodiment for intersections ahead of vehicles. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0118] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0119] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0120] It should be noted that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways; and it should be understood that the division of the various modules of the above device is only a logical functional division, and in actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can all be implemented in software through processing element calls; they can all be implemented in hardware; or some modules can be implemented by processing element calls to software, and some modules can be implemented in hardware. For example, the sector angle adjustment module can be a separately established processing element, or it can be integrated into a chip in the above device. Alternatively, it can be stored as program code in the memory of the above device, and called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the integrated logic circuit in the hardware of the processor element or by software instructions.

[0121] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a System-On-a-Chip (SOC).

[0122] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Video Discs, DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0123] Figure 4 A schematic diagram of the structure of an electronic device provided as an exemplary embodiment of this application. For example... Figure 4 As shown, the electronic device 40 in this embodiment includes:

[0124] At least one processor 41; and a memory 42 communicatively connected to the at least one processor;

[0125] The memory 42 stores instructions that can be executed by at least one processor 41 to cause the electronic device to perform the method as described in any of the above embodiments.

[0126] Alternatively, the memory 42 can be either standalone or integrated with the processor 41.

[0127] The memory 42 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0128] The processor 41 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Specifically, in implementing the dynamic search method for intersections ahead of vehicles described in the foregoing method embodiments, the electronic device may be, for example, an electronic device with processing capabilities such as a server.

[0129] Optionally, the electronic device may also include a communication interface 43. In specific implementations, if the communication interface 43, memory 42, and processor 41 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0130] Optionally, in a specific implementation, if the communication interface 43, memory 42 and processor 41 are integrated on a single chip, then the communication interface 43, memory 42 and processor 41 can communicate through an internal interface.

[0131] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0132] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed, they are used to implement the method steps as described in the above method embodiments. The specific implementation methods and technical effects are similar and will not be repeated here.

[0133] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0134] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in a dynamic search device at an intersection ahead of a vehicle.

[0135] This application also provides a computer program product, including a computer program, which, when executed, implements the method steps as described in the above method embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0136] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0137] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0138] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A dynamic search method for intersections ahead of a vehicle, characterized in that, include: Obtain the vehicle's current position and heading angle; Based on the current position and the heading angle, an initial sector search area is determined, and all potential intersections within the initial sector search area are extracted; For each potential intersection, determine the straight-line distance between the vehicle and the potential intersection; Based on the straight-line distance and according to a preset distance-angle mapping relationship, an individualized sector angle threshold for filtering potential intersections is dynamically determined; wherein, the distance-angle mapping relationship is configured such that the individualized sector angle threshold increases as the straight-line distance decreases; The step of dynamically determining the individualized sector angle threshold for filtering potential intersections based on the straight-line distance and a preset distance-angle mapping relationship includes: Determine a first difference between the maximum search distance and the straight-line distance, wherein the maximum search distance is the radius of the initial sector search region; Multiply the first difference by a preset proportional coefficient to obtain a product; The product is added to a preset minimum angle threshold, and the result of the addition is determined as the individualized sector angle threshold. The scaling factor is determined based on the maximum angle threshold, the minimum angle threshold, and the maximum search distance, wherein the maximum angle threshold is the sector angle of the initial sector search region; The parameters upon which the preset distance-angle mapping relationship depends are configured based on the intersection distribution characteristics of the current environment in which the vehicle is located; The configuration based on the intersection distribution characteristics of the vehicle's current environment includes: Identify whether the road type the vehicle is currently on is a high-density road network or a low-density road network; The pre-stored parameters corresponding to the road type are used for configuration; wherein, the maximum search distance configured for the high-density road network is less than the maximum search distance configured for the low-density road network. Based on the individualized sector angle threshold, it is determined whether the potential intersection is located within a dynamic sector area with the heading angle as the central axis and the individualized sector angle threshold as the angle. If so, the potential intersection is marked as a candidate intersection. From all the candidate intersections, the one with the smallest straight-line distance to the vehicle is selected as the target intersection ahead.

2. The dynamic search method for intersections ahead of a vehicle according to claim 1, characterized in that, The step of determining whether the potential intersection is located within a dynamic sector area centered on the heading angle and defined by the individualized sector angle threshold includes: Determine the azimuth angle between the potential intersection's position relative to the vehicle and the heading angle; Determine whether the absolute value of the azimuth angle is less than or equal to half of the individualized sector angle threshold.

3. The dynamic search method for intersections ahead of a vehicle according to claim 1 or 2, characterized in that, The proportionality coefficient is determined in the following manner: Determine a second difference between the maximum angle threshold and the minimum angle threshold; Divide the second difference by the maximum search distance, and determine the resulting quotient as the proportional coefficient.

4. The dynamic search method for intersections ahead of a vehicle according to claim 1 or 2, characterized in that, In addition to using the pre-stored parameter configuration corresponding to the road type, the method further includes: Based on the number of all potential intersections extracted within the initial sector search area, the minimum angle threshold and / or maximum search distance in the pre-stored parameter configuration are dynamically adjusted.

5. The dynamic search method for intersections ahead of a vehicle according to claim 1 or 2, characterized in that, Also includes: Before outputting the target intersection to the navigation or autonomous driving system, the estimated time to reach the target intersection is estimated based on the vehicle's real-time speed and the straight-line distance.

6. A dynamic search device for intersections ahead of a vehicle, characterized in that, include: The positioning module is used to obtain the vehicle's current position and heading angle; The potential intersection filtering module is used to determine an initial sector search area based on the current position and the heading angle, and to extract all potential intersections within the initial sector search area; The distance calculation module is used to determine the straight-line distance between the vehicle and the potential intersection for each potential intersection; The sector angle adjustment module is used to dynamically determine an individualized sector angle threshold for filtering potential intersections based on the straight-line distance and a preset distance-angle mapping relationship; wherein, the distance-angle mapping relationship is configured such that the individualized sector angle threshold increases as the straight-line distance decreases; the parameters on which the preset distance-angle mapping relationship depends are configured according to the intersection distribution characteristics of the current environment of the vehicle; The sector angle adjustment module is specifically used for: determining a first difference between the maximum search distance and the straight-line distance, wherein the maximum search distance is the radius of the initial sector search area; multiplying the first difference by a preset proportional coefficient to obtain a product; adding the product to a preset minimum angle threshold, and determining the result of the addition as the individualized sector angle threshold; wherein the proportional coefficient is determined based on the maximum angle threshold, the minimum angle threshold, and the maximum search distance, and the maximum angle threshold is the sector angle of the initial sector search area; and the sector angle adjustment module is further used for: identifying whether the road type currently in which the vehicle is located is a high-density road network road or a low-density road network road; configuring pre-stored parameters corresponding to the road type; wherein the maximum search distance configured for the high-density road network road is less than the maximum search distance configured for the low-density road network road; The precise filtering module is used to determine whether the potential intersection is located within a dynamic sector area with the heading angle as the central axis and the individualized sector angle threshold as the angle, based on the individualized sector angle threshold. If so, the potential intersection is marked as a candidate intersection. The target determination module is used to determine the candidate intersection with the smallest straight-line distance to the vehicle from all the candidate intersections as the target intersection ahead.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory is used to store computer-executed instructions; The processor is configured to execute the computer execution instructions to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1 to 5.

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