Door opening warning method and system for vehicle, and vehicle

Abstract

Disclosed are a door opening warning method and system for a vehicle, and a vehicle. The warning method comprises: on the basis of a plurality of UWB signal detectors provided on a vehicle body, detecting in real time a UWB echo signal in a rear-side region of the vehicle body, wherein the UWB echo signal is a UWB signal reflected by an object after a standard UWB signal transmitted by a UWB signal transmitter impinges upon the object; analyzing the UWB echo signal to obtain a detection signal, wherein the detection signal comprises the distance between a moving object corresponding to the current UWB echo signal and the vehicle body, and the movement direction of the moving object; and upon detecting that a door state of the vehicle body changes from a locked state to an opened state, if the detection signal indicates that there is a moving object approaching the vehicle body and the distance between the moving object and the vehicle body is less than a preset threshold value, outputting warning information on the basis of the detection signal.

Description

Vehicle door opening warning methods, systems and vehicles Technical Field

[0001] This application relates to the field of vehicle safety warning technology, and in particular to a vehicle door opening warning method, system and vehicle. Background Technology

[0002] With the widespread use of modern transportation, vehicle safety is receiving increasing attention. In daily life, when drivers or passengers open the doors to get out of a parked vehicle, they often neglect the situation to the side and rear, potentially leading to collisions with approaching moving objects (such as pedestrians, bicycles, motorcycles, etc.) and causing accidents. Therefore, vehicle door opening warning systems have emerged.

[0003] A vehicle door opening warning system primarily refers to a system that uses sensors to detect approaching moving objects on the sides and rear of a vehicle when it is stopped and the door is about to be opened, in order to prevent collisions. Currently, most door opening warning systems on the market use camera recognition or angular millimeter-wave radar technology to detect targets on the sides and rear of the vehicle.

[0004] However, existing camera and millimeter-wave radar solutions have certain limitations. These solutions generally suffer from high costs, high computing power consumption, and high energy consumption, resulting in a low market penetration rate for door opening warning functions. This situation means that only some high-end models are equipped with this safety feature, while the average consumer rarely experiences the safety and convenience brought by this technology.

[0005] Application content

[0006] The technical solution adopted in this application to solve its problem is:

[0007] The purpose of this application is to provide a vehicle door opening warning method, system, and vehicle that effectively reduces power consumption and cost.

[0008] To achieve the above objectives, this application provides a vehicle door opening warning method, which includes:

[0009] Based on several UWB signal detectors installed on the vehicle body, the UWB echo signal behind the side of the vehicle body is detected in real time. The UWB echo signal is the UWB signal reflected back from the object when the standard UWB signal emitted by the UWB signal transmitter hits the object.

[0010] The UWB echo signal is analyzed to obtain the detection signal;

[0011] When the vehicle door status changes from locked to open, if the detection signal indicates that a moving object is approaching the vehicle and the distance between the moving object and the vehicle is less than a preset threshold, then a warning message is output based on the detection signal.

[0012] Compared with existing technologies, the vehicle door opening warning method provided in this application uses a UWB signal detector to detect UWB echo signals from the side and rear of the vehicle body and generates a detection signal accordingly. When the user opens the door, if a danger is detected based on the detection signal, a warning signal is issued. Therefore, this method can accurately determine the distance and direction of movement of a moving object relative to the vehicle body, significantly reducing accidents caused by improper door opening and improving driving safety. Furthermore, UWB radar signals have higher measurement accuracy and lower hardware requirements, eliminating the need for complex image processing algorithms and thus reducing processor computing power demands, effectively lowering power consumption. Moreover, it is more cost-effective than traditional camera-plus-millimeter-wave radar warning systems. Attached Figure Description

[0013] Figure 1 is a flowchart of the vehicle door opening warning method in this application;

[0014] Figure 2 is a reference diagram showing the working status of the vehicle door opening warning method in this application;

[0015] Figure 3 is a flowchart of the steps for analyzing UWB signals in this application. Embodiments of the present invention

[0016] To explain in detail the technical content, structural features, objectives and effects of this application, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0017] In one embodiment of this application, a vehicle door opening warning method is disclosed for warning of dangerous situations when a vehicle door is opened. For example, if a pedestrian or bicycle is approaching from the side or rear when the vehicle door is opened, the warning method can issue a warning signal when the door is unlocked to remind the user to check the external situation of the vehicle before deciding whether to open the door, thereby improving the vehicle's safety performance. As shown in Figure 1, the warning method in this embodiment includes the following steps:

[0018] S1: Based on several UWB (Ultra-Wideband) signal detectors installed on the vehicle body, the UWB echo signal on the side and rear of the vehicle body is detected in real time. The UWB echo signal is the UWB signal reflected back from the object when the standard UWB signal emitted by the UWB signal transmitter hits the object.

[0019] S2: Analyze the UWB echo signal to obtain the detection signal;

[0020] The detection signal includes any one or more of the following:

[0021] The distance between the moving object and the vehicle body corresponding to the current UWB echo signal;

[0022] The velocity of the moving object corresponding to the current UWB echo signal;

[0023] The azimuth angle of the moving object relative to the vehicle body corresponding to the current UWB echo signal;

[0024] The direction of motion of the moving object corresponding to the current UWB echo signal.

[0025] The "direction of motion of the moving object" mentioned above can be calculated and obtained by the distance, speed and azimuth information between the moving object and the vehicle body.

[0026] For example, by using distance and azimuth information, the first spatial position of a moving object at the first moment and the second spatial position at the second moment can be determined. By combining the first and second spatial positions of the moving object, the direction of movement of the moving object can be determined.

[0027] S3: When the vehicle door status changes from locked to open, if the detection signal indicates that a moving object is approaching the vehicle and the distance between the moving object and the vehicle is less than a preset threshold, then a warning message is output based on the detection signal.

[0028] It should be noted that the UWB signal detector and the UWB signal transmitter constitute a UWB radar system. Unlike traditional UWB ranging systems, the UWB radar system does not require UWB tags to be placed on the target object. Therefore, it can detect any object around the vehicle.

[0029] Specifically, a car, as shown in Figure 2, has several UWB signal detectors installed on its body to monitor the environmental conditions to the side and rear of the vehicle in real time. The execution process of the aforementioned early warning method is as follows:

[0030] 1. UWB signal detection:

[0031] The UWB signal detector installed on the side and rear of the vehicle body continuously receives this UWB echo signal.

[0032] For example, when a bicycle approaches a parked car from behind, the UWB signal detector will receive the UWB echo signal reflected back from the bicycle and the person on it.

[0033] 2. Signal Analysis:

[0034] The received UWB echo signal is analyzed to obtain the detection signal. The detection signal includes, but is not limited to, the distance and direction of motion between the current reflecting object (bicycle) and the vehicle body.

[0035] Suppose that a bicycle is detected to be 5 meters away from the vehicle and is approaching it.

[0036] 3. Door status detection:

[0037] Real-time monitoring of door status. When a door changes from locked to open, further detection is triggered.

[0038] For example, when the driver is preparing to open the car door to get out, the door status changes to open.

[0039] 4. Early warning judgment and output:

[0040] Check the detection signal. If a moving object (bicycle) is detected approaching the vehicle and the distance is less than a preset safety threshold (e.g., 2 meters), a warning message will be issued.

[0041] In this example, the bicycle is 5 meters away from the vehicle and approaching. Assuming the set warning threshold is 2 meters, the current detection signal shows that the bicycle is approaching but has not reached the warning threshold, so no warning is issued at this time.

[0042] Continue monitoring; if the bicycle continues to approach and enters a 2-meter range, the system will immediately issue a warning message, such as an in-vehicle alarm or a message displayed on the rearview mirror display: "Caution: Approaching object."

[0043] Therefore, the above-mentioned vehicle door opening warning method can accurately determine the distance and direction of movement of the moving object to the vehicle body, thereby significantly reducing the occurrence of accidents caused by improper door opening and improving driving safety. Moreover, UWB radar signals have higher measurement accuracy and lower hardware requirements, and do not require complex image processing algorithms, thus reducing the processor's computing power requirements and power consumption. In addition, it is cheaper than the traditional camera plus millimeter-wave radar warning system.

[0044] Since typical vehicle door opening accidents usually involve collisions with pedestrians, bicycles, or electric bicycles, collisions with doors are rare for other vehicles where pedestrians are not visible, as they are easily visible in the rearview mirror. Therefore, in this embodiment, the moving object corresponding to the UWB echo signal is a living being. This not only provides early warning of pedestrians, bicycles, and electric bicycles approaching the vehicle from behind, but also further reduces the computational load on the UWB echo signal, requiring only the detection of living beings.

[0045] Furthermore, by analyzing and identifying minute motion changes in UWB echo signals, signal components related to physiological activities of living organisms (such as respiratory movements) are detected to generate detection signals.

[0046] Specifically, the aforementioned "detection of minute motion changes in UWB echo signals and signal components related to physiological activities (e.g., respiratory movements) of living organisms" includes, but is not limited to:

[0047] Information such as distance, speed, and azimuth of moving objects, as well as the calculated acceleration and its variation pattern (such as the gait cycle frequency of pedestrians and the uniform motion characteristics of vehicles).

[0048] Moving objects have different motion characteristics, and their speed / acceleration and their change patterns differ. For example, pedestrians have a gait cycle → their speed will fluctuate periodically (e.g., one step faster, one step slower); cars usually move at a constant speed or accelerate uniformly → their speed is stable and there are no periodic fluctuations.

[0049] By analyzing the above signal components, a corresponding target type detection signal can be generated, enabling the detection of moving objects that are living organisms.

[0050] Furthermore, multi-dimensional feature data of known types of moving objects (various life forms) can be collected, and the multi-dimensional feature data can be formed into a sample database for model training; the multi-dimensional feature data includes a high-dimensional feature vector composed of the above signal components and the corresponding type label of the moving object.

[0051] In one alternative approach, the type labels can be divided into three categories (pedestrians, animals, or other living beings), with each of the three different target types corresponding to a different high-dimensional feature vector.

[0052] The sample data from the sample database is input into a deep learning model (e.g., a 2D-CNN model), which learns the mapping pattern between the high-dimensional feature vectors and the target type, thereby achieving type classification output for different moving objects.

[0053] On the other hand, to improve the accuracy of UWB echo signal processing and avoid the influence of environmental noise data, the UWB echo signal processing method is as follows:

[0054] 1. Determine the target direction of the moving object causing the change in the UWB signal by observing the changes in several UWB echo signals;

[0055] 2. Generate core detection results based on UWB echo signals aligned with the target direction;

[0056] 3. Generate auxiliary detection results based on UWB echo signals that deviate from the target direction;

[0057] 4. By analyzing the auxiliary detection results, distinguish and extract the environmental noise data to obtain an estimate of the background noise;

[0058] 5. Filter the core detection results based on background noise to obtain the detection signal.

[0059] In this embodiment, by estimating the background noise level and filtering the core detection data, noise interference can be effectively eliminated, thereby improving the accuracy of the detection signal. This not only reduces the false alarm rate but also improves the reliability of the warning system, providing drivers with more timely and accurate collision warnings, enhancing vehicle safety performance, and reducing the risk of traffic accidents.

[0060] On the other hand, UWB signal detectors are installed at least at the rear of the vehicle, the middle of the vehicle, and both sides of the front of the vehicle; and preferably, UWB signal detectors are installed on both sides of the rear of the vehicle to detect the situation behind the left and right sides of the vehicle respectively.

[0061] On the other hand, UWB signal transmitters are installed in signal base stations located on or near the vehicle body.

[0062] In a preferred embodiment, the analysis of S2 and UWB echo signals includes at least the following steps:

[0063] S201. Distance and velocity information acquisition: The received UWB echo signal is processed in a structured manner to extract and output the distance and velocity information of the moving object.

[0064] Specifically, by structuring the UWB echo signal, basic quantization data is provided for subsequent steps, which also facilitates subsequent calculations / data processing.

[0065] S202, Azimuth Information Acquisition: Based on the signal characteristic differences of the received UWB echo signals, extract and output the azimuth information of the moving object.

[0066] Specifically, azimuth information is extracted based on the characteristic differences of the same UWB echo signal (such as the phase difference received by multiple receiving modules in the same UWB signal detector), overcoming the limitation that a single receiving module cannot sense direction. By analyzing the signal characteristic differences, the azimuth of moving objects can be stably output in complex scenarios, improving the accuracy of azimuth measurement, supplementing the spatial positioning of moving objects with directional dimension information, and enhancing the comprehensive perception capability of the target's relative position.

[0067] S203. Target Information Fusion: Multi-source data fusion processing is performed on distance, velocity, and azimuth information to obtain the spatial position and motion state of the moving object. The motion state includes information such as the object's velocity, acceleration, and direction of motion.

[0068] Specifically, multi-source fusion of distance, velocity, and azimuth information can eliminate the limitations and errors of single-source data. Multi-source data fusion processing can improve data robustness, integrating scattered parameters into a unified spatial position (such as three-dimensional coordinates) and motion state (such as trajectory trend), more comprehensively reflecting the target dynamics, and providing complete and reliable feature input for subsequent higher-order type recognition functions.

[0069] In a preferred embodiment, step S201, the distance and speed information acquisition step, includes:

[0070] 1. When performing structured processing on the received UWB echo signal, a multi-dimensional signal model is first constructed. The multi-dimensional signal model includes signal representations that are associated with distance and velocity information (but not limited to associated distance and velocity information, it can also be associated with azimuth angle, phase difference and other information). Then, distance and velocity information are extracted based on the signal representation.

[0071] By constructing a multi-dimensional signal model that incorporates distance and velocity correlation representations, both are collaboratively extracted from the signal. Furthermore, the multi-dimensional signal model can map the intrinsic correlation between signal features and target object motion parameters, providing more relevant foundational data for subsequent fusion and accelerating computational efficiency.

[0072] 2. Before extracting distance and speed information, interference suppression processing is performed on the received UWB echo signal, and the effective signal characteristics directly associated with the moving object are enhanced.

[0073] By suppressing interference in advance on the UWB echo signal, irrelevant signals such as environmental noise and multipath reflections can be filtered out; the direct correlation characteristics of the target can be enhanced, highlighting the effective components of the direct path signal. The above steps can reduce interference from invalid signals, making the subsequently extracted distance and velocity information purer, and directly improving the accuracy and stability of parameter calculation.

[0074] Furthermore, the "construction of a multi-dimensional signal model" in the above preferred scheme can be achieved through the following steps:

[0075] Construct a CIR matrix containing fast time and slow time dimensions; where the fast time dimension corresponds to the distance information of the moving object, and the slow time dimension corresponds to the speed information of the moving object.

[0076] In the above steps, the CIR matrix captures the energy distribution of the signal over distance (such as the position of direct / multipath peaks) in the fast time dimension and records the trend of signal change over time (such as amplitude fluctuations caused by Doppler shift) in the slow time dimension. The distance and velocity information are associated and represented in the matrix, so that the signal features of the two can be extracted together. This avoids problems such as parameter asynchrony or data fragmentation caused by separate processing or staged processing, and improves the correlation and extraction efficiency of distance and velocity parameters.

[0077] Furthermore, the structured storage method of constructing a multi-dimensional signal model ensures that distance and velocity information are no longer isolated but are uniformly stored in the constructed matrix. Subsequent steps can directly extract the required parameters based on the row / column dimensions of the CIR matrix, avoiding data retrieval chaos. At the same time, the synchronous processing method ensures that the calculation of distance (based on the fast time dimension) and velocity (based on the slow time dimension) is based on the same set of signal data and the same time reference, eliminating parameter errors caused by time differences from the source and improving the consistency of distance and velocity.

[0078] More specifically, each row of the CIR matrix corresponds to all sampling points (TAPs) of a single pulse, and the value of each row corresponds to the complex signal value (including amplitude and phase) of this pulse. Among the complex signal values ​​in each row of the CIR matrix, the direct path signal reflected by the moving object (the signal that best represents the true distance) will produce the largest amplitude fluctuation at a certain TAP. By finding this largest amplitude TAP, the distance of the moving object can be calculated through its corresponding time delay, thus converting the fast time dimension (row) into distance information.

[0079] Meanwhile, each column of the CIR matrix corresponds to the set of TAP signal values ​​of all pulses at the same sampling time. Based on the Doppler effect, the movement of a moving object will cause the signal phase to change. By analyzing the phase change law in the column direction of the matrix, the Doppler frequency shift can be calculated, and then the radial velocity of the moving object can be calculated, thus converting the slow time dimension (column) into velocity information.

[0080] Furthermore, the "interference suppression processing of the received UWB echo signal" in the above preferred scheme can be achieved through the following steps:

[0081] Fourier transform (FFT) is performed on the time-domain signal of the CIR matrix to obtain the frequency-domain signal of the range-Doppler spectrum (RD spectrum); then frequency-domain equalization is used to compensate for the selective fading of the signal caused by multipath interference.

[0082] Furthermore, the "interference suppression processing of the received UWB echo signal" in the above preferred scheme can be achieved through the following steps:

[0083] Then, the frequency domain signal of the distance Doppler spectrum is converted back to the time domain signal of the CIR matrix by inverse Fourier transform (inverse FFT) to enhance the significance of the pulse peak of the direct path of the UWB echo signal.

[0084] The essence of multipath interference is that signals of different frequencies attenuate to different degrees after passing through the channel. This difference is more intuitive in the frequency domain (e.g., some frequency bands are severely attenuated, while others are relatively normal). Multipath interference is difficult to separate in the time domain signal, but the frequency-selective fading it causes in the frequency domain signal follows a pattern.

[0085] Therefore, the above steps convert the time-domain signal of the CIR matrix into the frequency-domain signal of the range-Doppler spectrum pulse by pulse through FFT. This facilitates targeted compensation for selective fading caused by multipath interference in the frequency domain through frequency domain equalization, and accurately suppresses the attenuation imbalance of each frequency component.

[0086] Optionally, converting the frequency domain signal back to the time domain signal via inverse FFT can significantly enhance the amplitude and distinctiveness of the pulse peaks of the direct path, reduce the interference superposition of multipath reflection signals, and make the effective signal proportion higher when extracting distance and velocity information from the CIR matrix, thereby reducing the parameter extraction error caused by interference.

[0087] More specifically, the aforementioned "frequency domain equalization" can be achieved by designing a minimum mean square error (MMSE) equalizer, as follows:

[0088] By analyzing the attenuation law of the frequency domain signal, the compensation coefficient of the equalizer is calculated. For frequencies with severe attenuation, the compensation coefficient is greater than 1 (gain); for frequencies with normal attenuation, the compensation coefficient is close to 1 (no additional adjustment). Each frequency in the CFR matrix is ​​multiplied by the corresponding compensation coefficient to obtain the equalized CFR matrix. At this time, the signal strength of each frequency has become consistent, the frequency imbalance caused by multipath interference is corrected, and the signal of all frequencies is restored to the ideal state when there is no multipath interference.

[0089] Furthermore, the "interference suppression processing of the received UWB echo signal" in the above preferred scheme can be achieved through the following steps:

[0090] The constant false alarm rate (CFAR) detection algorithm is used to process the time domain signal of the CIR matrix or the frequency domain signal of the range Doppler spectrum to identify and lock the pulse peak position of the direct path of the UWB echo signal, and the pulse peak position of the direct path is used as the reference for extracting distance and velocity values.

[0091] Among them, the constant false alarm rate (CFAR) detection algorithm is a dynamic threshold detection algorithm. It dynamically adjusts the threshold (i.e., non-fixed threshold) based on the noise intensity around the detection point to determine whether the amplitude of the detection point exceeds the dynamic threshold. If it exceeds the threshold, it is determined to be a direct path peak; if it does not exceed the threshold, it is determined to be noise or multipath interference and is directly filtered out. Ultimately, it avoids false detection or missed detection caused by noise and locks the true direct path peak (i.e., the pulse peak of the direct path) in the signal.

[0092] Therefore, using a constant false alarm rate (CFAR) detection algorithm to process the time-domain signal of the CIR matrix or the frequency-domain signal of the range-Doppler spectrum can stably control the CFAR rate when noise fluctuates, avoid mistaking noise or weak multipath peaks as direct path peaks, accurately identify and lock the pulse peak position of the direct path, provide a reliable benchmark for the extraction of distance (fast time dimension) and velocity (slow time dimension) values, and significantly reduce the error of subsequent parameter calculations.

[0093] In a preferred embodiment, step S201, the distance and speed information acquisition step, further includes:

[0094] The extracted distance and velocity values ​​are optimized: based on the pulse peak position of the direct path, the parameter accuracy of distance and velocity values ​​is improved by resolution enhancement; multi-source observation data (i.e. distance and velocity information) are fused to reduce noise interference and output optimized distance and velocity values.

[0095] Specifically, the above steps use the pulse peak position of the direct path as a reliable benchmark, refine the quantification accuracy of distance and velocity parameters through resolution enhancement methods (such as reducing the minimum scale of distance measurement and increasing the velocity sampling interval), and fuse multi-source observation data (such as synchronous signals from different receiving modules and sampled values ​​of continuous time slices) to offset noise fluctuations of single data and reduce the impact of random interference. The final output of optimized distance and velocity values ​​is more accurate and more stable, providing a better dynamic parameter foundation for subsequent azimuth information fusion and target type identification.

[0096] Furthermore, the above-mentioned preferred solution's "improving the parameter accuracy of distance and velocity values ​​through resolution enhancement" can be achieved through the following steps:

[0097] 1. Delineate a local window centered on the pulse peak position of the direct path, and perform smooth interpolation algorithms on the real and imaginary components of the signal within the local window respectively. Generate subdivided sampling points by fitting the signal change trend.

[0098] In the above steps, defining a local window (e.g., a 6×6 grid) based on the pulse peak position along the direct path allows for focusing on the effective signal region, eliminating irrelevant data interference outside the local window, and reducing subsequent computational load. Performing smooth interpolation on the real and imaginary parts of the signal separately preserves the signal's phase and amplitude characteristics, avoiding information loss caused by single processing. The generated subdivided sampling points fill the gaps in the original sampling interval, significantly improving signal resolution and providing high-density, high-fidelity data support for subsequent precise pulse peak location optimization.

[0099] More specifically, the aforementioned "smooth interpolation" can employ the Spline interpolation method, performing Spline interpolation on the real and imaginary parts within a local window respectively. That is, drawing a continuous and smooth curve between the discrete points sampled by the hardware, and then inserting more virtual sampling points into the curve. After interpolation, the sampling point density is greatly improved, thereby achieving sub-pixel level resolution improvement through algorithmic point supplementation.

[0100] 2. Based on the constraints of the subdivided sampling points, the pulse peak optimization position of the direct path is repositioned, and the pulse peak optimization position of the direct path is used as the benchmark for extracting the distance and speed optimization values.

[0101] During the interpolation process, a large number of virtual sampling points will appear in the local window. However, the actual peak value of the direct path cannot be too far from the initial detection point found in the previous step. Therefore, a constraint range is set. Within the constraint range of the subdivided sampling points, the true peak value can be found while avoiding interference from pseudo-peaks outside the constraint range.

[0102] In the above steps, relying on the high-density data of subdivided sampling points, the true extreme points of the pulse peaks along the direct path can be captured more clearly. This effectively corrects the positioning deviation of the pulse peak positions along the direct path caused by large sampling intervals. Using the optimized position of the pulse peaks along the direct path as the benchmark for extracting optimized distance and velocity values ​​can directly eliminate parameter errors caused by inaccurate peak positioning, making the distance measurement scale more refined and the velocity calculation interval more precise, further improving the quantification accuracy and reliability of the two parameters. More specifically, the optimized distance and velocity values ​​can be obtained through the following preferred scheme.

[0103] In a preferred embodiment, step S201, the distance and speed information acquisition step, further includes:

[0104] 1. Distance calculation: using the formula Calculate the distance information of the moving object; where d is the distance between the moving object and the vehicle body, c is the speed of light, and Δt is the round-trip time of the UWB signal (theoretically twice the return time of the UWB echo signal).

[0105] In the above steps, relying on the constant characteristic of the speed of light c and the measured value of the round-trip time Δt of the UWB signal, a linear correspondence between time and distance is established. The above calculation method avoids complex conversion errors. Combined with the high time resolution advantage of the UWB echo signal, it can accurately convert the subtle changes in the signal round-trip time Δt into distance values, ensuring centimeter-level accuracy in distance measurement and providing a direct and reliable quantitative basis for the spatial positioning of moving objects.

[0106] 2. Speed ​​calculation: using the formula Calculate the velocity information of the moving object; where v is the radial velocity of the moving object, Δf is the frequency offset of the UWB echo signal, and λ is the wavelength of the UWB echo signal.

[0107] In the above steps, by utilizing the physical relationship between the frequency offset Δf of the UWB echo signal and the radial velocity v, combined with the fixed parameter of the signal wavelength λ, the frequency domain signal characteristics can be directly converted into velocity values. The above method does not require complex motion model derivation, can respond in real time to the frequency changes caused by the movement of moving objects, improve the dynamic tracking capability of velocity measurement, and accurately reflect the rate at which moving objects approach or move away.

[0108] More specifically, the frequency offset Δf is calculated using the formula Δf=f_d / f_c, where f_d is the Doppler frequency shift obtained by FFT transformation of the slow time dimension of the CIR matrix, and f_c is the carrier frequency.

[0109] It should be noted that the distance and speed information calculated and output by the above steps can correspond to both the unoptimized distance and speed values ​​and the optimized distance and speed values.

[0110] Furthermore, the "fusion of multi-source observation data to reduce noise interference" in the above-mentioned preferred scheme can be achieved through the following steps:

[0111] The observations from multiple receiving modules are fused using a weighted least squares method, and the result is obtained through the formula... The final distance information is calculated. The UWB signal detector includes multiple receiving modules arranged at preset intervals. w1 and w2 are inversely proportional to the signal-to-noise ratio of different receiving modules of the same UWB signal detector. d1 and d2 are the distance observation values ​​(i.e., distance calculation results) of different receiving modules of the same UWB signal detector, and d_final is the final distance information.

[0112] Specifically, the observations (i.e., measurement data) of a single receiving module are affected by noise, leading to errors in distance calculation. To address this, the above steps rely on multiple receiving modules (e.g., antenna arrays) arranged at predetermined intervals in a UWB signal detector. By leveraging the complementary nature of the spatially distributed observations of these modules, the local interference and field-of-view limitations of a single module can be avoided. By making the weights w1 and w2 inversely proportional to the signal-to-noise ratio of each receiving module, the higher-precision (smaller variance) observations d1 and d2 are given a larger proportion in the fusion process, reducing the interference from lower-precision data. Then, the calculation is performed using the formula... The weighted average effectively cancels out the random noise of each receiving module. The final output d_final not only significantly improves the distance measurement accuracy, but also reduces the error caused by the fluctuation of a single module, providing a more stable quantitative basis for subsequent target positioning.

[0113] It should be noted that the formula described in the above steps corresponds only to the fusion calculation method of the distance observation values ​​(d1 and d2) of two receiving modules of the same UWB signal detector. When the same UWB signal detector has three or more receiving modules, three or more distance observation values ​​will be generated accordingly. At this time, the third or more parameters (i.e., d3, w3; d4, w4; d5, w5...) are introduced, and the final distance information is obtained after fusion.

[0114] In addition, the above steps may also include a velocity information fusion processing step, which calculates the final velocity information by fusing velocity observations from multiple sources.

[0115] In a preferred embodiment, step S202, obtaining azimuth information, includes:

[0116] Based on the signal characteristic differences between the same UWB echo signals received by multiple receiving modules, the azimuth angle value of the moving object is output through stability optimization and accuracy improvement processing. The UWB signal detector includes multiple receiving modules arranged at preset intervals; each receiving module will produce different signal characteristics when receiving the same UWB echo signal.

[0117] The above steps utilize multiple receiver modules (e.g., antenna arrays) arranged at preset intervals. By leveraging the characteristic differences (such as phase difference and amplitude ratio) when receiving the same UWB echo signal, they provide a multi-dimensional reference for azimuth angle calculation, overcoming the limitation that a single receiver module cannot perceive direction. Simultaneously, through stability optimization to filter out transient interference and precision enhancement processing to refine angle quantization, the output azimuth angle value is not only more accurate but also more resistant to signal fluctuations. This supplements reliable directional dimension data for target spatial positioning, enhances the all-round perception capability of the target's relative position, and lays a precise angle information foundation for subsequent information fusion.

[0118] Furthermore, a phase difference is formed between the same UWB echo signals received by each receiving module; the signal characteristic differences mentioned in the previous step are precisely this phase difference. Based on this, the "calculation of the initial azimuth angle of the moving object" in the above preferred scheme can be achieved through the following steps:

[0119] The Capon beamforming algorithm is used to search for the peak value of the spatial spectral function, and the phase difference corresponding to the peak value is used as the phase difference value. The spatial spectral function is constructed based on the steering vector and the signal covariance matrix. The steering vector is the phase distribution template of the UWB echo signal in different receiving modules, and the signal covariance matrix is ​​calculated from the spacing between multiple receiving modules and the wavelength of the UWB echo signal.

[0120] In the steps described above, when constructing the spatial spectral function, the steering vector serves as a phase distribution template for the UWB echo signal incident on different receiving modules at a certain angle when it arrives at the UWB signal detector. Combined with the covariance matrix calculated from the distance between the receiving modules and the signal wavelength, the spatial spectral function accurately maps the signal energy distribution at different angles, strengthening the physical correlation between azimuth angle values ​​and signal characteristics. Simultaneously, the Capon beamforming algorithm is used to search for spectral peaks, effectively suppressing sidelobe interference and accurately locking the angle corresponding to the direction of strongest energy as the azimuth angle value, laying a high-quality parameter foundation for subsequent processing.

[0121] More specifically, when multiple receiving modules receive the UWB signal from the same target, the signal waveforms are similar, but there is a phase shift. The phase shift is the phase difference Δ between the receiving modules. The above steps are used to record the phase difference Δ of all paired receiving modules. .

[0122] Furthermore, the "stability optimization and accuracy improvement processing" of the above preferred scheme can be achieved through the following steps:

[0123] A diagonal loading process is applied to the signal covariance matrix R, adding a diagonal perturbation term of a set strength to the signal covariance matrix R to improve the numerical stability of the initial azimuth angle value.

[0124] The above steps add a diagonal perturbation term to the signal covariance matrix R, which can effectively improve the singularity problem that the matrix may cause due to insufficient samples or excessive noise, enhance the numerical stability of the matrix, avoid the phenomena such as spectral peak splitting and pseudo-peaks that occur in the Capon beamforming algorithm when the signal is weak or the interference is complex, keep the initial azimuth angle value robust in noise fluctuations, reduce extreme value jumps, and provide a more reliable benchmark for subsequent optimization.

[0125] More specifically, the signal covariance matrix R needs to be calculated using sampled data from multiple receiver modules. However, in dynamic scenarios such as vehicle monitoring (e.g., rapid target movement and short sampling time), the amount of sampled data may be insufficient, causing the signal covariance matrix R to become a singular matrix (mathematically non-invertible). Capon beamforming requires calculating the inverse of the signal covariance matrix, and a singular matrix would cause the algorithm to crash, making it impossible to calculate the spatial spectral function P(θ). Therefore, the essence of the previous step is to ensure that the signal covariance matrix R is always invertible. Diagonal loading is equivalent to adding a stabilizing term, ensuring that the Capon beamforming algorithm can operate normally.

[0126] In an alternative approach, the initial azimuth angle can also be calculated directly using a spatial spectrum function, as follows:

[0127] At this point, the formula for the spatial spectral function is: Where a(θ) is the steering vector and R is the signal covariance matrix. It is the inverse of the signal covariance matrix, and θ is the azimuth angle to be calculated. The spatial spectrum function P(θ) is used to measure the degree of matching between the actual received signal and the steering vector a(θ). The higher the value of P(θ), the greater the probability that the signal is incident from the θ direction. Therefore, the θ value corresponding to the peak of the spatial spectrum is the initial value of the azimuth angle of the target object. The signal covariance matrix R is used to describe the correlation between the signals received by multiple receiving modules. aH(θ) is the conjugate transpose of a(θ).

[0128] Optionally, after obtaining the initial azimuth value through the spatial spectrum function, the historical angle and the current angle information can be combined to obtain more reliable and accurate angle information, i.e., the optimized azimuth value, through the Kalman filter algorithm.

[0129] The above steps use the current azimuth angle as the initial value to avoid redundant calculations and irrelevant interference in the global search. By using the Kalman filter algorithm for fine iteration within this local range, the mapping relationship between signal features and angles can be fitted using a probabilistic model to accurately locate the angle value with the smallest error. This significantly reduces the quantization error of the initial value, making the azimuth angle value closer to the true angle and improving the precision of angle measurement.

[0130] The initial azimuth angle mentioned above is a relative concept. Generally speaking, the initial azimuth angle value is obtained from the CIR matrix. In the iterative calculation of the spatial spectrum function, the value before each iteration can be called the initial azimuth angle value.

[0131] In a preferred embodiment, step S202, the azimuth information acquisition step, further includes:

[0132] Formula based on phase difference physical model The azimuth value is obtained by calculation. Here, θ is the azimuth value, and Δ... λ is the phase difference, d is the spacing between the receiving modules, and λ is the wavelength of the UWB echo signal.

[0133] The above steps calculate the azimuth angle value using the phase difference physical model formula, and then calculate the phase difference value Δ. Physical parameters such as the distance between receiving modules (d) and the signal wavelength (λ) are directly related to the azimuth angle (θ) of the moving object.

[0134] In a preferred embodiment, step S203, the target information fusion step, includes:

[0135] 1. Time synchronization: Synchronize all observations to the same time reference; the observations include distance and velocity information and azimuth information observed by different UWB signal detectors.

[0136] The essence of the above time synchronization steps is to eliminate the target position deviation caused by time difference, and ensure that all subsequent data comparisons and fusions are based on the target state at the same moment, avoiding misjudgments caused by time asynchrony (for example, mistakenly thinking they are two different targets).

[0137] The above steps synchronize the distance, velocity, and azimuth observations from different UWB signal detectors to the same time reference, eliminating target position deviations caused by time differences between data, ensuring that all subsequent data comparisons and fusions are based on the target state at the same moment, and avoiding misjudgments caused by time asynchrony (such as mistakenly believing them to be two different moving objects).

[0138] Specifically, the same time reference can be the vehicle's system clock (such as the vehicle ECU clock). The sampling time of all UWB signal detectors is converted to this time reference. If the sampling time of a certain UWB signal detector does not coincide with the target time of the unified reference (data sampled asynchronously), linear interpolation is used to complete the data. Finally, the observation data of all modules are unified to the same time.

[0139] 2. Coordinate System 1: Transform the observations in the local coordinate system with the UWB signal detector as the origin to the global coordinate system with the rear axle center of the vehicle as the origin.

[0140] The above steps transform the observations in the local coordinate system (with the UWB signal detector as the origin) to the global coordinate system (with the rear axle center of the vehicle as the origin), unify the spatial reference, eliminate coordinate deviations caused by differences in the positions of different UWB signal detectors, enable multi-source data to be fused within the same spatial framework, and improve the overall spatial positioning of the target.

[0141] Specifically, when defining the global coordinate system, a Cartesian coordinate system (x, y) is established with the center of the vehicle's rear axle as the origin. For the observation value (d, θ) of each module, the coordinates of the UWB signal detector itself in the global coordinate system are first calculated, then the local Cartesian coordinates of the moving object relative to the UWB signal detector are calculated, and then the local coordinates are converted into global coordinates. Finally, all the observation values ​​of the UWB signal detectors are converted into (x, y) coordinates in the global coordinate system, and the positions can be directly compared later.

[0142] 3. Uncertainty initialization: Assign a covariance matrix P to the observations; where the covariance matrix P is set by the reliability of each observation.

[0143] Each UWB signal detector's observation data has errors (e.g., the front module has smaller distance errors due to less obstruction, while the rear module has larger distance errors due to more multipath interference). If all observation data are treated equally, data with large errors will lower the fusion accuracy. Therefore, it is necessary to quantify the reliability of each data point using initial uncertainty (higher reliability means lower uncertainty) to provide a basis for subsequent weighted fusion and avoid large-error data affecting the final result. The above steps assign a covariance matrix P according to the reliability of the observations, quantifying the error range of each observation data point. This allows the fusion algorithm to process data based on reliability differences, reducing the interference weight of low-quality observations, providing a quantitative basis for error fusion of multi-source data, and improving the robustness of the results.

[0144] Specifically, the defined covariance matrix P is the initial uncertainty matrix of each observation. For each observation (x, y) of the UWB signal detector, its uncertainty is represented by an N×N covariance matrix P. Based on the hardware accuracy of the UWB signal detector and the actual scene calibration variance value, a corresponding covariance matrix P is assigned to each global coordinate (x, y). During subsequent fusion, the data weights will be adjusted according to the size of the covariance matrix P (the smaller P is, the greater the weight).

[0145] In a preferred embodiment, step S203, the target information fusion step, further includes:

[0146] 1. Redundancy detection: Set a spatial distance threshold; for any two observations from different UWB signal detectors, calculate the Euclidean distance between them in the global coordinate system; determine whether there is redundant correlation between the observations by comparing the magnitude of the Euclidean distance with the spatial distance threshold.

[0147] Specifically, if observations from two UWB signal detectors correspond to the same moving object, their spatial distance (i.e., Euclidean distance) in the global coordinate system will be very small. If they correspond to different moving objects or false targets, the distance will be very large. Therefore, the above step uses an Euclidean distance threshold to determine whether an observation is redundant (i.e., multiple observations of the same moving object). This step is used to initially screen potential observation groups of the same target, preparing for subsequent clustering and grouping, while excluding obvious false targets (e.g., data observed only by a single UWB signal detector and whose observation distance from other UWB signal detectors is far beyond the threshold).

[0148] The above steps, by setting a spatial distance threshold, calculate the Euclidean distance between observations from different UWB signal detectors in the global coordinate system and compare it with the threshold, can quickly identify redundant correlations between observations from multiple UWB signal detectors. This avoids repeated data interference caused by moving objects in overlapping detection areas, thus preventing subsequent fusion. It also helps to select candidate data with redundancy potential for clustering and grouping, ensuring the simplicity and relevance of data before fusion from the data source.

[0149] More specifically, for any two different UWB signal detectors, the Euclidean distance between the observed values ​​(x1, y1) and (x2, y2) is calculated as follows: First, calculate and square the difference between the coordinates of the two points along the x-axis; then calculate and square the difference between the coordinates of the two points along the y-axis; add these two squared results together; finally, take the square root of this sum. The formula is: .

[0150] 2. Clustering and grouping: A distance-based clustering algorithm is used to group observations that are close in location in the global coordinate system into the same redundant cluster; and a redundant cluster is determined to be a valid redundant cluster only when it contains at least two observations from different UWB signal detectors.

[0151] The above steps employ a distance-based clustering algorithm, which aggregates redundant observations from multiple UWB signal detectors into clusters based on spatial proximity in the global coordinate system. This means multiple observations of the same moving object are grouped into a single redundant cluster, counting only one target and avoiding duplicate counting. Furthermore, the redundant cluster must contain observations from at least two different detectors, ensuring that the data within the cluster represents valid multi-source observations of the real target (rather than noise or false points from a single detector). This provides a reliable multi-source redundant set for subsequent fusion, enhancing information density through the complementarity of observations from multiple detectors while filtering out meaningless single-point clusters (if a cluster contains only one observation from a UWB signal detector, it may be a false target and must be directly removed), thus improving the effectiveness of the fusion input data.

[0152] More specifically, the aforementioned "distance-based clustering algorithm" can automatically cluster observations that are close together into a redundant cluster without knowing the number of moving objects in advance. It performs clustering, using coordinates in the global coordinate system as clustering features, and groups all observations according to the rule that the Euclidean distance is less than a set threshold. Each redundant cluster represents a potential target. By setting the validity conditions of the redundant clusters, multiple effective redundant clusters are finally obtained, and each cluster corresponds to all redundant observations of a real moving object.

[0153] In a preferred embodiment, step S203, the target information fusion step, further includes:

[0154] Fusion Output: Based on the target motion model, the state prediction information of the moving object at the next moment is obtained; all observations in the effective redundant cluster and their corresponding observation noise covariance matrix are obtained; using the observations and their corresponding observation noise covariance matrix, the state prediction information is updated by the Kalman filter algorithm to obtain the fused state optimization information; the optimized state optimization information is output; the state prediction information and the state optimization information include the position and velocity information of the moving object.

[0155] Specifically, the target motion model in the above steps preferably adopts a constant velocity model (assuming the target object's velocity remains constant in a short time) to describe the change law of the moving object's motion state at the prediction time; the observation noise covariance matrix (R1, R2, ...) is the covariance matrix corresponding to each observation value, used to quantify the noise level of each observation value (Z1, Z2, ...) within the effective redundant cluster; the update process of the Kalman filter algorithm specifically includes: calculating the state deviation based on the state prediction information and the observation values, correcting the deviation by combining the observation noise covariance matrix to optimize the target state, and thereby obtaining the state optimization information.

[0156] Since the target is dynamically moving, observations at a single moment cannot accurately reflect its position and velocity information, and different observations within a redundant cluster also exhibit inherent error differences. Therefore, the above steps combine the target motion model and observation reliability to fuse and optimize the observations. The Kalman filter algorithm can simultaneously achieve state prediction and observation updates, ensuring that the prediction phase aligns with the actual motion patterns of the target (such as uniform speed, acceleration, and deceleration), avoiding unfounded state estimation biases. Furthermore, by utilizing multi-source reliable observations from the effective redundant cluster, the deviation between the predicted and actual observations is accurately corrected, while the covariance corresponding to the observations is updated to quantify the error range of the current state. Ultimately, the optimized state information of the target is obtained, providing more quantitative basis for subsequent vehicle decisions.

[0157] In another preferred embodiment of this application, a vehicle is also disclosed, including a vehicle body and a plurality of UWB signal detectors and controllers disposed on the vehicle body, wherein the controllers operate based on the vehicle door opening warning method in the above embodiments.

[0158] On the other hand, the vehicle is also equipped with a sound alarm that is electrically connected to the controller, through which warning information is output.

[0159] This application also discloses a vehicle door opening warning system, which includes one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors. The programs include instructions for performing the vehicle door opening warning method as described above. The processor may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, used to execute the relevant programs to implement the functions required by the modules in the vehicle door opening warning system of this application embodiment, or to execute the vehicle door opening warning method of this application method embodiment.

[0160] This application also discloses a computer-readable storage medium comprising a computer program executable by a processor to perform the vehicle door opening warning method described above. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid-state disks (SSDs).

[0161] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned vehicle door opening warning method.

Claims

1. A vehicle door opening warning method, comprising: Based on several UWB signal detectors installed on the vehicle body, the UWB echo signal behind the side of the vehicle body is detected in real time. The UWB echo signal is the UWB signal reflected back from the object when the standard UWB signal emitted by the UWB signal transmitter hits the object. The UWB echo signal is analyzed to obtain the detection signal; When the vehicle door status changes from locked to open, if the detection signal indicates that a moving object is approaching the vehicle and the distance between the moving object and the vehicle is less than a preset threshold, then a warning message is output based on the detection signal.

2. The vehicle door opening warning method according to claim 1, wherein The UWB signal detectors are installed at least at the rear of the vehicle body, the middle of the vehicle body, and on both sides of the front of the vehicle body.

3. The vehicle door opening warning method according to claim 1, wherein The moving object is a living being that is close to the vehicle.

4. The vehicle door opening warning method according to claim 3, wherein The detection signal is generated by analyzing and identifying minute motion changes in the UWB echo signal to detect signal components related to the physiological activities of living organisms.

5. The vehicle door opening warning method of claim 1, wherein The target direction of the moving object causing the change in the UWB signal is determined by the changes in several UWB echo signals. Based on the UWB echo signal aligned with the target direction, the core detection results are generated. Auxiliary detection results are generated based on the UWB echo signal that deviates from the target direction; By analyzing the auxiliary detection results, environmental noise data is distinguished and extracted to obtain an estimate of the background noise. The core detection result is filtered based on the background noise to obtain the detection signal.

6. The vehicle door opening warning method of claim 1, wherein The UWB signal transmitter is installed on the vehicle body or in a signal base station near the exterior of the vehicle body.

7. The vehicle door opening warning method of claim 1, wherein The detection signal includes any one or more of the following: The distance between the moving object and the vehicle body corresponding to the current UWB echo signal; The velocity of the moving object corresponding to the UWB echo signal mentioned above; The azimuth angle of the moving object relative to the vehicle body corresponding to the current UWB echo signal; The direction of motion of the moving object corresponding to the UWB echo signal mentioned above.

8. The vehicle door opening warning method of claim 1, wherein Analyzing the UWB echo signal includes at least the following steps: Distance and velocity information acquisition: The received UWB echo signal is processed in a structured manner to extract and output the distance and velocity information of the moving object; Azimuth information acquisition: Based on the signal characteristic differences of the same received UWB echo signal, the azimuth information of the moving object is extracted and output; Target information fusion: Multi-source data fusion processing is performed on the distance and velocity information and the azimuth information to obtain the spatial position and motion state of the moving object.

9. The vehicle door opening warning method according to claim 8, wherein The steps for obtaining distance and speed information include: When performing structured processing on the received UWB echo signal, a multi-dimensional signal model is first constructed, which includes a signal representation that is associated with distance and velocity information. Then, the distance and velocity information is extracted based on the signal representation. Before extracting the distance and speed information, interference suppression processing is performed on the received UWB echo signal, and the effective signal features directly associated with the moving object are enhanced.

10. The vehicle door opening warning method according to claim 9, wherein The construction of the multi-dimensional signal model includes: Construct a CIR matrix that includes a fast time dimension and a slow time dimension; where the fast time dimension corresponds to the distance information of the moving object, and the slow time dimension corresponds to the speed information of the moving object.

11. The vehicle door opening warning method of claim 10, wherein, The interference suppression processing performed on the received UWB echo signal includes: Perform a Fourier transform on the time-domain signal of the CIR matrix to obtain the range-Doppler spectrum; Frequency domain equalization compensates for selective signal fading caused by multipath interference.

12. The vehicle door opening warning method of claim 11, wherein, The interference suppression processing performed on the received UWB signal further includes: The frequency domain signal of the distance-Doppler spectrum is converted back to the time domain signal of the CIR matrix by inverse Fourier transform in order to enhance the significance of the pulse peak of the direct path of the UWB echo signal.

13. The vehicle door opening warning method of claim 11, wherein, The interference suppression processing of the received UWB echo signal further includes: A constant false alarm rate (CFAR) detection algorithm is used to process the frequency domain signal of the range-Doppler spectrum, identify and lock the pulse peak position of the direct path of the UWB echo signal, and use the pulse peak position of the direct path as the reference for extracting distance and velocity values.

14. The vehicle door opening warning method of claim 12, wherein, The interference suppression processing of the received UWB echo signal further includes: A constant false alarm rate (CFAR) detection algorithm is used to process the time-domain signal of the CIR matrix, identify and lock the pulse peak position of the direct path of the UWB echo signal, and use the pulse peak position of the direct path as the reference for extracting distance and velocity values.

15. The vehicle door opening warning method according to any one of claims 13 or 14, wherein, The distance and speed information acquisition step further includes optimization processing, which includes: Based on the pulse peak position of the direct path, the parameter accuracy of the distance and velocity values ​​is improved by resolution enhancement methods; By fusing multi-source observation data to reduce noise interference, optimized distance and velocity values ​​are output.

16. The vehicle door opening warning method of claim 15, wherein, The method of improving the accuracy of distance and velocity values ​​through resolution enhancement includes: A local window is defined centered on the pulse peak position of the direct path. A smooth interpolation algorithm is then applied to the real and imaginary parts of the signal within the local window to generate subdivided sampling points by fitting the signal change trend. Based on the constraints of the subdivided sampling points, the pulse peak optimization position of the direct path is repositioned, and the pulse peak optimization position of the direct path is used as the benchmark for extracting the distance and speed optimization values.

17. The vehicle door opening warning method of claim 16, wherein, The distance and speed information acquisition step further includes distance calculation and speed calculation, wherein the distance calculation and speed calculation include: By the formula Calculate the distance information of the moving object; where d is the distance between the moving object and the vehicle body, c is the speed of light, and Δt is the round-trip time of the UWB signal; By the formula Calculate the velocity information of the moving object; where v is the radial velocity of the moving object, Δf is the frequency offset of the UWB echo signal, and λ is the wavelength of the UWB echo signal.

18. The vehicle door opening warning method of claim 17, wherein, The fusion of multi-source observation data to reduce noise interference includes: The observation values of the plurality of receiving modules are fused by a weighted least square method, and the formula is Calculate the final distance information; wherein the UWB signal detector includes multiple receiving modules arranged at a preset interval, w1 and w2 are inversely proportional to the signal-to-noise ratio of different receiving modules of the same UWB signal detector, d1 and d2 are the distance observation values ​​of different receiving modules of the same UWB signal detector, and d_final is the final distance information.

19. The vehicle door opening warning method of claim 8, wherein, The steps for obtaining the azimuth information include: Based on the signal characteristic differences between the same UWB echo signal received by multiple receiving modules, the azimuth angle value of the target object is output through stability optimization and accuracy improvement processing; wherein the UWB signal detector includes multiple receiving modules arranged at a preset interval.

20. The vehicle door opening warning method of claim 19, wherein, The output of the azimuth angle value of the target object includes: The Capon beamforming algorithm is used to search for the peak value of the spatial spectrum function, and the phase difference corresponding to the peak value is used as the phase difference value. The spatial spectrum function is constructed based on the steering vector and the signal covariance matrix. The steering vector is the phase distribution template of the UWB echo signal in different receiving modules, and the signal covariance matrix is ​​calculated by the spacing between multiple receiving modules and the wavelength of the UWB echo signal.

21. The vehicle door opening warning method of claim 20, wherein, The stability optimization and accuracy improvement processes include: The signal covariance matrix is ​​subjected to diagonal loading processing, and a diagonal perturbation term of a set strength is added to the signal covariance matrix to improve the numerical stability of the azimuth angle value.

22. The vehicle door opening warning method of claim 21, wherein, The acquisition of azimuth information also includes: Based on the phase difference physical model formula solving for the azimuth angle value; where θ is the azimuth angle value, Δ Let d be the phase difference, d be the spacing between the receiving modules, and λ be the wavelength of the UWB echo signal.

23. The vehicle door opening warning method of claim 8, wherein, The target information fusion step includes time synchronization, coordinate system 1, and uncertainty initialization. The time synchronization, coordinate system 1, and uncertainty initialization include: All observations are synchronized to the same time base; wherein the observations include the distance and velocity information and the azimuth information observed by different UWB signal detectors; The observed values ​​in the local coordinate system with the UWB signal detector as the origin are transformed to the global coordinate system with the rear axle center of the vehicle as the origin; A covariance matrix is ​​assigned to the observations; wherein the covariance matrix is ​​determined by the reliability of each observation.

24. The vehicle door opening warning method of claim 23, wherein, The target information fusion step further includes redundancy detection and clustering grouping, wherein the redundancy detection and the clustering grouping include: Set a spatial distance threshold; For any two observations from different UWB signal detectors, calculate the Euclidean distance between them in the global coordinate system; Determine the magnitude of the Euclidean distance and the spatial distance threshold, and determine whether there is redundant correlation between the observed values ​​based on the comparison result; A distance-based clustering algorithm is used to group the observations that are close in location in the global coordinate system into the same redundant cluster; Determine whether the redundant cluster contains at least two observations from different UWB signal detectors; if so, the redundant cluster is determined to be a valid redundant cluster.

25. The vehicle door opening warning method of claim 24, wherein, The target information fusion step further includes a fusion output, which includes: Based on the target motion model, the state prediction information of the moving object at the next moment is obtained; Obtain all observations within the effective redundant cluster and their corresponding observation noise covariance matrix; Using the observed values ​​and the observed noise covariance matrix, the state prediction information is updated using the Kalman filter algorithm to obtain the fused state optimization information; The optimized state information is output; wherein the state prediction information and the state optimization information include the spatial position and motion state of the moving object.

26. A vehicle, comprising a vehicle body and a plurality of UWB signal detectors and a controller disposed on the vehicle body, the controller operating based on the vehicle door opening warning method according to any one of claims 1 to 25.

27. The vehicle of claim 26, further comprising an audible alarm electrically connected to the controller.

28. A vehicle door opening warning system, comprising: One or more processors; Memory; And one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including instructions for performing the vehicle door opening warning method as claimed in any one of claims 1 to 25.

29. A computer-readable storage medium comprising a computer program that can be executed by a processor to perform the vehicle door opening warning method as claimed in any one of claims 1 to 25.

Images