A vehicle-mounted GPS positioning blind area filling calculation method and system

Abstract

The application relates to the technical field of GPS positioning, and discloses a vehicle-mounted GPS positioning blind area compensation calculation method and system.The method comprises the following steps: acquiring the motion parameters and environment data of a vehicle in real time, wherein the environment data comprises radar echo signals and gradient sensor signals; when the GPS signals of the vehicle are lost, the initial position trajectory of the vehicle is calculated by using the motion parameters; the radar echo signals are analyzed to obtain the environment invariable elements of the surrounding environment corresponding to the vehicle, and the constraint field boundary of the vehicle is constructed in combination with the environment invariable elements; and based on the position mapping relationship between the gradient sensor signals and the constraint field boundary, the offset information of the vehicle is inversed.The application can solve the problem that the prior art lacks spatial constraints on the surrounding environment of the vehicle, the measurement cumulative error of inertial devices is continuously amplified, and the trajectory deduction result deviates from the actual driving path of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of GPS positioning technology, and in particular to a method and system for calculating blind spot compensation in vehicle-mounted GPS positioning. Background Technology

[0002] In-vehicle GPS positioning technology is a core technology in the fields of vehicle navigation and intelligent driving. In scenarios such as tunnels, underground garages, and mountain valleys, GPS positioning is easily lost due to signal obstruction and interference, forming positioning blind spots and threatening driving safety. Currently, the mainstream positioning solutions for GPS blind spots are mostly based on dead reckoning using inertial measurement units. They rely on vehicle motion parameters to complete trajectory extrapolation, but lack spatial constraints on the vehicle's surrounding environment. As the duration of signal loss increases, the cumulative measurement error of the inertial devices will continue to amplify, causing the trajectory extrapolation results to deviate from the actual driving path of the vehicle.

[0003] Furthermore, while the current solution utilizes radar-assisted positioning, it primarily focuses on simple obstacle detection and fails to extract stable, unchanging features from the surrounding environment to establish constraint field boundaries. It also lacks integration with slope sensor signals to separately process vertical and horizontal offsets, resulting in insufficient correction accuracy. Additionally, it lacks an adaptation mechanism for dynamically adjusting based on the driver's steering intentions. Summary of the Invention

[0004] This invention provides a method and system for calculating blind spot compensation in vehicle GPS positioning, the main purpose of which is to address the problems mentioned in the background art above.

[0005] To achieve the above objectives, the present invention provides a method for calculating blind spot compensation in vehicle GPS positioning, comprising: S1: Real-time acquisition of vehicle motion parameters and environmental data, including radar echo signals and slope sensing signals; S2: When the vehicle's GPS signal is lost, the initial position trajectory of the vehicle is calculated using the motion parameters; S3: By analyzing the radar echo signal, the environmental invariant elements of the vehicle's surrounding environment are obtained, and the constraint field boundary of the vehicle is constructed by combining the environmental invariant elements. S4: Based on the position mapping relationship between the slope sensing signal and the constraint field boundary, the vehicle's offset information is inverted, and the initial position trajectory of the vehicle is corrected through the offset information to obtain the vehicle's secondary positioning trajectory; S5: Monitor the driver's steering intention in the vehicle and determine the superposition relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory; S6: Convert the superposition relationship into an adaptive correction factor, and correct the secondary positioning trajectory according to the adaptive correction factor.

[0006] Preferably, the real-time acquisition of vehicle motion parameters and environmental data includes: Activate the inertial measurement unit of the vehicle and monitor the motion parameters of the inertial measurement unit; The vehicle's surrounding environment is scanned using a pre-set radar detector to obtain environmental data about the vehicle.

[0007] Preferably, when the vehicle's GPS signal is lost, calculating the vehicle's initial position trajectory using the motion parameters includes: When the vehicle's GPS signal is lost, the vehicle's baseline trajectory is extracted; Based on the three-axis acceleration components and vehicle velocity vector in the motion parameters, the displacement increment vector of the vehicle is synthesized; The angular velocity component in the motion parameters is used to deduce the change in the vehicle's heading angle; The reference trajectory is corrected by combining the change in heading angle and the displacement increment vector to obtain the initial position trajectory of the vehicle.

[0008] Preferably, the step of obtaining the environmental invariant elements of the vehicle's surrounding environment by analyzing the radar echo signal includes: Scan the pulse scattering intensity sequence of the radar echo signal; Verify the difference in intensity between adjacent pulse scattering intensities in the pulse scattering intensity sequence to obtain a stable set of reflection points; By combining the spatial topology of the stable reflection point set, the static reflection topology of the stable reflection point set is constructed; Extract the geometrically invariant properties from the static reflection topology; The environmental invariant elements of the vehicle's surrounding environment are determined based on the geometrically invariant properties.

[0009] Preferably, constructing the constraint field boundary of the vehicle by combining the environmentally invariant elements includes: Extract the road contour from the environmentally invariant elements and decouple the road contour into two-dimensional projected boundary coordinates; Verify the Euclidean distance continuity of the two-dimensional projected boundary coordinates, and connect the verified two-dimensional projected boundary coordinates into a continuous constrained polyline segment; The portion of the continuous constrained polyline segment that exceeds the road curvature radius limit is removed to obtain the constraint field boundary of the vehicle.

[0010] Preferably, the step of retrieving the vehicle's offset information based on the positional mapping relationship between the slope sensing signal and the constraint field boundary includes: The slope angle in the slope sensing signal is analyzed and converted into a height axis offset in the vertical projection plane; By combining the height axis offset with the constraint field boundary, a dynamic mapping curve between the vehicle's corresponding slope and the boundary is constructed. Mark the spatial interference points between the initial position trajectory and the dynamic mapping curve, and calculate the depth vector and orientation angle of the spatial interference points penetrating the boundary of the constraint field; The penetration vectorization sequence of the initial position trajectory is determined based on the depth vector and the orientation angle; The chassis of the vehicle is subjected to multi-axis motion decoupling mapping through the permeation vectorization sequence to obtain the permeation components of the vehicle's height axis and horizontal axis. The height axis penetration component and the horizontal axis penetration component are integrated into the vehicle's offset information.

[0011] Preferably, the step of correcting the initial position trajectory of the vehicle using the offset information to obtain the secondary positioning trajectory of the vehicle includes: The rationality of the offset information is verified, and the initial position trajectory is corrected and compensated based on the verified offset information to obtain the secondary positioning trajectory of the vehicle.

[0012] Preferably, the step of monitoring the driver's steering intention in the vehicle and determining the superposition relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory includes: The steering wheel angle sensor and gear position signal of the vehicle are dynamically sampled to obtain the driving operation timing sequence; Extract the driver's steering intention vector from the driving operation time sequence; The steering intention vector and the secondary positioning trajectory are superimposed to obtain the superposition relationship.

[0013] Preferably, the step of converting the superposition relationship into an adaptive correction factor and correcting the secondary positioning trajectory according to the adaptive correction factor includes: The superposition relationship is converted into an adaptive correction factor, and the motion trajectory of the secondary positioning trajectory is reconstructed based on the adaptive correction factor.

[0014] A vehicle-mounted GPS positioning blind spot compensation calculation system is provided to implement a method for calculating vehicle-mounted GPS positioning blind spot compensation. The system includes: Data acquisition module 101: used to acquire vehicle motion parameters and environmental data in real time, the environmental data including radar echo signals and slope sensing signals; Trajectory generation module 102: used to calculate the initial position trajectory of the vehicle using the motion parameters when the vehicle's GPS signal is lost; Constraint module 103: used to obtain the environmental invariant elements of the vehicle's surrounding environment by analyzing the radar echo signal, and to construct the constraint field boundary of the vehicle by combining the environmental invariant elements. The trajectory correction module 104 is used to invert the vehicle's offset information based on the position mapping relationship between the slope sensing signal and the constraint field boundary, and correct the vehicle's initial position trajectory through the offset information to obtain the vehicle's secondary positioning trajectory. Intent overlay module 105: used to monitor the driver's steering intention in the vehicle and determine the overlay relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory; Final generation module 106: used to convert the superposition relationship into an adaptive correction factor, and correct the secondary positioning trajectory according to the adaptive correction factor.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention extracts static and invariant elements of the surrounding environment by analyzing radar echo signals, constructs road constraint field boundaries, and establishes a reliable spatial constraint benchmark for trajectory extrapolation. This overcomes the technical limitations of traditional dead reckoning, which relies solely on kinematic parameters and lacks environmental boundary verification. By combining the positional mapping relationship between slope sensing signals and constraint field boundaries, the invention decouples the multi-axis motion of the vehicle, inverts the vertical and horizontal components of trajectory offset, and corrects the initial trajectory, significantly suppressing the cumulative error of inertial devices.

[0016] This invention extracts the driver's steering intention vector by dynamically sampling the steering wheel angle and gear position signals, clarifies the spatial vector superposition relationship between the vector and the secondary positioning trajectory, and converts it into an adaptive correction factor to complete the final optimization of the trajectory. This solves the problem of low matching degree between the trajectory and actual driving behavior in existing compensation schemes, and makes the final output positioning trajectory highly consistent with the actual driving state of the vehicle. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for calculating blind spot compensation in vehicle GPS positioning according to an embodiment of the present invention. Figure 2 This is a functional module diagram of a vehicle-mounted GPS positioning blind spot compensation calculation system provided in an embodiment of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a method for calculating blind spot compensation in vehicle GPS positioning. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a method for calculating blind spot compensation in vehicle GPS positioning according to an embodiment of the present invention. In this embodiment, the method for calculating blind spot compensation in vehicle GPS positioning includes: S1: Real-time acquisition of vehicle motion parameters and environmental data, including radar echo signals and slope sensing signals.

[0021] In this embodiment, the real-time acquisition of vehicle motion parameters and environmental data includes: Activate the inertial measurement unit of the vehicle and monitor the motion parameters of the inertial measurement unit; The vehicle's surrounding environment is scanned using a pre-set radar detector to obtain environmental data about the vehicle.

[0022] Specifically, vehicle motion parameters are physical parameters that reflect the real-time driving motion state of a vehicle. They are the core basic data for calculating the vehicle's driving trajectory. They are collected from the vehicle's inertial measurement unit and can completely capture various driving states of the vehicle in three-dimensional space, such as acceleration, deceleration, steering, and attitude changes.

[0023] Environmental data is core data that reflects the surrounding physical environment and vertical and horizontal conditions of the road during vehicle operation, providing a stable benchmark for vehicle positioning through the surrounding fixed environment.

[0024] Radar echo signals are pulse signals reflected back from objects such as road boundaries, walls, guardrails, pillars, and rock walls around the vehicle after the vehicle-mounted radar detector emits detection pulses into the surrounding environment.

[0025] Slope sensing signals are another core component of environmental data. They are collected by the vehicle's slope sensors and can reflect information about the vertical road conditions, such as the slope angle and uphill / downhill direction of the road the vehicle is currently traveling on.

[0026] The inertial measurement unit (IMU) is a core motion sensing device installed at the core of the vehicle body. It has built-in core sensing elements such as a three-axis accelerometer and a three-axis gyroscope to independently collect motion state data in the three-dimensional space of the vehicle.

[0027] The motion parameters of the inertial measurement unit are output in real time by the activated inertial measurement unit, containing core data that can reflect the real-time motion state of the vehicle.

[0028] The pre-set radar detector is an on-board radar detection device that is pre-installed on the vehicle and has its core parameters configured in advance. It is the sole source of radar echo signals.

[0029] Environmental data refers to all the physical spatial environment surrounding the vehicle during its operation, including the boundary outline of the road, guardrails on both sides of the road, tunnel walls, underground parking garage walls and pillars, mountain rock walls, and various static fixed objects such as surrounding fixed buildings.

[0030] In detail, the on-board computing unit serves as the core execution entity, synchronously connecting with various vehicle sensing devices to mark all collected data with a unified timestamp to ensure time synchronization. During the collection process, the validity of the data is verified in real time and invalid data is removed. First, the vehicle's inertial measurement unit and the preset radar detector are activated simultaneously. After the power-on self-test, zero-point and core parameter calibration of both are completed, the motion parameters output by the inertial measurement unit are continuously monitored at a high-frequency fixed sampling frequency. At the same time, the radar detector performs a 360-degree full-coverage cyclic scan of the vehicle's surrounding environment according to preset parameters such as transmission power and scanning frequency. The reflected pulses are received in real time and converted into standardized environmental data. The slope sensing signal, radar echo signal and motion parameters are timestamped and time-series matched and then cached in a unified manner to ensure that continuous and effective data can be used seamlessly in the event of GPS signal loss, without any data gaps.

[0031] S2: When the vehicle loses its GPS signal, the initial position trajectory of the vehicle is calculated using the motion parameters.

[0032] In this embodiment, the step of calculating the initial position trajectory of the vehicle using the motion parameters when the vehicle's GPS signal is lost includes: When the vehicle's GPS signal is lost, the vehicle's baseline trajectory is extracted; Based on the three-axis acceleration components and vehicle velocity vector in the motion parameters, the displacement increment vector of the vehicle is synthesized; The angular velocity component in the motion parameters is used to deduce the change in the vehicle's heading angle; The reference trajectory is corrected by combining the change in heading angle and the displacement increment vector to obtain the initial position trajectory of the vehicle.

[0033] Specifically, the baseline trajectory refers to the driving path output by the Global Positioning System at the last moment before the GPS signal was lost.

[0034] The three-axis acceleration components are the core components of motion parameters, referring to the acceleration values ​​of the vehicle in three mutually perpendicular directions: the X, Y, and Z axes (e.g., vertical, uphill, downhill). These components collectively describe the rate and direction of the vehicle's velocity change in three-dimensional space.

[0035] Vehicle speed vector refers to the instantaneous speed value with directional information obtained from the vehicle's wheel speed sensors or inertial measurement unit.

[0036] The displacement increment vector refers to the direction and distance by which a vehicle moves from one position to the next within a very short time interval.

[0037] Angular velocity components refer to the angular rates of a vehicle's rotation around the X-axis (e.g., roll rate, indicating left-right tilt), Y-axis (e.g., pitch rate, indicating nose-to-nose movement), and Z-axis (e.g., yaw rate, indicating left-right turning). The yaw rate along the Z-axis plays a decisive role in the vehicle's horizontal heading.

[0038] The heading angle change refers to the angular change in the direction in which a vehicle's nose faces relative to its initial reference direction during driving. It reflects whether the vehicle is turning left, right, or continuing straight.

[0039] The initial position trajectory refers to the system continuously correcting and extending the final baseline trajectory based solely on the vehicle's own motion parameters after the GPS signal is lost.

[0040] In detail, when the vehicle's GPS signal is lost, the vehicle's onboard computing unit first determines that the signal is interrupted, and immediately retrieves the accurate driving path at the last moment before the GPS failure from the memory as the reference trajectory, which is used as the starting point for calculation. Subsequently, motion parameters were continuously collected, and the three-axis acceleration components were integrated twice and fused with the vehicle speed vector to synthesize the displacement increment vector describing the actual direction and distance of vehicle movement per unit time. On the other hand, the yaw rate in the angular velocity component is accumulated to deduce the change in the vehicle's heading angle, thus capturing every turn of the vehicle. Finally, the changes in heading angle are dynamically superimposed onto the direction of the displacement increment vector to stitch the path together, and the path is extended outward from the end of the reference trajectory. By continuously correcting the direction and distance of each tiny movement, the initial position trajectory is deduced.

[0041] S3: By analyzing the radar echo signal, the environmental invariant elements of the vehicle's surrounding environment are obtained, and the constraint field boundary of the vehicle is constructed by combining the environmental invariant elements.

[0042] In this embodiment, the step of obtaining the environmental invariant elements of the vehicle's surrounding environment by analyzing the radar echo signal includes: Scan the pulse scattering intensity sequence of the radar echo signal; Verify the difference in intensity between adjacent pulse scattering intensities in the pulse scattering intensity sequence to obtain a stable set of reflection points; By combining the spatial topology of the stable reflection point set, the static reflection topology of the stable reflection point set is constructed; Extract the geometrically invariant properties from the static reflection topology; The environmental invariant elements of the vehicle's surrounding environment are determined based on the geometrically invariant properties.

[0043] Specifically, the pulse scattering intensity sequence refers to a continuous data sequence that is arranged in time or space order after the radar echo signal is scanned, and reflects the intensity of reflected energy at different locations.

[0044] The difference in scattering intensity between adjacent pulses refers to the degree of change in the scattering intensity values ​​between two consecutive pulses in the pulse scattering intensity sequence.

[0045] A stable reflection point cluster refers to a set of reflection points selected from the original reflection points after verifying the difference in the intensity of adjacent pulse scattering in the pulse scattering intensity sequence. These reflection points are characterized by stable reflection properties and are not easily affected by environmental interference.

[0046] Spatial topological association refers to the geometric and positional relationships such as adjacency, continuity, and connection formed between various reflection points in the stable reflection point cloud based on their actual spatial locations.

[0047] Static reflection topology refers to a networked spatial model that connects the points in the stable reflection point cloud based on the spatial topological structure association of the stable reflection point cloud, thereby constructing a model that reflects the overall outline and structural relationship of static objects in the surrounding environment.

[0048] Geometrically invariant properties refer to spatial geometric features extracted from the static reflection topology that do not change with vehicle movement or viewing angle, such as straight boundaries, constant curvature, fixed angles, and parallel relationships.

[0049] Environmentally invariant elements refer to physical elements that exist in the vehicle's surrounding environment for a long time and have a stable structure, as determined by the aforementioned geometrically invariant properties. These include static spatial structures such as road outlines, guardrail boundaries, wall facades, and tunnel sidewalls.

[0050] In detail, after obtaining the radar echo signal, the on-board computing unit performs a full scan of the signal and converts it into a sequence of pulse scattering intensities arranged in order.

[0051] Then, the computing unit verifies the degree of difference in scattering intensity between adjacent pulses in the sequence one by one, filters out and retains points with small differences and stable reflection characteristics, and removes points with large differences that may be caused by moving objects or noise, thereby extracting a stable reflection point cluster from the original data. Subsequently, the computing unit takes each point in the stable reflection point cloud as a node, analyzes and establishes the adjacent and continuous connection relationships between points based on their actual spatial locations. Through this spatial topological association, the discrete reflection points are integrated into a static reflection topology that can completely reflect the outline and structure of the surrounding static objects. Meanwhile, the computing unit performs structural analysis on the static reflection topology, identifying and extracting geometrically invariant properties such as straight boundaries and constant curvature arcs that do not change with vehicle movement.

[0052] Finally, the computing unit maps these geometric features to the actual physical environment around the vehicle, identifies the specific structures represented by these geometric features, such as road outlines, guardrails, and walls, and determines the environmentally invariant elements.

[0053] In this embodiment, constructing the constraint field boundary of the vehicle by combining the environmentally invariant elements includes: Extract the road contour from the environmentally invariant elements and decouple the road contour into two-dimensional projected boundary coordinates; Verify the Euclidean distance continuity of the two-dimensional projected boundary coordinates, and connect the verified two-dimensional projected boundary coordinates into a continuous constrained polyline segment; The portion of the continuous constrained polyline segment that exceeds the road curvature radius limit is removed to obtain the constraint field boundary of the vehicle.

[0054] Specifically, the constraint field boundary refers to the boundary benchmark constructed based on the invariant elements of the vehicle's surrounding environment, which can limit the legal driving space of the vehicle. It is used to verify whether the vehicle's projected trajectory exceeds the driving range of the actual road.

[0055] Road outline refers to the features extracted from unchanging environmental factors that reflect the overall shape, lateral boundary range, and direction of the road on which a vehicle is currently traveling. It is the boundary feature that distinguishes the drivable area from the non-drivable area of ​​a road.

[0056] Two-dimensional projection boundary coordinates refer to the planar coordinate values ​​of road boundary points obtained by decoupling the road outline in three-dimensional space onto the horizontal two-dimensional plane of vehicle travel. They represent the specific spatial location of the road boundary in the horizontal driving plane of the vehicle.

[0057] Euclidean distance continuity refers to the degree of continuity of the straight-line distance between two adjacent coordinate points in a two-dimensional projected boundary coordinate sequence. It is used as a criterion for verifying whether adjacent road boundary points belong to the same continuous road boundary.

[0058] A continuous constrained polyline segment refers to a polyline shape that can represent the continuous direction of a road boundary by connecting the two-dimensional projected boundary coordinates that have passed the Euclidean distance continuity test in sequence according to their spatial position.

[0059] The road curvature radius limit refers to the minimum curvature radius threshold corresponding to the road where the vehicle is currently traveling, which conforms to road design specifications and the actual driving capacity of the vehicle. It is used as a criterion for eliminating abnormal road boundary segments.

[0060] In detail, firstly, the road contour that can characterize the boundary shape of the road where the vehicle is currently driving is extracted from the environmental invariant elements. Then, the road contour features in three-dimensional space are mapped to the horizontal two-dimensional plane where the vehicle is driving, completing the decoupling process of the spatial dimension and transforming it into two-dimensional projected boundary coordinates that can be directly used for spatial positioning calculation. Then, the continuity of all generated two-dimensional projected boundary coordinates is checked. The continuity of the Euclidean distance between adjacent coordinate points is checked one by one. Valid coordinate points that meet the continuity characteristics are selected. Then, all the two-dimensional projected boundary coordinates that have passed the check are connected end to end in order of their actual position in space to form a continuous constrained polyline segment that can completely reflect the continuous direction of the road boundary. Finally, based on the curvature radius limit of the current road, the curvature compliance of each segment of the continuous constrained polyline is checked, and abnormal parts in the polyline segment that exceed the road curvature radius limit, do not conform to the actual road design specifications and the normal driving logic of the vehicle are identified and eliminated, thus obtaining the vehicle constraint field boundary.

[0061] S4: Based on the position mapping relationship between the slope sensing signal and the constraint field boundary, the vehicle's offset information is inverted, and the initial position trajectory of the vehicle is corrected through the offset information to obtain the vehicle's secondary positioning trajectory.

[0062] In this embodiment, the step of retrieving the vehicle's offset information based on the positional mapping relationship between the slope sensing signal and the constraint field boundary includes: The slope angle in the slope sensing signal is analyzed and converted into a height axis offset in the vertical projection plane; By combining the height axis offset with the constraint field boundary, a dynamic mapping curve between the vehicle's corresponding slope and the boundary is constructed. Mark the spatial interference points between the initial position trajectory and the dynamic mapping curve, and calculate the depth vector and orientation angle of the spatial interference points penetrating the boundary of the constraint field; The penetration vectorization sequence of the initial position trajectory is determined based on the depth vector and the orientation angle; The chassis of the vehicle is subjected to multi-axis motion decoupling mapping through the permeation vectorization sequence to obtain the permeation components of the vehicle's height axis and horizontal axis. The height axis penetration component and the horizontal axis penetration component are integrated into the vehicle's offset information.

[0063] Specifically, the slope sensing signal is sensor data collected in real time by the slope sensor on the vehicle, reflecting the vertical road conditions such as the slope angle and uphill / downhill direction of the road on which the vehicle is currently traveling.

[0064] The position mapping relationship refers to the correspondence between the road vertical slope information reflected by the slope sensing signal and the road lateral spatial boundary information represented by the constraint field boundary within the same vehicle driving space coordinate system.

[0065] Offset information refers to the spatial offset of the initial position trajectory of a vehicle calculated solely based on motion parameters, relative to the vehicle's actual driving path. It includes offset information in both the vertical and horizontal directions.

[0066] Secondary positioning trajectory refers to a positioning trajectory with higher accuracy and better conformity to the actual driving state of the vehicle after correcting and compensating the initial position trajectory with offset information.

[0067] The slope angle refers to the angle between the slope of the road surface where the vehicle is currently traveling and the horizontal plane, which is obtained from the slope sensor signal.

[0068] A vertical projection plane is a vertical projection plane that is perpendicular to the horizontal ground on which the vehicle travels and is used to characterize changes in the vehicle's travel height.

[0069] The height axis offset refers to the change in the vehicle's position along the height direction during driving, obtained after converting the slope angle to the vertical projection plane.

[0070] The dynamic mapping curve refers to a continuous correlation curve constructed by combining the height axis offset and the constraint field boundary, which can synchronously reflect the changes in the vertical slope of the road and the changes in the lateral drivable boundary during vehicle driving.

[0071] Furthermore, a spatial interference point refers to a path point on the initial position trajectory that intersects the dynamic mapping curve or the boundary of the constraint field in space and exceeds the drivable range.

[0072] The depth vector is the set of distance values ​​and penetration directions of the initial position trajectory penetrating the boundary of the constraint field at the spatial interference point, which can reflect the degree to which the initial trajectory exceeds the drivable boundary.

[0073] The orientation angle refers to the angle between the direction of travel of the initial position trajectory through the boundary of the constraint field at the spatial interference point and the normal direction of the boundary of the constraint field, reflecting the orientation of the initial trajectory offset.

[0074] The permeation vectorization sequence refers to a continuous offset vector sequence formed by arranging the depth vectors and orientation angles corresponding to all spatial interference points in an orderly manner according to the temporal and spatial order of the initial position trajectory. It reflects the offset changes throughout the entire initial trajectory.

[0075] Multi-axis motion decoupling mapping refers to the process of decomposing the multi-directional motions of a vehicle chassis coupled together in three-dimensional space into independent vertical and horizontal axis motions, separating the independent offset components of the initial trajectory in the vertical and horizontal directions.

[0076] The height axis penetration component refers to the offset component of the vehicle in the vertical height direction obtained after multi-axis motion decoupling mapping. It reflects the vertical trajectory offset of the vehicle caused by the slope change, as well as the intrusion depth of the vehicle in the vertical direction that breaks through the boundary of the constraint field, such as the squeezing distance between the roof of the vehicle and the top of the tunnel.

[0077] The horizontal axis penetration component refers to the offset component of the vehicle in the horizontal driving plane obtained after multi-axis motion decoupling mapping, reflecting the trajectory offset of the vehicle in the transverse plane of the road.

[0078] In detail, the slope sensing signal collected by the vehicle slope sensor is first analyzed and processed to extract the slope angle that reflects the vertical tilt of the road from the original sensing signal. Then, the slope angle is mapped onto the vertical projection plane to complete the conversion from angle to position change, and the height axis offset along the height direction during vehicle travel is obtained. By spatially associating and matching the vertical offset data with the constraint field boundary, a dynamic mapping curve can be constructed that can synchronously correspond to the changes in the vertical slope of the road and the changes in the lateral drivable boundary during vehicle driving. The initial position trajectory is compared with the dynamic mapping curve and the constraint field boundary in the entire spatial position. The spatial interference points where the initial position trajectory exceeds the driving range and intersects with the dynamic mapping curve and the constraint field boundary are marked one by one. The depth vector and direction angle of the initial position trajectory penetrating the constraint field boundary at the location are calculated to obtain the degree and orientation of the trajectory offset at each location. According to the driving time sequence and spatial position order of the initial position trajectory, the depth vectors and orientation angles corresponding to all interference points are arranged in an orderly manner to form a permeation vectorization sequence that can reflect the offset change law of the entire initial trajectory. Based on the continuous offset data of this sequence, the three-dimensional multi-axis motion of the vehicle chassis is decoupled and mapped, and the originally coupled vertical and horizontal motion of the vehicle is decomposed into two independent dimensions. The vertical axis penetration component of the initial position trajectory and the horizontal axis penetration component in the horizontal driving plane are extracted respectively. By spatially integrating the height axis penetration component and the horizontal axis penetration component, vehicle offset information is generated regarding the omnidirectional offset of the initial position trajectory in three-dimensional space.

[0079] In this embodiment, the step of correcting the initial position trajectory of the vehicle using the offset information to obtain the secondary positioning trajectory of the vehicle includes: The rationality of the offset information is verified, and the initial position trajectory is corrected and compensated based on the verified offset information to obtain the secondary positioning trajectory of the vehicle.

[0080] Specifically, the secondary positioning trajectory refers to the vehicle's driving trajectory with higher positioning accuracy and stronger conformity to the actual driving path of the vehicle after the initial position trajectory has been corrected and compensated by effective offset information.

[0081] The rationality of offset information refers to whether the magnitude, direction, and temporal variation of the offset information conform to the physical motion of normal vehicle driving, the drivable space range defined by the boundary of the constraint field, and the effective acquisition range of the on-board sensing equipment.

[0082] In detail, during the verification process, the numerical range, offset direction, and temporal change pattern of each set of offset information are checked one by one according to the time sequence of vehicle driving to confirm whether it conforms to the physical motion logic of normal vehicle driving, the legal drivable space limit defined by the boundary of the constraint field, and the effective acquisition threshold of the on-board sensor equipment. Invalid and abnormal offset data that do not meet the reasonableness requirements are eliminated simultaneously, and effective offset information that can truly reflect the actual offset of the vehicle trajectory is selected. After verifying the rationality of the offset information and filtering the valid data, the valid offset information that has been verified is used as the core correction benchmark. Following the time sequence of vehicle travel and the correspondence between spatial positions, the spatial position of each path point of the initial position trajectory is corrected and compensated point by point and segment by segment. The actual offsets in the vertical height direction and horizontal travel direction corresponding to the offset information are back-superimposed and matched to the corresponding path points of the initial position trajectory to offset the spatial position offset caused by the cumulative error of the inertial measurement unit.

[0083] S5: Monitor the driver's steering intention in the vehicle and determine the superposition relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory.

[0084] In this embodiment, monitoring the driver's steering intention in the vehicle and determining the superposition relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory includes: The steering wheel angle sensor and gear position signal of the vehicle are dynamically sampled to obtain the driving operation timing sequence; Extract the driver's steering intention vector from the driving operation time sequence; The steering intention vector and the secondary positioning trajectory are superimposed to obtain the superposition relationship.

[0085] Specifically, the driver's steering intention refers to the driver's subjective driving intention, conveyed through vehicle driving operations, to change the vehicle's course and control the vehicle to complete the steering maneuver.

[0086] A spatial vector refers to the vector data corresponding to each path point in a secondary positioning trajectory, which simultaneously carries information about the vehicle's driving direction and displacement. It can characterize the vehicle's driving status, spatial orientation, and positional change trend at the corresponding point.

[0087] Superposition relationship refers to the matching, association and fusion relationship between the driver's steering intention vector and the spatial vector corresponding to the secondary positioning trajectory in terms of driving direction, displacement change and temporal sequence.

[0088] A steering wheel angle sensor is an onboard sensing device installed on a vehicle's steering system that can collect steering operation data such as the steering wheel's rotation angle, direction, and rate of rotation in real time.

[0089] The gear position signal is an electrical signal output by the gear position sensor unit of the vehicle's transmission, which can reflect the vehicle's current gear and driving status in real time.

[0090] Dynamic sampling refers to the synchronous, continuous, and real-time acquisition of the output data of the steering wheel angle sensor and the gear position signal according to a preset high-frequency fixed sampling frequency.

[0091] Driving operation timing sequence refers to a continuous set of driving operation data formed by aligning dynamically sampled steering wheel angle data and gear signals with a unified timestamp and then arranging them in the order of their acquisition time.

[0092] Steering intention vector refers to vector data extracted from the timing sequence of driving operations that can characterize the driver's steering intention, including steering direction, steering amplitude, steering rate, and desired heading change.

[0093] In detail, according to a pre-set high-frequency fixed sampling frequency, the steering operation data output in real time by the steering wheel angle sensor on the vehicle and the gear signal output by the vehicle transmission are dynamically sampled. During the sampling process, a unified timestamp is marked for each set of synchronously collected steering wheel angle data and gear signal. At the same time, the validity of the collected data is verified in real time, and invalid or abnormal interference data is removed. Then, all the driving operation data that has passed the validity verification are arranged and regulated in an orderly manner according to the time sequence of collection to form a driving operation time sequence that can completely and coherently reflect the driving operation behavior change pattern of the driver throughout the entire period. Full-time feature extraction is performed on the time series. Combining the continuous changes in the steering wheel angle, rotation amplitude, and rotation rate in the time series, as well as the real-time driving status of the vehicle reflected by the gear signal, a steering intention vector that can completely and quantitatively represent the driver's subjective steering intention is extracted from the continuous time series data, clarifying the core needs of the driver's expected changes in vehicle driving direction and steering. Then, within the three-dimensional coordinate system of vehicle driving, the extracted steering intention vector is matched and superimposed point by point and segment by segment with the spatial vector of each path point corresponding to the secondary positioning trajectory, and the correspondence and fusion characteristics of the two in terms of vehicle driving direction, displacement change, and temporal progression are verified.

[0094] The formula for calculating the superposition relationship is: in: It is an additive relationship. For steering wheel angle, The instantaneous linear velocity of the secondary positioning trajectory. This is the maximum angular velocity limit for the steering system corresponding to the vehicle. Let be the wheelbase of the vehicle. Let be the heading angle of the secondary positioning trajectory. The desired heading angle corresponding to the steering intention vector.

[0095] Specifically, The superposition relationship is a quantitative representation of the matching, association, and fusion relationship formed between the driver's steering intention and the spatial vector of the secondary positioning trajectory. It is used to convert into an adaptive correction factor to complete the final trajectory correction.

[0096] The steering wheel angle is collected in real time by the vehicle's steering wheel angle sensor. It is a core physical quantity that reflects the driver's steering operation and represents the angle at which the driver turns the steering wheel.

[0097] The instantaneous linear velocity of the secondary positioning trajectory is the vehicle's instantaneous linear velocity at each path point in the secondary positioning trajectory obtained by correcting the initial position trajectory with offset information. This reflects the vehicle's speed at the corresponding positioning point during blind spot compensation calculation.

[0098] The maximum angular velocity limit of the steering system corresponding to the vehicle is a hardware performance parameter of the vehicle steering system itself. It is the upper limit of the angular velocity of the steering system rotation and a limiting threshold for the superposition of driving intention and trajectory vector, avoiding invalid calculations that exceed the vehicle's physical performance.

[0099] The wheelbase of the vehicle is an inherent structural parameter of the vehicle itself, referring to the distance from the center of the front axle to the center of the rear axle. It is an important basic parameter for deriving changes in the vehicle's driving trajectory in conjunction with steering operations.

[0100] The heading angle of the secondary positioning trajectory is the vehicle's heading angle corresponding to each path point in the secondary positioning trajectory. It reflects the vehicle's driving direction at the corresponding position after blind spot compensation calculation and is the core directional feature of the positioning trajectory space vector.

[0101] The desired heading angle corresponding to the steering intention vector is the driving heading angle that the driver expects the vehicle to reach, which is extracted from the driver's driving operation time sequence and directly reflects the driver's subjective steering intention.

[0102] In detail, by using the vehicle's own inherent parameters , To calculate the basic threshold and benchmark, core driver operation parameters collected during GPS blind spot compensation were used. Key parameters of the secondary positioning trajectory obtained by the supplementary calculation , And the core parameters of steering intent extracted from driving operations. ; By coupling the parameters, the actual steering operation and subjective steering expectation of the driver are combined with the speed and direction characteristics of the secondary positioning trajectory after blind spot compensation, and the spatial vector superposition relationship between the two is quantitatively represented.

[0103] Among them, the product of the steering wheel angle and the instantaneous linear velocity of the secondary positioning trajectory correlates the driver's steering operation with the vehicle's current positioning trajectory speed, reflecting the actual effect of the steering operation at the current driving speed. The product of the maximum angular velocity limit of the vehicle steering system and the wheelbase serves as a constraint at the vehicle's physical performance level, limiting the correlation between steering operation and trajectory speed to ensure it conforms to the physical logic of actual vehicle driving. The difference between the heading angle of the secondary positioning trajectory and the expected heading angle of the steering intention vector is calculated to reflect the degree of deviation between the driving direction of the current replacement trajectory and the direction subjectively desired by the driver. The degree of deviation is then quantified by cosine operation. Combined with the nonlinear mapping of the result of the calculation of the preceding parameters by the hyperbolic tangent function, the superposition value of the superposition relationship between the driver's steering intention and the spatial vector of the secondary positioning trajectory is reflected.

[0104] S6: Convert the superposition relationship into an adaptive correction factor, and correct the secondary positioning trajectory according to the adaptive correction factor.

[0105] In this embodiment, converting the superposition relationship into an adaptive correction factor and correcting the secondary positioning trajectory according to the adaptive correction factor includes: The superposition relationship is converted into an adaptive correction factor, and the motion trajectory of the secondary positioning trajectory is reconstructed based on the adaptive correction factor.

[0106] Specifically, the adaptive correction factor refers to a quantitative correction parameter obtained based on the superposition relationship, which can dynamically adjust the trajectory correction magnitude and correction direction according to the driver's steering intention and the real-time driving status of the vehicle.

[0107] The secondary positioning trajectory is a vehicle driving trajectory with high positioning accuracy and conforms to the road driving space constraints after the initial position trajectory is spatially corrected and compensated by the offset vector obtained by inversion.

[0108] The trajectory is the final vehicle travel path that perfectly matches the driver's actual driving intention, the vehicle's actual driving state, and the constraints of the road's drivable space.

[0109] In detail, when the vehicle is in a positioning blind spot where GPS signals are lost, such as in tunnels, underground garages, or mountain canyons, the first step is to perform parameter conversion processing on the superposition relationship. The core features contained in the superposition relationship, such as the driver's steering direction, steering amplitude, changes in desired heading, and the matching deviation between the secondary positioning trajectory and the driving intention, are converted into adaptive correction factors that can dynamically adapt to the real-time driving status of the vehicle and adjust the trajectory correction amplitude and direction, ensuring that the correction factors can accurately match the driver's actual driving operation needs. After the conversion and generation of the adaptive correction factor are completed, the adaptive correction factor is used as the final trajectory optimization and adjustment benchmark. Following the temporal sequence of vehicle travel and the correspondence between spatial positions, a full-process, full-dimensional reconstruction and optimization is carried out on each path point and each continuous driving path of the secondary positioning trajectory. The heading angle, displacement increment, spatial direction and driving trend of the trajectory are adjusted point by point and segment by segment according to the adaptive correction factor. This accurately offsets the heading deviation and positional offset between the secondary positioning trajectory and the driver's actual steering intention, so that the reconstructed trajectory fully matches the driver's subjective driving intention and the actual driving state of the vehicle. At the same time, it matches the drivable space limit of the road constraint field boundary constructed in the early stage, and finally completes the final correction and optimization of the secondary positioning trajectory.

[0110] like Figure 2 The diagram shown is a functional block diagram of a vehicle-mounted GPS positioning blind spot compensation calculation system provided in an embodiment of the present invention.

[0111] The vehicle-mounted GPS positioning blind spot compensation calculation system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the vehicle-mounted GPS positioning blind spot compensation calculation system 100 may include a data acquisition module 101, a trajectory generation module 102, a constraint module 103, a trajectory correction module 104, an intent overlay module 105, and a final generation module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0112] In this embodiment, the functions of each module / unit are as follows: Data acquisition module 101: used to acquire vehicle motion parameters and environmental data in real time, the environmental data including radar echo signals and slope sensing signals; Trajectory generation module 102: used to calculate the initial position trajectory of the vehicle using the motion parameters when the vehicle's GPS signal is lost; Constraint module 103: used to obtain the environmental invariant elements of the vehicle's surrounding environment by analyzing the radar echo signal, and to construct the constraint field boundary of the vehicle by combining the environmental invariant elements. The trajectory correction module 104 is used to invert the vehicle's offset information based on the position mapping relationship between the slope sensing signal and the constraint field boundary, and correct the vehicle's initial position trajectory through the offset information to obtain the vehicle's secondary positioning trajectory. Intent overlay module 105: used to monitor the driver's steering intention in the vehicle and determine the overlay relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory; Final generation module 106: used to convert the superposition relationship into an adaptive correction factor, and correct the secondary positioning trajectory according to the adaptive correction factor.

[0113] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0114] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0115] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0116] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0117] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for calculating blind spot compensation in vehicle GPS positioning, characterized in that, The method includes: S1: Real-time acquisition of vehicle motion parameters and environmental data, including radar echo signals and slope sensing signals; S2: When the vehicle's GPS signal is lost, the initial position trajectory of the vehicle is calculated using the motion parameters; S3: By analyzing the radar echo signal, the environmental invariant elements of the vehicle's surrounding environment are obtained, and the constraint field boundary of the vehicle is constructed by combining the environmental invariant elements. S4: Based on the position mapping relationship between the slope sensing signal and the constraint field boundary, the vehicle's offset information is inverted, and the initial position trajectory of the vehicle is corrected through the offset information to obtain the vehicle's secondary positioning trajectory; S5: Monitor the driver's steering intention in the vehicle and determine the superposition relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory; S6: Convert the superposition relationship into an adaptive correction factor, and correct the secondary positioning trajectory according to the adaptive correction factor.

2. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 1, characterized in that, The real-time acquisition of vehicle motion parameters and environmental data includes: Activate the inertial measurement unit of the vehicle and monitor the motion parameters of the inertial measurement unit; The vehicle's surrounding environment is scanned using a pre-set radar detector to obtain environmental data about the vehicle.

3. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 2, characterized in that, When the vehicle's GPS signal is lost, calculating the vehicle's initial position trajectory using the motion parameters includes: When the vehicle's GPS signal is lost, the vehicle's baseline trajectory is extracted; Based on the three-axis acceleration components and vehicle velocity vector in the motion parameters, the displacement increment vector of the vehicle is synthesized; The angular velocity component in the motion parameters is used to deduce the change in the vehicle's heading angle; The reference trajectory is corrected by combining the change in heading angle and the displacement increment vector to obtain the initial position trajectory of the vehicle.

4. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 3, characterized in that, The step of analyzing the radar echo signal to obtain the environmental invariant elements of the vehicle's surrounding environment includes: Scan the pulse scattering intensity sequence of the radar echo signal; Verify the difference in intensity between adjacent pulse scattering intensities in the pulse scattering intensity sequence to obtain a stable set of reflection points; By combining the spatial topology of the stable reflection point set, the static reflection topology of the stable reflection point set is constructed; Extract the geometrically invariant properties from the static reflection topology; The environmental invariant elements of the vehicle's surrounding environment are determined based on the geometrically invariant properties.

5. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 1, characterized in that, The construction of the constraint field boundary of the vehicle by combining the environmentally invariant elements includes: Extract the road contour from the environmentally invariant elements and decouple the road contour into two-dimensional projected boundary coordinates; Verify the Euclidean distance continuity of the two-dimensional projected boundary coordinates, and connect the verified two-dimensional projected boundary coordinates into a continuous constrained polyline segment; The portion of the continuous constrained polyline segment that exceeds the road curvature radius limit is removed to obtain the constraint field boundary of the vehicle.

6. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 5, characterized in that, The process of retrieving the vehicle's offset information based on the positional mapping relationship between the slope sensing signal and the constraint field boundary includes: The slope angle in the slope sensing signal is analyzed and converted into a height axis offset in the vertical projection plane; By combining the height axis offset with the constraint field boundary, a dynamic mapping curve between the vehicle's corresponding slope and the boundary is constructed. Mark the spatial interference points between the initial position trajectory and the dynamic mapping curve, and calculate the depth vector and orientation angle of the spatial interference points penetrating the boundary of the constraint field; The penetration vectorization sequence of the initial position trajectory is determined based on the depth vector and the orientation angle; The chassis of the vehicle is subjected to multi-axis motion decoupling mapping through the permeation vectorization sequence to obtain the permeation components of the vehicle's height axis and horizontal axis. The height axis penetration component and the horizontal axis penetration component are integrated into the vehicle's offset information.

7. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 1, characterized in that, The step of correcting the initial position trajectory of the vehicle using the offset information to obtain the secondary positioning trajectory of the vehicle includes: The rationality of the offset information is verified, and the initial position trajectory is corrected and compensated based on the verified offset information to obtain the secondary positioning trajectory of the vehicle.

8. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 7, characterized in that, The monitoring of the driver's steering intention in the vehicle and the determination of the superposition relationship between the steering intention and the spatial vector corresponding to the secondary positioning trajectory include: The steering wheel angle sensor and gear position signal of the vehicle are dynamically sampled to obtain the driving operation timing sequence; Extract the driver's steering intention vector from the driving operation time sequence; The steering intention vector and the secondary positioning trajectory are superimposed to obtain the superposition relationship.

9. The method for calculating blind spot compensation in vehicle GPS positioning as described in claim 1, characterized in that, The step of converting the superposition relationship into an adaptive correction factor and correcting the secondary positioning trajectory according to the adaptive correction factor includes: The superposition relationship is converted into an adaptive correction factor, and the motion trajectory of the secondary positioning trajectory is reconstructed based on the adaptive correction factor.

10. A vehicle-mounted GPS positioning blind spot compensation calculation system, used to implement the vehicle-mounted GPS positioning blind spot compensation calculation method according to any one of claims 1-9, characterized in that, The system includes: Data acquisition module (101): used to acquire vehicle motion parameters and environmental data in real time, the environmental data including radar echo signal and slope sensing signal; Trajectory generation module (102): used to calculate the initial position trajectory of the vehicle using the motion parameters when the GPS signal of the vehicle is lost; Constraint module (103): used to obtain the environmental invariant elements of the vehicle's surrounding environment by analyzing the radar echo signal, and to construct the constraint field boundary of the vehicle by combining the environmental invariant elements. The trajectory correction module (104) is used to invert the vehicle's offset information based on the position mapping relationship between the slope sensing signal and the constraint field boundary, and correct the vehicle's initial position trajectory through the offset information to obtain the vehicle's secondary positioning trajectory. Intent overlay module (105): used to monitor the driver's steering intent in the vehicle and determine the overlay relationship between the steering intent and the spatial vector corresponding to the secondary positioning trajectory; Final generation module (106): used to convert the superposition relationship into an adaptive correction factor, and correct the secondary positioning trajectory according to the adaptive correction factor.

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