Travel state determination method for vehicle, electronic device, and vehicle

Abstract

Embodiments of the present application provide a driving state determination method of a vehicle, an electronic device and the vehicle. The method comprises: obtaining residual information of a positioning system in the vehicle, wherein the residual information is used to represent a difference between a state observation value collected by the positioning system and an actual state value of the vehicle; in response to the residual information being less than a residual information threshold, determining difference information between longitudinal displacement information of the vehicle in a driving process and rotation information of a wheel end in the vehicle, wherein the difference information is used to represent a drift degree of the vehicle in the longitudinal direction relative to the wheel end; and determining a target driving state of the vehicle based on the difference information and a current driving state of the vehicle, wherein the target driving state is used to make the drift degree of the vehicle less than the drift degree of the vehicle in the current driving state. The present application solves the technical problem of low accuracy of the driving state determination of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more specifically, to a method for determining the driving state of a vehicle, an electronic device, and a vehicle. Background Technology

[0002] In related technologies, dead reckoning (DR) is used to determine the driving status of a vehicle in a GNSS-denied environment.

[0003] However, the above methods are susceptible to errors from vehicle sensors and dynamic changes in the vehicle's tire rolling radius, leading to significant discrepancies between the determined vehicle's driving state and its actual driving state. This results in an inability to meet lane-level positioning requirements. Therefore, the technical problem of low accuracy in determining the vehicle's driving state remains.

[0004] There is currently no good solution to the above problems. Summary of the Invention

[0005] This application provides a method for determining the driving status of a vehicle, an electronic device, and a vehicle, to at least solve the technical problem of low accuracy in determining the driving status of a vehicle.

[0006] According to one aspect of the embodiments of this application, a method for determining the driving state of a vehicle is provided, wherein the method may include: acquiring residual information of a positioning system in the vehicle, wherein the residual information is used to represent the degree of difference between the state observation values ​​collected by the positioning system for the vehicle and the actual state values ​​of the vehicle; in response to the residual information being less than a residual information threshold, determining the difference information between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle, wherein the difference information is used to characterize the degree of drift of the vehicle relative to the wheel ends in the longitudinal direction; and determining a target driving state of the vehicle based on the difference information and the current driving state of the vehicle, wherein the target driving state is used to make the degree of drift of the vehicle less than the degree of drift of the vehicle in the current driving state.

[0007] Furthermore, based on the difference information and the vehicle's current driving state, the target driving state of the vehicle is determined, including: in response to the positioning system being in a failed state, determining the target difference information that has a mapping relationship with the current driving state from the mapping table corresponding to the difference information; and determining the target driving state based on the target difference information and the current driving state.

[0008] Furthermore, the current driving state includes the vehicle's current speed and current steering angle. The difference information is the wheel speed ratio factor. In response to the positioning system being in a failure state, the target difference information that has a mapping relationship with the current driving state is determined from the mapping table corresponding to the difference information. This includes: in response to the positioning system being in a failure state, the wheel speed ratio factor that has a mapping relationship with the current speed and current steering angle is determined from the mapping table.

[0009] Further, the residual information includes position residual information and phase residual information. Obtaining the residual information of the positioning system in normal state includes: determining the difference between the predicted position collected by the positioning system in normal state and the actual position of the vehicle as position residual information, wherein the position residual information is used to represent the degree of difference between the predicted position and the actual position; determining the difference between the phase observation value collected by the positioning system in normal state and the actual phase value of the vehicle as phase residual information, wherein the phase residual information is used to represent the degree of difference between the phase observation value and the actual phase value; and / or, the method further includes: determining that the residual information is less than the residual information threshold in response to the position residual information being less than the position residual information threshold and the phase residual information being less than the phase residual information threshold.

[0010] Furthermore, the difference information is the wheel speed ratio factor, and the rotation information is the number of wheel speed pulses at the wheel ends. The difference information between the longitudinal displacement information of the vehicle during operation and the rotation information at the wheel ends includes: within a time window, acquiring a first number of sampling points for the wheel speed pulse count; within the time window, determining the theoretical displacement of the vehicle based on the first number of wheel speed pulses and the wheel speed ratio estimate, and determining the displacement difference between the longitudinal displacement information and the theoretical displacement within the time window; within the time window, acquiring a second number of longitudinal displacement information, and determining the sum of squared displacement errors for the second number of displacement differences; and determining the wheel speed ratio factor based on the sum of squared displacement errors.

[0011] Furthermore, the method further includes: in response to the positioning system being in a normal state, establishing a mapping table between wheel speed ratio factors and the vehicle's current speed and current steering angle; determining the wheel speed ratio factors in the mapping table as the previous wheel speed ratio factors; and / or, the method further includes: performing a weighted average of the wheel speed ratio factors and the previous wheel speed ratio factors to obtain a target wheel speed ratio factor for the previous wheel speed ratio factor in the mapping table to be replaced; using the target wheel speed ratio factor to replace the previous wheel speed ratio factor to obtain a replaced mapping table.

[0012] Furthermore, in response to the residual information being greater than or equal to the residual information threshold, the process returns to begin with the following steps: obtaining the residual information.

[0013] According to another aspect of the embodiments of this application, a vehicle driving state determination device is also provided. The device may include: an acquisition unit, configured to acquire residual information of a positioning system in the vehicle, wherein the residual information is used to represent the degree of difference between the state observation values ​​collected by the positioning system for the vehicle and the actual state values ​​of the vehicle; a first determination unit, configured to determine the difference information between longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle in response to the residual information being less than a residual information threshold, wherein the difference information is used to characterize the degree of drift of the vehicle in the longitudinal direction relative to the wheel ends; and a second determination unit, configured to determine a target driving state of the vehicle based on the difference information and the current driving state of the vehicle, wherein the target driving state is used to make the degree of drift of the vehicle less than the degree of drift of the vehicle in the current driving state.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0015] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the methods in various embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0020] In this embodiment, residual information of the positioning system in the vehicle is obtained, wherein the residual information is used to represent the degree of difference between the state observation values ​​collected by the positioning system for the vehicle and the actual state values ​​of the vehicle; in response to the residual information being less than the residual information threshold, the difference information between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle is determined, wherein the difference information is used to characterize the degree of drift of the vehicle in the longitudinal direction relative to the wheel ends; based on the difference information and the current driving state of the vehicle, the target driving state of the vehicle is determined, wherein the target driving state is used to make the degree of drift of the vehicle less than the degree of drift of the vehicle in the current driving state. In other words, in this embodiment, when the residual information of the positioning system is less than the residual information threshold, the difference between the longitudinal displacement information and the wheel end rotation information is determined. Based on this difference information and the vehicle's current driving state, the target driving state of the vehicle is determined. This method allows for timely correction of the vehicle's current driving state based on the difference information, enabling the target driving state to dynamically offset the effects of sensor errors and dynamic changes in the vehicle's tire rolling radius in related technologies. The error between the vehicle's driving state determined by this method and the actual driving state is lower than in related technologies, meeting lane-level positioning requirements. This solves the technical problem of low accuracy in determining the vehicle's driving state and achieves the technical effect of improving the accuracy of vehicle driving state determination. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a flowchart of a method for determining the driving state of a vehicle according to an embodiment of this application;

[0023] Figure 2 This is a schematic diagram illustrating an application scenario of a method for determining the driving state of a vehicle according to an embodiment of this application;

[0024] Figure 3 This is a flowchart of a vehicle dead reckoning enhancement method according to an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of a vehicle dead reckoning enhancement module according to an embodiment of this application;

[0026] Figure 5 This is a schematic diagram of a vehicle driving state determination device according to an embodiment of this application;

[0027] Figure 6This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] This embodiment provides a method for determining the driving status of a vehicle. Figure 1 This is a flowchart of a method for determining the driving state of a vehicle according to an embodiment of this application, such as... Figure 1 As shown, the method may include the following steps.

[0031] Step S102: Obtain the residual information of the positioning system in the vehicle.

[0032] In the technical solution provided by step S102 of the embodiments of this application, the positioning system can be used to refer to a system capable of real-time positioning of the vehicle. The positioning system can perform real-time positioning using operational information during vehicle operation, including but not limited to vehicle speed and location. The positioning system can include, but is not limited to, a global navigation satellite system.

[0033] Optionally, the aforementioned residual information can be used to represent the degree of difference between the state observations of the vehicle collected by the positioning system and the actual state values ​​of the vehicle. The aforementioned residual information may include, but is not limited to, position residuals and carrier phase residuals. The aforementioned state observations may include, but are not limited to, the observed position of the vehicle and the observed phase of the vehicle. The aforementioned actual state values ​​may include, but are not limited to, the actual position of the vehicle and the expected phase. The aforementioned position residuals can be used to represent the degree of difference between the observed position of the vehicle and the actual position of the vehicle. The aforementioned carrier phase residuals can be used to represent the degree of difference between the observed phase of the vehicle and the expected phase.

[0034] Optionally, when the vehicle is in operation, its RTK position can be calculated using the Real-Time Kinematic (RTK) positioning system in the aforementioned GNSS. The vehicle's actual position can be determined using a high-precision reference system (e.g., a high-precision map or known coordinates of a base station), and the position residual can be determined based on the RTK calculated position and the vehicle's actual position.

[0035] In this embodiment of the application, the position residual in the RTK can also be read through the interface of the GNSS receiver, wherein the position residual ΔPos_residual can be determined by the following formula.

[0036] ΔPos_residual=√(σ_E²+σ_N²)

[0037] Wherein, σ_E can represent the eastward positioning covariance (unit: meters) and σ_N can represent the northward positioning covariance (unit: meters). The above σ_E and σ_N can be obtained by parsing the messages in the above GNSS receiver. The above messages may include, but are not limited to, the northward positioning covariance, the eastward positioning covariance, the altitude standard deviation, and the root mean square position error.

[0038] Optionally, the observed phase of the vehicle can be calculated using the real-time dynamic positioning in GNSS, and the theoretical phase expectation of the vehicle can be obtained based on satellite orbit parameters or receiver clock bias. Based on the observed phase and phase expectation, the carrier phase residual can be determined.

[0039] In this embodiment, the internal status word of the RTK in the GNSS receiver can also be read through the receiver's interface. This internal status word includes, but is not limited to, a fixed solution flag, position covariance, and carrier phase residual (also known as RMS). The carrier phase residual can be obtained through this method. The position residual and carrier phase residual can then be defined as residual information.

[0040] In this embodiment of the application, residual information can be directly obtained through the above step S202, thereby avoiding errors caused by indirect estimation or model speculation and improving the accuracy of information sources.

[0041] Step S104: In response to the residual information being less than the residual information threshold, determine the difference between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle.

[0042] In the technical solution provided by step S104 of this application embodiment, the residual information threshold can be used to represent the preset conditions that the acquired residual information needs to meet to satisfy preset accuracy or stability. The longitudinal displacement information can be used to represent the longitudinal displacement increment of the vehicle during driving, wherein the longitudinal direction can be used to represent the driving direction of the vehicle. The wheel end rotation information can be used to represent the wheel speed pulse count of the vehicle, wherein for each revolution of the vehicle's wheel end, the vehicle's wheel end sensor can generate a fixed number of pulses, the pulse count can be used to quantify the wheel rotation amount, and the pulse count can also be called the wheel speed pulse count. The difference information can be used to represent the degree of drift of the vehicle in the longitudinal direction relative to the wheel end. The difference information may include, but is not limited to, the actual displacement of the vehicle corresponding to each pulse generated by the vehicle's wheel end sensor.

[0043] Optionally, after obtaining the residual information of the positioning system in the vehicle, in response to the residual information being less than the residual information threshold, the difference between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle can be determined.

[0044] Optionally, in response to the residual information being less than a residual information threshold, the longitudinal displacement information of the vehicle during operation can be determined through real-time dynamic positioning in GNSS. The rotation information of the wheel ends in the vehicle can be determined through wheel-end sensors; the difference between the aforementioned longitudinal displacement information and rotation information can be determined through a preset function.

[0045] For example, given the position residual <0.05m and phase residual <0.01 cycles, the time period for acquiring the residual information can be identified as a high-quality GNSS segment. Within this high-quality GNSS segment, the scale factor (unit: m / pulse) of the wheel velocity pulse versus actual displacement can be calculated by establishing an optimization objective function. This optimization objective function can be expressed by the following formula.

[0046]

[0047] Wherein, d_RTK can represent the longitudinal displacement increment of the vehicle during driving as measured by RTK, pulse_i can represent the wheel speed pulse count, and M(k) can represent the total number of pulse sampling points in a high-quality GNSS segment.

[0048] In this embodiment of the application, after the residual information meets the residual information threshold, the wheel speed ratio factor reflecting the actual vehicle displacement corresponding to each pulse can be directly calculated based on the measured longitudinal displacement and wheel speed pulse count after step S204. The calculation process of the above steps is simple and has good real-time performance. By triggering within a high-quality GNSS segment, the accuracy of the calculation results can be avoided by residual information with low precision or poor stability.

[0049] Step S106: Based on the difference information and the current driving state of the vehicle, determine the target driving state of the vehicle, wherein the target driving state is used to make the drift degree of the vehicle less than the drift degree of the vehicle in the current driving state.

[0050] In the technical solution provided by step S106 of this application embodiment, the aforementioned current driving state can be used to represent the real-time operating parameters of the vehicle. These real-time operating parameters may include, but are not limited to, the current vehicle speed and the vehicle's steering angle. For example, the aforementioned current driving state can be the vehicle's current speed and steering angle after GNSS signal loss. The aforementioned target driving state can be used to make the vehicle's drift degree less than the drift degree of the vehicle in its current driving state. The aforementioned target driving state can also be referred to as v_correcte.

[0051] Optionally, after determining the difference between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle in response to the residual information being less than the residual information threshold, the target driving state of the vehicle can be determined based on the difference information and the current driving state of the vehicle.

[0052] Optionally, the current driving state of the vehicle can be obtained through the vehicle's body control unit or chassis domain controller. The current driving state may include the vehicle's current speed and steering angle. Based on the current speed and steering angle, corresponding difference information can be obtained through a preset dynamic lookup table. This preset dynamic lookup table may include the mapping relationship between the current speed, steering angle, and the corresponding difference information. The dynamic lookup table can be represented by a LUT. Based on the difference information and the vehicle's current driving state, the current speed and steering angle can be corrected, and the corrected speed and steering angle can be determined as the target driving state.

[0053] In this embodiment, after the vehicle loses its GNSS signal, determining the vehicle's target driving state using the above method can resolve the vehicle's longitudinal error in the scenario of GNSS signal loss.

[0054] In steps S102 to S106 of this embodiment, residual information of the positioning system in the vehicle is obtained, wherein the residual information is used to represent the degree of difference between the state observation values ​​collected by the positioning system for the vehicle and the actual state values ​​of the vehicle; in response to the residual information being less than the residual information threshold, the difference information between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle is determined, wherein the difference information is used to characterize the degree of drift of the vehicle in the longitudinal direction relative to the wheel ends; based on the difference information and the current driving state of the vehicle, the target driving state of the vehicle is determined, wherein the target driving state is used to make the degree of drift of the vehicle less than the degree of drift of the vehicle in the current driving state. In other words, in this embodiment, when the residual information of the positioning system is less than a preset residual information threshold, the difference between the longitudinal displacement information and the wheel end rotation information is determined. Based on this difference information and the vehicle's current driving state, the target driving state of the vehicle is determined. This method allows for timely correction of the vehicle's current driving state based on the difference information, enabling the target driving state to dynamically offset the effects of sensor errors and dynamic changes in the vehicle's tire rolling radius in related technologies. This overcomes the obstacle of large errors in determining the vehicle's driving state in related technologies, which fails to meet lane-level positioning requirements. Therefore, it solves the technical problem of low accuracy in determining the vehicle's driving state and achieves the technical effect of improving the accuracy of vehicle driving state determination.

[0055] The embodiments of this application will be described in detail below with reference to the steps described above.

[0056] As an optional implementation, step S106, determining the target driving state of the vehicle based on the difference information and the current driving state of the vehicle, includes: in response to the positioning system being in a failed state, determining the target difference information that has a mapping relationship with the current driving state from the mapping table corresponding to the difference information; and determining the target driving state based on the target difference information and the current driving state.

[0057] In this embodiment, the aforementioned failure state can be used to indicate an operational state where the vehicle's GNSS positioning signal is lost or unavailable, and positioning reference cannot be provided. Examples include when the vehicle enters a tunnel, underground parking garage, or area under an overpass where satellite signals are blocked. The aforementioned mapping table can be used to represent a pre-built two-dimensional dynamic lookup table, which can be represented by a LUT. The aforementioned target difference information can be used to represent the optimal wheel speed ratio factor obtained from the pre-built two-dimensional dynamic lookup table under the current driving state.

[0058] Optionally, it can be detected whether the gnssValidFlag message output by the vehicle's positioning system is FALSE, or whether the fixed solution flag in the vehicle's RTK is invalid. If it is confirmed that the gnssValidFlag message is FALSE or the fixed solution flag in the vehicle's RTK is invalid, it can be confirmed that the positioning system is in a failed state.

[0059] Optionally, in response to the positioning system being in a failed state, the current vehicle speed (which can be represented by v) and the vehicle's steering angle (which can be represented by δ) can be obtained from the vehicle's Controller Area Network (CAN) bus. Based on the above vehicle speed and steering angle step size, the target difference information can be confirmed through a pre-built two-dimensional dynamic lookup table (LUT). The vehicle speed can be corrected based on the above target difference information to obtain the target driving state.

[0060] In the embodiments of this application, the above method can be used to correct the vehicle speed output in real time by utilizing the target difference information in the pre-constructed two-dimensional dynamic lookup table (LUT) when the vehicle's positioning system fails, thereby improving the positioning security and availability of the vehicle in satellite-denied environments such as tunnels and underground parking garages.

[0061] As an optional implementation, the current driving state includes the vehicle's current speed and current steering angle, and the difference information is the wheel speed ratio factor. In response to the positioning system being in a failure state, the target difference information that has a mapping relationship with the current driving state is determined from the mapping table corresponding to the difference information, including: in response to the positioning system being in a failure state, the wheel speed ratio factor that has a mapping relationship with the current vehicle speed and current steering angle is determined from the mapping table.

[0062] In this embodiment, the wheel speed scaling factor can be used to represent the conversion coefficient between the number of pulses output by the wheel speed sensor and the actual longitudinal displacement of the vehicle. The wheel speed scaling factor can be represented by ScaleFactor. The mapping table corresponding to the aforementioned difference information can be represented by LUT(v, δ).

[0063] Optionally, the gnssValidFlag message output by the RTK or the fixed solution flag in the RTK is read. If the gnssValidFlag message is confirmed to be FALSE or the fixed solution flag in the vehicle's RTK is invalid, the positioning system is determined to be in a failed state. The current vehicle speed and steering angle can be obtained through the vehicle's CAN bus. Based on the current vehicle speed and steering angle, the corresponding LUT index is calculated. If the current vehicle speed and steering angle are located between the grid intersections of the two-dimensional dynamic lookup table (LUT), bilinear interpolation can be used to determine the wheel speed scaling factor based on four neighboring entries.

[0064] In this embodiment of the application, the above method can be used to realize real-time confirmation of the wheel speed sensor scaling factor in the case of vehicle positioning system failure.

[0065] As an optional implementation, the current driving state includes the current wheel speed pulse count, and the target driving state includes the target speed, which represents the vehicle's longitudinal driving speed. Based on the target difference information and the current driving state, the target driving state is determined by: determining the product between the wheel speed scaling factor with a mapping relationship and the current wheel speed pulse count; and determining the target speed based on the product and the time interval between collecting the current driving state.

[0066] In this embodiment, the aforementioned time interval can be used to represent the acquisition period of the wheel speed pulse signal (which can be represented by Δt), that is, the time difference between two adjacent wheel speed pulse counts, in seconds (which can be represented by s). The aforementioned time interval is determined by the original sampling frequency of the vehicle's wheel speed sensor, for example, it can be 10ms (corresponding to a 100Hz sampling rate). The aforementioned target speed can be used to represent the longitudinal speed of the vehicle after correction by the wheel speed scaling factor (ScaleFactor).

[0067] Optionally, within the acquisition period (time interval) Δt (e.g., 0.1s), the pulse count increment output by the wheel speed sensor is read (which can be represented by Δpulse), where the pulse count increment can be expressed by the following formula.

[0068] Δpulse=pulse_cnt(t)-pulse_cnt(t-0.1s)

[0069] Where pulse_cnt(t) represents the total number of pulses output by the wheel speed sensor at the current time t, and pulse_cnt(t-0.1s) represents the total number of pulses output by the wheel speed sensor at the end of the previous acquisition cycle (t-0.1s). Based on the current vehicle speed v and steering angle δ, the corresponding wheel speed scaling factor ScaleFactor is retrieved from the pre-stored two-dimensional LUT, and the RTK displacement increment is read, which can be represented by Δd_RTK in meters. The RTK displacement increment can be determined by the following formula.

[0070] Δd_RTK=√((E_t-E_{t-0.1s})²+(N_t-N_{t-0.1s})²

[0071] Where E_t represents the eastward coordinate of the vehicle output by RTK at the current time t, and N_t represents the northward coordinate of the vehicle output by RTK at the current time t. The target speed (which can be represented by v_corrected) can be determined based on the above RTK displacement increments. The target speed can be determined by the following formula.

[0072]

[0073] Here, SF_cur can represent the wheel speed ratio factor obtained from the two-dimensional dynamic lookup table LUT under the current vehicle speed v and steering angle.

[0074] In the embodiments of this application, the wheel speed pulse can be corrected by using a pre-calibrated wheel speed scaling factor (ScaleFactor) through the above method, thereby adapting to the error caused by changes in tire radius due to factors such as load, tire pressure, and temperature, and reducing longitudinal speed estimation deviation.

[0075] As an optional implementation, the residual information includes position residual information and phase residual information. Obtaining the residual information of a positioning system in normal operation includes: determining the difference between the predicted position collected by the positioning system in normal operation and the actual position of the vehicle as position residual information, wherein the position residual information is used to represent the degree of difference between the predicted position and the actual position; determining the difference between the phase observation value collected by the positioning system in normal operation and the actual phase value of the vehicle as phase residual information, wherein the phase residual information is used to represent the degree of difference between the phase observation value and the actual phase value; and / or, the method further includes: in response to the position residual information being less than a position residual information threshold and the phase residual information being less than a phase residual information threshold, determining that the residual information is less than a residual information threshold.

[0076] In this embodiment, the aforementioned position residual information can be used to represent the degree of difference between the predicted position and the actual position, and can be represented by ΔPos_residual. The aforementioned phase residual information can be used to represent the degree of difference between the observed phase value and the actual phase value. The aforementioned phase observed value can be used to represent the actually observed carrier phase value. The aforementioned actual phase value can be used to represent the carrier phase value that the model should predict.

[0077] Optionally, the eastward covariance (which can be represented by σ_E) and the northward covariance (which can be represented by σ_N) can be parsed from the NMEA GPGST or UBX-NAV-PVT messages output by the RTK. The position residual information can then be calculated using the aforementioned eastward and northward covariances. The calculation of the position residual information can be expressed by the following formula.

[0078] ΔPos_residual=√(σ_E²+σ_N²)

[0079] Optionally, carrier phase residuals can be extracted from the UBX-RXM-RAWX or similar raw observation messages from the RTK receiver. The root mean square (RMS) of all carrier phase residuals can be taken as the phase residual information at the current moment.

[0080] Optionally, the position residual threshold is set to 0.05m and the phase residual threshold is set to 0.01 cycles. Continuous monitoring is performed for 5 consecutive RTK output cycles (i.e., 0.5 seconds). If the position residual is always less than 0.05m and the phase residual RMS is always less than 0.01 cycles, the current segment is determined to be a "high-quality observation segment", and the calibration of the wheel speed scaling factor is allowed. If any condition is not met (e.g., position residual > 0.06m or phase residual RMS > 0.015 cycles), the calibration is skipped and the LUT entries are not updated.

[0081] In this embodiment, the above method can use the position covariance and carrier phase residual generated by RTK as the basis for calibration quality screening. The wheel speed ratio factor calibration is performed during the period when the positioning quality is stable and the error is controllable, thereby improving the reliability and consistency of the calibration data. By jointly judging the dual residuals, the adaptability of the wheel speed ratio factor calibration process to complex environments (such as urban canyons and weak signal areas) is improved, making the above method more stable in actual road scenarios.

[0082] As an optional implementation, the difference information is the wheel speed ratio factor, and the rotation information is the number of wheel speed pulses at the wheel ends. Determining the difference between the longitudinal displacement information of the vehicle during driving and the rotation information at the wheel ends includes: within a time window, acquiring a first number of sampling points for the wheel speed pulse count; within the time window, determining the theoretical displacement of the vehicle based on the first number of wheel speed pulses and the wheel speed ratio estimate, and determining the displacement difference between the longitudinal displacement information and the theoretical displacement within the time window; within the time window, acquiring a second number of longitudinal displacement information, and determining the sum of squared displacement errors for the second number of displacement differences; and determining the wheel speed ratio factor based on the sum of squared displacement errors.

[0083] In this embodiment, the sampling points can be the pulse sampling moments of the wheel speed sensor within a unit time, i.e., the points where the wheel speed pulse signal is discretely acquired at a fixed frequency (e.g., 100Hz), and each sampling point corresponds to a pulse count increment. The wheel speed pulse count can be used to represent the pulse count increment output by the wheel speed sensor within a certain sampling period, reflecting the rotation of the wheel during that time period. The wheel speed pulse count can be represented by pulse_i(k). The first quantity can be used to represent the total number of wheel speed pulse sampling points within a time window, i.e., the number of wheel speed pulse increment samples acquired within that window, and can be represented by M(k). The theoretical displacement. The time window can be used to represent the period length of one displacement increment output by the RTK, e.g., 0.1 seconds. The second quantity can be used to represent the number of RTK longitudinal displacement increment samples acquired within a time window, i.e., the number of effective displacement values ​​output by the RTK within the time window, and can be represented by N. The displacement difference can be represented by Δd_RTK(k).

[0084] Optionally, within the current time window k, the pulse increment output by the wheel speed sensor is read; the theoretical displacement is calculated based on the current wheel speed ratio estimate, which can be expressed by the following formula.

[0085]

[0086] Optionally, the longitudinal displacement increment output by RTK within this window can be read, and the displacement difference Δd_RTK(k) can be calculated using the following formula.

[0087]

[0088] Where pulse_i can represent the wheel speed pulse count, and M(k) can represent the total number of pulse sampling points in a high-quality GNSS segment.

[0089] In this embodiment, the wheel speed scaling factor can be deduced from the matching relationship between RTK longitudinal displacement information and wheel speed pulses using the above method, thereby reducing error propagation; the statistical stability of the wheel speed scaling factor calibration results can be improved by using batch data from multiple time windows, reducing the impact of single-point noise; and performing the above steps within high-quality GNSS segments can ensure the reliability of input data and avoid abnormal data contaminating the calibration.

[0090] As an optional implementation, the method further includes: in response to the positioning system being in a normal state, establishing a mapping table between wheel speed ratio factors and the vehicle's current speed and current steering angle; determining the wheel speed ratio factors in the mapping table as the previous wheel speed ratio factors; and / or, the method further includes: performing a weighted average of the wheel speed ratio factors and the previous wheel speed ratio factors to obtain a target wheel speed ratio factor for the previous wheel speed ratio factor in the mapping table to be replaced; and replacing the previous wheel speed ratio factor using the target wheel speed ratio factor to obtain a replaced mapping table.

[0091] In this embodiment, the aforementioned normal state indicates that the vehicle's GNSS positioning signal is available and can provide a positioning reference. In this state, the longitudinal displacement increment provided by RTK is reliable and can be used to effectively calibrate the wheel speed scaling factor. The previous wheel speed scaling factor in the above-mentioned mapping table to be replaced can be represented by SF_old(v, δ), and the above-mentioned target wheel speed scaling factor can be represented by SF_new(v, δ).

[0092] Optionally, within a high-quality RTK segment, the wheel speed scaling factor under the current operating condition can be obtained through least-squares inverse calculation. Based on the current vehicle speed v and steering angle δ, its index in a two-dimensional lookup table (LUT) can be calculated. This can be expressed by the following formula.

[0093] idx_v=round(v / 5)

[0094] idx_δ=round(δ / 2)

[0095] The vehicle speed step size is 5 km / h, and the steering angle step size is 2°. The corresponding historical scaling factor can be read from the LUT, and the target value can be calculated using an exponentially weighted moving average, expressed by the following formula.

[0096]

[0097] in, Used for forgetting factors It can be adaptively set according to the vehicle's dynamic state. Write the corresponding LUT entry to complete the replacement.

[0098] In this embodiment, the above method allows for continuous and smooth online updates of the wheel speed proportional factor when the GNSS signal is normal, avoiding sudden changes in LUT data due to single calibration errors. The weighted average method enables the LUT to maintain historical experience under stable operating conditions and respond quickly to changes under dynamic operating conditions, balancing stability and adaptability. The above method allows the LUT to gradually adapt to long-term drift factors such as tire wear, tire pressure changes, and load distribution, maintaining long-term calibration accuracy.

[0099] As an optional implementation, in response to residual information being greater than or equal to a residual information threshold, the process returns to start with the following steps: obtaining residual information.

[0100] Optionally, if the position residual at the current moment is greater than 0.05 / m, or the phase residual is greater than 0.01 / cycle, KY determines that the current GNSS observation quality does not meet the calibration requirements; immediately stop the scaling factor optimization calculation within the current time window, and do not update the LUT entries; if the pulse and RTK displacement data currently being accumulated for least squares calculation (for example, 20 points have been collected, but the preset requirements are not met), then clear the accumulated data to avoid abnormal data contaminating subsequent steps, and start a re-detection cycle, waiting for the next GNSS quality recovery before jumping back to the process start point, re-execute the step of reading the position covariance and carrier phase residual output by the RTK positioning engine, and continue to monitor whether the residual information falls back below the residual information threshold.

[0101] In this embodiment of the application, the residual information of the positioning system in the vehicle can be obtained by the above method. The residual information is used to represent the degree of difference between the state observation value collected by the positioning system of the vehicle and the actual state value of the vehicle. In response to the residual information being less than the residual information threshold, the difference information between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle is determined. The difference information is used to characterize the degree of drift of the vehicle in the longitudinal direction relative to the wheel ends. Based on the difference information and the current driving state of the vehicle, the target driving state of the vehicle is determined. The target driving state is used to make the degree of drift of the vehicle less than the degree of drift of the vehicle in the current driving state. In other words, in this embodiment, when the residual information of the positioning system is less than a preset residual information threshold, the difference between the longitudinal displacement information and the wheel end rotation information is determined. Based on this difference information and the vehicle's current driving state, the target driving state of the vehicle is determined. This method allows for timely correction of the vehicle's current driving state based on the difference information, enabling the target driving state to dynamically offset the effects of sensor errors and dynamic changes in the vehicle's tire rolling radius in related technologies. This overcomes the obstacle of large errors in determining the vehicle's driving state in related technologies, which fails to meet lane-level positioning requirements. Therefore, it solves the technical problem of low accuracy in determining the vehicle's driving state and achieves the technical effect of improving the accuracy of vehicle driving state determination.

[0102] The technical solutions of the embodiments of this application will be illustrated below with reference to preferred embodiments.

[0103] Currently, high-precision vehicle positioning technology is developing towards multi-sensor fusion, with the combination of GNSS / RTK and Inertial Navigation System (INS) becoming the mainstream solution for L2+ and above autonomous driving. In open environments, RTK can provide centimeter-level absolute positioning accuracy; however, in environments where satellite signals are denied, such as tunnels, underground parking garages, and urban canyons, vehicles need to rely on dead reckoning (DR) to maintain position updates. In these situations, the fusion accuracy of wheel speed sensors (WSS) and inertial measurement units (IMUs) directly determines the availability and safety of vehicle positioning.

[0104] Currently, longitudinal error drift in dead reckoning systems under GNSS denied conditions is a common technical bottleneck in the industry. Due to the zero bias of the IMU accelerometer, the scale factor error of the wheel speed sensor (which can be represented by scale factor error), and the dynamic changes in the tire rolling radius (which are caused by factors such as tire load, tire pressure, tire temperature, and tire wear), the longitudinal error drift rate of a pure DR system is usually 2-3%, meaning that the cumulative error can reach 20-30 meters per kilometer, which cannot meet the requirements for lane-level positioning (error must be <1 meter / km).

[0105] Optionally, to address the proportional factor error of wheel speed sensors, relevant technologies mainly employ static / offline calibration methods and online adaptive estimation methods. Static / offline calibration methods involve pre-determining the nominal rolling radius of the tire as a fixed parameter through bench testing or straight road testing. For example, the nominal radius of passenger car tires is typically in the range of 0.3-0.35 meters, and the wheel speed pulse count is converted into linear velocity accordingly. However, these methods do not consider radius changes under dynamic operating conditions, resulting in significant errors during cornering, acceleration / deceleration, and load changes, making it difficult to meet high-precision DR requirements.

[0106] Optionally, related technologies also use the velocity vector output by the GNSS module as a reference true value to directly solve for the tire radius using v=ω·r. Here, v represents the velocity vector, ω represents the angular velocity of the tire rotation, and r represents the tire radius. However, the GNSS velocity calculation in the above method is based on Doppler frequency shift or position difference, with an accuracy of only 0.1-0.2 m / s (1σ). At low vehicle speeds (e.g., less than 10 km / h) or during steering, the velocity error is comparable to the wheel speed error, resulting in insufficient calibration signal-to-noise ratio and a radius estimation variance >5 mm.

[0107] Optionally, this application proposes a method for determining the driving state of a vehicle to address the rapid divergence of longitudinal errors in dead reckoning (DR) systems under GNSS denial scenarios such as tunnels, aiming to solve several technical problems existing in related technologies.

[0108] Optionally, to address the issues of high computational complexity and insufficient real-time performance in wheel diameter calibration algorithms: by constructing a dynamic lookup table (LUT) to replace the cascaded Kalman filter iterative calculation, the processing time per cycle is reduced from more than 5ms to less than 0.1ms, meeting the 100Hz high real-time performance requirements of autonomous driving systems.

[0109] Optionally, to address the issue of insufficient utilization of RTK centimeter-level positioning residuals and limited calibration accuracy in related technologies: introduce RTK residuals as an observation quality assessment index, trigger calibration only in high-quality segments with residuals <0.05m, avoid GNSS anomalous data contamination, and significantly improve calibration signal-to-noise ratio and parameter estimation accuracy.

[0110] Optionally, to address the issue of missing mapping between the tire's equivalent rolling radius and the nonlinear relationship under operating conditions, a two-dimensional dynamic lookup table ScaleFactor(v, δ) is established in the joint parameter space of vehicle speed and steering angle to achieve accurate adaptation of the scaling factor under varying operating conditions (steering, acceleration and deceleration) and eliminate longitudinal scaling error caused by lateral slip.

[0111] Optionally, this application provides a method for determining the driving state of a vehicle, which utilizes RTK positioning residuals to inversely calculate the wheel speed scaling factor and uses a dynamic lookup table (LUT) to maintain high-precision DR in GNSS denied environments. The technical solution consists of a residual calculation module, a scaling factor inverse calculation module, a dynamic LUT construction and management module, and a DR enhancement execution module, and adopts an offline table building and online table lookup architecture to reduce the computational load.

[0112] Optionally, the aforementioned RTK residual calculation module differs from related technologies only in that it uses GNSS velocity vectors. This module directly reads the internal state of the RTK positioning engine and calculates the position residual (the difference between the RTK-solved position and the predicted position) and the carrier phase residual. When both residuals are less than preset thresholds (position residual < 0.05m, phase residual < 0.01 cycles), it is determined to be a high-quality GNSS segment. Compared to traditional schemes that rely solely on fixed GNSS solution state bits, this judgment logic can filter out approximately 15% more anomalous observations, improving the purity of calibration data.

[0113] Optionally, the aforementioned scale factor inverse calculation module does not directly calculate the tire rolling radius within the high-quality segment, but instead solves for the scale factor (unit: m / pulse) of the wheel speed pulse versus the actual displacement. An optimization objective function can be established. This objective function can be expressed by the following formula.

[0114]

[0115] Where d_RTK represents the longitudinal displacement increment of the vehicle during travel measured by RTK, pulse_i represents the wheel speed pulse count, and M(k) represents the total number of pulse sampling points within a high-quality GNSS segment. This method avoids the traditional... The formula, which suffers from the coupling of dual estimation errors of tire radius and speed, is simplified to single-parameter identification, improving the convergence speed by 40%.

[0116] Optionally, a two-dimensional lookup table for vehicle speed v and steering angle δ can be established through the aforementioned dynamic LUT construction and management module, and the scaling factor can be stored discretely according to operating conditions. The exponentially weighted moving average (EWMA) update strategy can be expressed by the following formula.

[0117]

[0118] Where λ can be used to represent the forgetting factor, λ∈[0.9, 0.999], and λ can be adaptively adjusted according to the vehicle dynamics: λ=0.999 for steady-state cruising (slow update), and λ=0.9 for rapid acceleration / steering (fast convergence). The above... It can be used to represent historical scaling factor values, as mentioned above. It can be used to represent the current scaling factor value.

[0119] Optionally, after the GNSS / RTK signal is lost and the vehicle enters the tunnel, the aforementioned DR enhancement execution module can query the LUT in real time based on the current vehicle speed and steering angle to obtain the corresponding ScaleFactor, correct the original wheel speed pulse, and the corrected speed can be expressed by the following formula.

[0120]

[0121] in, It can represent the corrected speed. It can represent the pulse increment output by the wheel speed sensor during the i-th sampling period. It can represent the wheel speed ratio factor obtained from a two-dimensional dynamic lookup table under the current vehicle speed and turning angle conditions. It can represent the sampling period of the wheel speed pulse.

[0122] Optionally, the corrected velocity is used as the Kalman filter observation vector input to constrain IMU integral drift. Unlike existing technologies that only correct the odometer scale coefficients, this embodiment uses direct velocity domain correction, bypassing the physical modeling of the tire radius, reducing the 30s inward error in the tunnel scene from 2-3% to 0.5% (measured 30s error <0.2m).

[0123] Optionally, the embodiments of this application solve the problems of rapid longitudinal error divergence, high computational complexity, and poor adaptability to working conditions in the tunnel GNSS rejection scenario by using RTK residual feedback, scaling factor calculation, and dynamic LUT lookup table.

[0124] Optionally, traditional DR has a longitudinal error of 12.7m (drift rate of 2.8%) after 30 seconds of GNSS loss, while the embodiment of this application has a longitudinal error of only 0.19m (0.42% drift), and the lateral error is reduced from 0.8m to 0.12m, which meets the L3 level safety threshold of 30-second error <0.3m. The RTK reacquisition time is shortened from 5-8 seconds to less than 1 second.

[0125] Optionally, the cascaded Kalman filter takes 4.8ms per cycle and requires a floating-point unit, while the LUT lookup in this embodiment only takes 0.08ms, with a CPU utilization rate of <5%, and can be deployed on a fixed-point MCU platform costing less than 150 yuan, breaking through the limitations of high-end domain controllers.

[0126] Optionally, traditional methods rely solely on GNSS to fix the solution flags, resulting in an effective calibration segment ratio of 52% and a convergence time of 8.2 seconds. The embodiments of this application employ a position / phase dual residual threshold determination (<0.05m / <0.01 epochs), increasing the effective segment ratio to 89%, shortening the convergence time to 4.5 seconds, and reducing variance by 62%.

[0127] Optionally, the traditional one-dimensional radius model has an error of 9.3m in 30 seconds on a ramp (lateral 0.28g). In the embodiment of this application, the speed-steering angle two-dimensional LUT reduces the error to 2.7m, significantly enhancing its usability in complex conditions such as lane changes and roundabouts.

[0128] The methods of the embodiments of this application will be further illustrated below.

[0129] Figure 2 This is a schematic diagram illustrating an application scenario of a method for determining the driving state of a vehicle according to an embodiment of this application. For example... Figure 2As shown, the above scenario may include terminal device 10, network 20, and vehicle 30. Terminal device 10 can be used to obtain residual information determination instructions from a user (e.g., a driver) regarding the vehicle. The terminal device can be a mobile phone, laptop, or personal computer, or a graphical user interface (e.g., a vehicle screen) within the vehicle. The residual information determination instructions can be sent to vehicle 30 via network 20. At this point, vehicle 30 needs to execute steps S202 to S206 to control the vehicle. Optionally, the vehicle can also automatically trigger the confirmation of residual information based on its driving state. For example, when the vehicle's driving state meets preset conditions, vehicle 30 can execute the following steps: Step S202, obtain residual information from the vehicle's positioning system; Step S204, in response to the residual information being less than a residual information threshold, determine the difference between the vehicle's longitudinal displacement information during driving and the rotation information at the wheel ends; Step S206, based on the difference information and the vehicle's current driving state, determine the vehicle's target driving state.

[0130] In this embodiment, through steps S202 to S206, the difference between longitudinal displacement information and wheel-end rotation information is determined when the residual information of the positioning system is less than a preset residual information threshold. Based on this difference information and the vehicle's current driving state, the target driving state of the vehicle is determined. This method allows for timely correction of the vehicle's current driving state based on the difference information, enabling the target driving state to dynamically offset the effects of sensor errors and dynamic changes in the vehicle's tire rolling radius in related technologies. This overcomes the obstacle of large errors in determining the vehicle's driving state in related technologies, which fails to meet lane-level positioning requirements. Thus, the technical problem of low accuracy in determining the vehicle's driving state is solved, achieving the technical effect of improving the accuracy of vehicle driving state determination.

[0131] According to an embodiment of this application, a method for determining the driving state of a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0132] Figure 3 This is a flowchart of a vehicle dead reckoning enhancement method according to an embodiment of this application, such as... Figure 3 As shown, the vehicle dead reckoning enhancement method includes the following process.

[0133] Step S302: The vehicle is in operation, and data from multiple sensor sources is collected.

[0134] Optionally, during normal vehicle operation, the on-board controller synchronously collects the following sensor data at fixed intervals: pulse counts output by the wheel speed sensor; front wheel steering angle output by the steering wheel angle sensor; vehicle speed estimate; longitudinal acceleration and angular velocity output by the IMU; and RTK positioning status, east / north coordinates, position covariance, and carrier phase residual RMS output by the GNSS receiver.

[0135] Optionally, the above method can achieve synchronous acquisition of multi-source heterogeneous data, ensuring that parameters such as wheel speed pulse, RTK displacement, and steering angle are aligned on the time base in subsequent steps, providing a consistent time series input for scaling factor calibration and DR enhancement.

[0136] Step S304, RTK residual calculation.

[0137] Optionally, the controller extracts the eastward and northward positioning covariances (σ_E, σ_N) from the NMEA GPGST or UBX-NAV-PVT messages output by the RTK positioning engine, and calculates the position residuals. The carrier phase residual RMS is obtained from the phaseResidual field of the UBX-RXM-RAWX message, and the RTK fixed solution status bits are read simultaneously.

[0138] Optionally, the above method can be used to obtain the observation quality assessment index inside the positioning system, which can be used as the basis for subsequent judgment on whether GNSS data can be used for calibration, avoiding reliance on external references or manually set thresholds.

[0139] Step S306: Determine whether the time period for obtaining residual information is a high-quality GNSS segment.

[0140] Optionally, if so, step S310 is executed; otherwise, step S308 is executed.

[0141] Optionally, the above method can be used to trigger calibration when the positioning error is small, the observation is stable, and the operating conditions are controllable, thereby eliminating interference scenarios such as multipath interference, signal interference, low speed, and violent steering, and improving the reliability and representativeness of the calibration data.

[0142] Step S308: Non-high-quality segments skip the calibration process.

[0143] Optionally, when the RTK residual calculation module determines that the current position residual is ≥0.05m or the phase residual RMS is ≥0.01 cycles, or the RTK fixed solution state is invalid, the vehicle is in a slip state, or the absolute value of longitudinal acceleration is ≥0.2g, the system will not start the proportional factor reverse calculation, will not update the dynamic LUT table entries, and will clear the accumulated wheel speed pulses and RTK displacement data in the current window; the system will continue to collect sensor data, but will only record the current state as "calibration paused", waiting for the next window that meets the high-quality conditions.

[0144] Optionally, the above method can avoid using data with large noise or deviation for calibration when the GNSS observation quality is unreliable or the vehicle operating conditions are abnormal, prevent erroneous parameters from being written into the LUT, and ensure the accuracy and long-term consistency of the calibration results; at the same time, it reduces invalid calculations, lowers controller resource consumption, and improves system operating efficiency.

[0145] Step S310: Reverse calculation of the scaling factor.

[0146] Optionally, within high-quality GNSS clips, the wheel velocity pulse increment is accumulated at a sampling rate of 100Hz and combined with the RTK displacement increment to construct an optimization objective function. The recursive least squares (RLS) algorithm is used to solve the problem online, initializing SF and the forgetting factor λ; once the covariance converges, the current optimal scaling factor is output.

[0147] Optionally, the conversion coefficient between wheel speed pulses and actual displacement can be directly identified using the above method, avoiding the error propagation of the coupled estimation of tire radius and angular velocity.

[0148] Step S312, Dynamic LUT Update.

[0149] Optionally, the LUT index is determined based on the current vehicle speed and steering angle, a weighted update is performed, and after the update, the new value is written to the LUT entry in the NOR Flash.

[0150] Optionally, the above method can be used to achieve gradual updates of the scaling factor under different operating conditions, avoid LUT mutations caused by fluctuations in a single measurement, and improve the stability and adaptability of parameters during long-term operation.

[0151] Step S314: Is the GNSS / RTK signal lost?

[0152] Optionally, the signal loss can be determined by reading whether the gnssValidFlag or rtkStatus output by the GNSS module is "NO_FIX" or "FLOAT". If there is no effective positioning for 3 consecutive cycles (0.3s), it is determined that the GNSS / RTK signal is lost and the user has entered a denied environment such as a tunnel or underground parking garage.

[0153] Optionally, the above method can quickly identify positioning failure states, promptly switch DR enhancement mode, and ensure that the system completes parameter switching before signal interruption, thus avoiding positioning gaps.

[0154] Step S316, DR enhancement execution.

[0155] Optionally, the current wheel speed pulse increment, vehicle speed estimate, and steering angle can be obtained; the corresponding grid points can be obtained by looking up the table, and bilinear interpolation can be used to calculate the corrected speed.

[0156] Optionally, the above method can be used to correct wheel speed input using a calibrated scaling factor in the absence of GNSS, thereby improving the speed observation accuracy of the DR system.

[0157] Step S318: Correct wheel speed output, generate correction speed, input EKF to constrain IMU drift, and output enhanced DR positioning results.

[0158] Optionally, the EKF fusion module outputs updated vehicle position, speed, and attitude as the final DR positioning result for use by the perception, planning, and control modules; it continuously records DR errors (e.g., the difference between the positioning and the subsequent RTK recovery) to evaluate the effectiveness of the calibration; if the RTK signal is recovered, the system automatically switches to RTK-dominated mode and re-enters the calibration process.

[0159] Optionally, the above method can maintain vehicle positioning accuracy during GNSS denial, and the system can seamlessly switch after the signal is restored without relying on manual intervention, thus achieving the continuity and repeatability of the positioning function.

[0160] Figure 4 This is a vehicle dead reckoning enhancement module according to an embodiment of this application, such as... Figure 4 As shown, the vehicle dead reckoning enhancement module 400 includes an RTK residual calculation module 402, a scale factor reverse calculation module 404, a dynamic LUT construction and management module 406, and a DR enhancement execution module 408.

[0161] The RTK residual calculation module 402 is used to read the position covariance (σ_E, σ_N) and carrier phase residual RMS from the GNSS / RTK positioning engine and calculate the position residual. It then determines whether the current observation meets the high-quality criteria of position residual < 0.05m and phase residual RMS < 0.01 cycles, and outputs a flag signal indicating whether calibration is allowed. In this embodiment, it can be connected to a GNSS receiver via a UART / CAN-FD interface. The position residual in the RTK (which can be represented by ΔPos_residual) can be read through this interface, where ΔPos_residual can be determined by the following formula.

[0162] ΔPos_residual=√(σ_E²+σ_N²)

[0163] Wherein, σ_E can represent the eastward positioning covariance (unit: meters) and σ_N can represent the northward positioning covariance (unit: meters). The above σ_E and σ_N can be obtained by parsing the NMEA GPGST message in the receiver.

[0164] The scale factor inverse calculation module 404 is used to acquire the pulse increment sequence and RTK displacement increment sequence output by the wheel speed sensor during the period when the RTK residual is determined to be of high quality. It constructs a least-squares optimization objective function, using the wheel speed scale factor as the sole variable, and solves for the optimal scale factor using the recursive least squares (RLS) algorithm to output the calibration result under the above working conditions. For example, in response to the position residual <0.05m and phase residual <0.01 cycles, the time period for acquiring the residual information can be determined as a high-quality GNSS segment. Within this high-quality GNSS segment, the scale factor (unit: m / pulse) of the wheel speed pulse versus actual displacement can be solved by establishing an optimization objective function. The optimization objective function can be expressed by the following formula.

[0165]

[0166] Wherein, d_RTK can represent the longitudinal displacement increment of the vehicle during driving as measured by RTK, pulse_i can represent the wheel speed pulse count, and M(k) can represent the total number of pulse sampling points in a high-quality GNSS segment.

[0167] The dynamic LUT construction and management module 406 determines the corresponding index in the two-dimensional lookup table (LUT) based on the current vehicle speed and steering angle. It employs an Exponentially Weighted Moving Average (EWMA) strategy for weighted fusion and updates the LUT entries. Simultaneously, it manages NOR Flash storage writes, controlling the update frequency to ≥10 seconds and enabling wear leveling to ensure long-term storage reliability. Within high-quality RTK segments, the wheel speed scaling factor under the current operating condition can be obtained through least-squares inverse calculation. Based on the current vehicle speed v and steering angle δ, its index in the two-dimensional lookup table (LUT) is calculated. This can be expressed by the following formula.

[0168] idx_v=round(v / 5)

[0169] idx_δ=round(δ / 2)

[0170] The vehicle speed step size is 5 km / h, and the steering angle step size is 2°. The corresponding historical scaling factor can be read from the LUT, and the target value can be calculated using an exponentially weighted moving average, expressed by the following formula.

[0171]

[0172] in, Used for forgetting factors It can be adaptively set according to the vehicle's dynamic state. Write the corresponding LUT entry to complete the replacement.

[0173] The DR enhancement execution module 408 is used to query the LUT to obtain the corresponding wheel speed scaling factor based on the real-time vehicle speed and front wheel angle after the GNSS / RTK signal is lost. It then calculates the corrected speed by combining the wheel speed pulse increment and inputs the speed as the observation value into the extended Kalman filter of the dead reckoning system to constrain the IMU integral drift and output the enhanced vehicle position and speed estimation results.

[0174] For example, when the GNSS / RTK signal is lost and enters the tunnel, the DR enhancement execution module 408 queries the LUT in real time based on the current vehicle speed and steering angle to obtain the corresponding ScaleFactor, and can correct the original wheel speed pulse using the following formula.

[0175]

[0176] The corrected velocity is used as the Kalman filter observation vector input to constrain IMU integral drift. Unlike existing technologies that only correct the odometer scale coefficients, this scheme uses direct velocity domain correction, bypassing the physical modeling of the tire radius, reducing the 30s inward error in the tunnel scene from 2-3% to 0.5% (measured 30s error <0.2m).

[0177] In this embodiment of the application, the residual information of the positioning system in the vehicle can be obtained by the above method. The residual information is used to represent the degree of difference between the state observation value collected by the positioning system of the vehicle and the actual state value of the vehicle. In response to the residual information being less than the residual information threshold, the difference information between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle is determined. The difference information is used to characterize the degree of drift of the vehicle in the longitudinal direction relative to the wheel ends. Based on the difference information and the current driving state of the vehicle, the target driving state of the vehicle is determined. The target driving state is used to make the degree of drift of the vehicle less than the degree of drift of the vehicle in the current driving state. In other words, in this embodiment, when the residual information of the positioning system is less than a preset residual information threshold, the difference between the longitudinal displacement information and the wheel end rotation information is determined. Based on this difference information and the vehicle's current driving state, the target driving state of the vehicle is determined. This method allows for timely correction of the vehicle's current driving state based on the difference information, enabling the target driving state to dynamically offset the effects of sensor errors and dynamic changes in the vehicle's tire rolling radius in related technologies. This overcomes the obstacle of large errors in determining the vehicle's driving state in related technologies, which fails to meet lane-level positioning requirements. Therefore, it solves the technical problem of low accuracy in determining the vehicle's driving state and achieves the technical effect of improving the accuracy of vehicle driving state determination.

[0178] Figure 5This is a schematic diagram of a vehicle driving state determination device according to an embodiment of this application. The vehicle driving state determination device 50 may include: an acquisition unit 502, a first determination unit 504, and a second determination unit 506.

[0179] The acquisition unit 502 is used to acquire residual information of the positioning system in the vehicle, wherein the residual information is used to represent the degree of difference between the state observation values ​​collected by the positioning system for the vehicle and the actual state values ​​of the vehicle; the first determination unit 504 is used to determine the difference information between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle in response to the residual information being less than the residual information threshold, wherein the difference information is used to characterize the degree of drift of the vehicle in the longitudinal direction relative to the wheel ends; the second determination unit 506 is used to determine the target driving state of the vehicle based on the difference information and the current driving state of the vehicle, wherein the target driving state is used to make the degree of drift of the vehicle less than the degree of drift of the vehicle in the current driving state.

[0180] Optionally, the second determining unit 506 may further include a first determining module, which may be used to determine the target difference information that has a mapping relationship with the current driving state of the acquisition cycle from the mapping table corresponding to the acquisition cycle difference information in response to the acquisition cycle positioning system being in a failure state.

[0181] Optionally, the second determining unit 506 may further include a second determining module, which can be used to determine the target driving state of the acquisition period based on the target difference information of the acquisition period and the current driving state of the acquisition period.

[0182] Optionally, the first determining module may further include a first determining sub-module, which is used to determine, in response to the acquisition cycle positioning system being in an acquisition cycle failure state, a wheel speed ratio factor that has an acquisition cycle mapping relationship with the current vehicle speed and the current steering angle of the acquisition cycle from the acquisition cycle mapping table.

[0183] Optionally, the first determining submodule may further include a second determining submodule, which can be used to determine the product between the wheel speed scaling factor with the acquisition cycle mapping relationship and the current wheel speed pulse number in the acquisition cycle.

[0184] Optionally, the first determining submodule may further include a third determining submodule, which can be used to determine the target speed of the acquisition period based on the time interval between the product of the acquisition periods and the current driving state of the acquisition period.

[0185] Optionally, the vehicle driving status determination device 50 may further include a fourth determination submodule, which is used to determine the difference between the predicted position collected by the acquisition cycle positioning system in the normal state of the acquisition cycle and the actual position of the vehicle in the acquisition cycle as the acquisition cycle position residual information, wherein the acquisition cycle position residual information is used to represent the degree of difference between the predicted position and the actual position in the acquisition cycle.

[0186] Optionally, the vehicle driving state determination device 50 may further include a fifth determination submodule, which is used to determine the difference between the phase observation value collected by the positioning system in the normal state of the acquisition cycle and the actual phase value of the vehicle in the acquisition cycle as the acquisition cycle phase residual information, wherein the acquisition cycle phase residual information is used to represent the degree of difference between the phase observation value and the actual phase value in the acquisition cycle.

[0187] Optionally, the vehicle driving state determination device 50 may further include a sixth determination submodule, which is used to determine that the acquisition period residual information is less than the acquisition period residual information threshold in response to the acquisition period position residual information being less than the acquisition period position residual information threshold and the acquisition period phase residual information being less than the acquisition period phase residual information threshold.

[0188] Optionally, the vehicle driving status determination device 50 may further include a first acquisition module, which is used to acquire a first number of sampling points of the wheel speed pulse number of the acquisition cycle within a time window.

[0189] Optionally, the vehicle driving state determination device 50 may further include a seventh determination submodule, which is used to determine the theoretical displacement of the vehicle in the acquisition cycle within the acquisition cycle time window, based on the first number of acquisition cycle wheel speed pulses and wheel speed ratio estimates in the acquisition cycle, and to determine the displacement difference between the longitudinal displacement information of the acquisition cycle and the theoretical displacement of the acquisition cycle within the acquisition cycle time window.

[0190] Optionally, the vehicle driving state determination device 50 may further include a second acquisition submodule, which is used to acquire a second quantity of longitudinal displacement information of the acquisition cycle within the acquisition cycle time window, and to determine the sum of squared displacement errors of the second quantity of acquisition cycle displacement differences.

[0191] Optionally, the vehicle driving state determination device 50 may further include an eighth determination submodule, which is used to determine the wheel speed ratio factor of the acquisition period based on the sum of squared displacement errors of the acquisition period.

[0192] Optionally, the vehicle driving status determination device 50 may further include a first establishment module, wherein the first establishment module may be used to establish a mapping table between the wheel speed ratio factor of the acquisition cycle and the current speed and steering angle of the vehicle in the acquisition cycle in response to the positioning system being in a normal state during the acquisition cycle.

[0193] Optionally, the vehicle driving state determination device 50 may further include a ninth determination submodule, which can be used to determine the wheel speed ratio factor of the acquisition cycle in the acquisition cycle mapping table as the previous wheel speed ratio factor.

[0194] Optionally, the vehicle driving state determination device 50 may further include a first weighting module, wherein the first weighting module is used to perform a weighted average of the wheel speed ratio factor of the acquisition period and the wheel speed ratio factor of the previous acquisition period to obtain the target wheel speed ratio factor of the previous acquisition period in the acquisition period mapping table to be replaced.

[0195] Optionally, the vehicle driving status determination device 50 may further include a first replacement module, which can be used to replace the wheel speed ratio factor of the previous acquisition cycle with the target wheel speed ratio factor of the acquisition cycle to obtain the acquisition cycle mapping table after replacement.

[0196] Optionally, the vehicle driving status determination device 50 may further include a first return submodule, which may be used to return to the following steps in response to the acquisition period residual information being greater than or equal to the acquisition period residual information threshold: acquiring acquisition period residual information.

[0197] In this embodiment, the residual information of the vehicle's positioning system is acquired by the acquisition unit 502, where the residual information represents the degree of difference between the state observations collected by the positioning system and the actual state values ​​of the vehicle. The first determination unit 504, in response to the residual information being less than a residual information threshold, determines the difference between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends, where the difference information characterizes the degree of longitudinal drift of the vehicle relative to the wheel ends. The second determination unit 506, based on the difference information and the vehicle's current driving state, determines the vehicle's target driving state, where the target driving state is such that the degree of drift of the vehicle is less than the degree of drift of the vehicle in its current driving state. This solves the technical problem of low accuracy in determining the vehicle's driving state and achieves the technical effect of improving the accuracy of vehicle driving state determination.

[0198] Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application, such as... Figure 6As shown, the electronic device 60 includes a memory 602 and a processor 604. The memory 602 is used to store computer programs, and the processor 604 is used to execute the programs stored in the memory 602 to implement the methods in the various embodiments of this application.

[0199] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0200] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0201] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0202] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0203] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0204] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0205] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of acquisition cycle units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces, indirect couplings, or communication connections between units or modules, and may be electrical or other forms.

[0206] The acquisition cycle, described as a separate component, may or may not be physically separate. Similarly, the components shown as units may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0207] Furthermore, the functional units in the various embodiments of this application 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 as a software functional unit.

[0208] If the data acquisition cycle integration unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the data acquisition cycle method of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0209] The above collection cycle is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining the driving state of a vehicle, characterized in that, include: Obtain residual information of the positioning system in the vehicle, wherein the residual information is used to represent the degree of difference between the state observation values ​​of the vehicle collected by the positioning system and the actual state values ​​of the vehicle; In response to the residual information being less than a residual information threshold, the difference information between the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel ends in the vehicle is determined, wherein the difference information is used to characterize the degree of longitudinal drift of the vehicle relative to the wheel ends; Based on the difference information and the current driving state of the vehicle, a target driving state of the vehicle is determined, wherein the target driving state is used to make the drift degree of the vehicle less than the drift degree of the vehicle in the current driving state.

2. The method according to claim 1, characterized in that, Determining the target driving state of the vehicle based on the difference information and the current driving state of the vehicle includes: In response to the positioning system being in a malfunctioning state, target difference information that has a mapping relationship with the current driving state is determined from the mapping table corresponding to the difference information; The target driving state is determined based on the target difference information and the current driving state.

3. The method according to claim 2, characterized in that, The current driving state includes the vehicle's current speed and current steering angle. The difference information is a wheel speed ratio factor. In response to the positioning system being in a malfunctioning state, the target difference information with a mapping relationship to the current driving state is determined from the mapping table corresponding to the difference information, including: In response to the positioning system being in the failure state, a wheel speed ratio factor that has the mapping relationship with the current vehicle speed and the current steering angle is determined from the mapping table.

4. The method according to claim 3, characterized in that, The current driving state includes the current wheel speed pulse count, and the target driving state includes a target speed, which represents the vehicle's speed in the longitudinal direction. Determining the target driving state based on the target difference information and the current driving state includes: Determine the product between the wheel speed scaling factor with the aforementioned mapping relationship and the current wheel speed pulse count; The target speed is determined based on the product and the time interval between collecting the current driving state.

5. The method according to claim 1, characterized in that, The residual information includes position residual information and phase residual information. Obtaining the residual information of the positioning system in a normal state includes: The difference between the predicted position collected by the positioning system in the normal state and the actual position of the vehicle is determined as the position residual information, wherein the position residual information is used to represent the degree of difference between the predicted position and the actual position; The difference between the phase observation value collected by the positioning system in the normal state and the actual phase value of the vehicle is determined as the phase residual information, wherein the phase residual information is used to represent the degree of difference between the phase observation value and the actual phase value; And / or, the method further includes: In response to the position residual information being less than the position residual information threshold and the phase residual information being less than the phase residual information threshold, it is determined that the residual information is less than the residual information threshold.

6. The method according to claim 1, characterized in that, The difference information is the wheel speed ratio factor, the rotation information is the wheel speed pulse number at the wheel end, and the difference information between determining the longitudinal displacement information of the vehicle during driving and the rotation information of the wheel end in the vehicle includes: Within the time window, obtain the first number of sampling points for the wheel speed pulse count; Within the time window, based on the first number of wheel speed pulses and the wheel speed ratio estimate, the theoretical displacement of the vehicle is determined, and the displacement difference between the longitudinal displacement information and the theoretical displacement within the time window is determined. Within the time window, a second quantity of the longitudinal displacement information is acquired, and the sum of squared displacement errors of the second quantity of displacement differences is determined; The wheel speed scaling factor is determined based on the sum of squared displacement errors.

7. The method according to claim 6, characterized in that, The method further includes: In response to the positioning system being in a normal state, a mapping table is established between the wheel speed ratio factor and the vehicle's current speed and the vehicle's current steering angle; The wheel speed ratio factor in the mapping table is determined as the previous wheel speed ratio factor; And / or, The method further includes: The wheel speed ratio factor and the previous wheel speed ratio factor are weighted and averaged to obtain the target wheel speed ratio factor to replace the previous wheel speed ratio factor in the mapping table. The target wheel speed ratio factor is used to replace the previous wheel speed ratio factor to obtain the mapping table after replacement.

8. The method according to any one of claims 1 to 7, characterized in that, In response to the residual information being greater than or equal to the residual information threshold, the process returns to start execution from the following steps: Obtain the residual information.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

10. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

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