Vehicle longitudinal speed estimation method based on multi-sensor fusion
By employing a multi-sensor fusion method, utilizing data from GPS, IMU, and wheel speed sensors, and combining it with the Kalman filter algorithm, the accuracy and real-time performance issues of vehicle speed estimation under complex operating conditions were resolved, achieving efficient and low-cost vehicle speed estimation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-16
- Publication Date
- 2026-03-27
AI Technical Summary
Existing vehicle speed estimation methods have poor adaptability under various operating conditions, high sensor costs, and insufficient system reliability. In particular, they suffer from low vehicle speed estimation accuracy and poor real-time performance in high dynamic, low-adhesion, or signal-limited scenarios.
A multi-sensor fusion method is adopted, including GPS speed data, inertial measurement unit (IMU) data, wheel speed sensor data, and vehicle speed data output by the drive braking system controller. The weights are dynamically adjusted through Kalman filtering or weighted fusion algorithm to estimate the longitudinal speed of the vehicle.
It improves the accuracy and adaptability of vehicle speed estimation, reduces costs, and ensures the reliability and real-time performance of the system under complex operating conditions.
Smart Images
Figure CN121734419A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle longitudinal speed estimation, and in particular to a method for estimating vehicle longitudinal speed based on multi-sensor fusion. Background Technology
[0002] Accurate estimation of vehicle longitudinal speed is crucial for systems such as vehicle stability control, anti-lock braking systems, traction control, and autonomous driving. Currently, commonly used speed estimation methods include wheel speed sensor-based calculations, estimation algorithms based on vehicle kinematic models, and GPS-based speed measurement methods. However, wheel speed sensors fail when wheels slip or lock up; vehicle kinematic models rely on calibration parameters, considering large decelerations during emergency braking, analyzing different vehicle-road adhesion conditions, and determining deceleration values based on experimental data. The main drawback of this method is its poor adaptability; it cannot actively adjust deceleration values according to changes in road adhesion conditions. GPS speed measurement uses a GPS terminal to receive electromagnetic wave signals emitted by satellites to determine the vehicle's real-time position and speed. Currently, the positioning accuracy and update frequency of civilian GPS are relatively low, with a positioning accuracy typically around 10 meters and a frequency of 1–10 Hz. Therefore, directly using GPS for speed measurement is insufficient to meet the accuracy requirements for vehicle speed measurement. To further improve GPS speed measurement accuracy, differential GPS (DGPS) technology has been conditionally adopted by autonomous vehicles. The specific method involves pre-installing a GPS receiver at a precisely located base station for observation, calculating and transmitting real-time corrections for the distance from the base station to the satellite. The user receiver, while performing direct GPS positioning and speed measurement, also receives these corrections from the base station, thus improving positioning and speed measurement accuracy. Although DGPS technology offers high speed measurement accuracy, satellite signal transmission is susceptible to weather and obstacles, and its effective range is limited by the distribution of ground base stations. Summary of the Invention
[0003] The purpose of this invention is to address the shortcomings of existing vehicle speed estimation methods in terms of adaptability to multiple operating conditions, sensor cost, and system reliability, especially the problems of low vehicle speed estimation accuracy and poor real-time performance in high dynamic, low adhesion, or signal-constrained scenarios, and to provide a vehicle longitudinal speed estimation method based on multi-sensor fusion.
[0004] The above-mentioned objective of this application is achieved through the following technical solution: S1: Acquire multi-source sensor data of the vehicle, including GPS speed data, inertial measurement unit (IMU) data, wheel speed sensor data, and vehicle speed data output by the drive and braking system controller; S2: Calculate the vehicle's longitudinal acceleration based on the GPS speed data and the inertial measurement unit (IMU) data; S3: Calculate the longitudinal speed of the first vehicle and the longitudinal speed of the second vehicle based on the GPS speed data and the vehicle speed data output by the drive braking system controller. S4: Determine the longitudinal speed of the third vehicle based on the vehicle's longitudinal acceleration; S5: Use Kalman filtering or weighted fusion algorithm to fuse the longitudinal speeds of the first vehicle, the second vehicle, and the third vehicle, and output the final longitudinal speed of the vehicle.
[0005] Optionally, step S2 includes: Let the road slope angle be... :
[0006] in This represents the vehicle's forward acceleration calculated from data from the vehicle's inertial measurement unit (IMU). This represents acceleration data calculated from GPS velocity data; It represents the acceleration due to gravity.
[0007] Optionally, step S2 may further include: S21: Assuming the slope is zero, calculate the first slope estimation angle. :
[0008] S22: Estimate the first slope angle Substitute into the formula ,get ; S23: Calculation and The absolute value of the difference is compared with the calibration value; S24: If the absolute value is greater than the calibration value, let Repeat steps S22 to S23 until the absolute value is less than the calibration value, then proceed to step S25. S25: If the absolute value is less than or equal to the calibration value, let Determine the current slope angle ; Using the current slope angle Calculate the vehicle's longitudinal acceleration as follows:
[0009] in This indicates the longitudinal acceleration of the vehicle.
[0010] Optionally, step S3 includes: The longitudinal speed of the first vehicle was obtained using GPS speed data, as follows:
[0011] in, This represents the vehicle's original speed value calculated from GPS speed data without slope compensation. The longitudinal speed of the second vehicle is obtained by using data from the wheel speed sensors and the vehicle speed data output by the drive braking system controller, as follows:
[0012] in, Indicates wheel speed. This indicates the effective radius of the wheel.
[0013] Optionally, step S4 includes: Let the estimated longitudinal speed of the vehicle at the previous moment be... Then, based on the vehicle's acceleration, the longitudinal speed of the third vehicle is calculated as follows:
[0014] in Indicates a time interval.
[0015] Optionally, step S5 includes: The fusion algorithm employs a weighted fusion algorithm.
[0016] in Indicates the final longitudinal speed of the vehicle; , and Indicates the weighting coefficient; When GPS fails The weight is zero; when the wheels lock up or slip, The weight is zero; when hour, The value is 1.
[0017] An electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform a vehicle longitudinal speed estimation method based on multi-sensor fusion.
[0018] A computer-readable storage medium storing instructions that, when executed, perform a method for estimating the longitudinal speed of a vehicle based on multi-sensor fusion.
[0019] The beneficial effects of the technical solution provided in this application are: Adaptive fusion architecture of multi-source heterogeneous sensors: It creatively integrates GPS, IMU, wheel speed sensor and vehicle speed signal from inside the vehicle controller, and dynamically adjusts the weights through confidence, thus solving the problem of single sensor failure under specific working conditions.
[0020] Real-time slope compensation vehicle speed calculation method: Introducing IMU-based real-time slope estimation into the kinematic equations corrects the systematic errors of traditional models in slope scenarios, significantly improving the accuracy and adaptability of longitudinal vehicle speed estimation.
[0021] A low-cost engineering implementation path: Make full use of the vehicle's existing sensors without adding additional high-cost dedicated vehicle speed sensors, and achieve high-precision estimation through lightweight fusion algorithms, balancing performance and cost. Attached Figure Description
[0022] The present application will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a step diagram of an embodiment of this application; Figure 2 This is a schematic diagram of the electronic device structure in the embodiments of this application; Figure 3 This is a comparison chart of vehicle speed estimation in the embodiments of this application. Detailed Implementation
[0023] To provide a clearer understanding of the technical features, objectives, and effects of this application, the specific embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0024] The embodiments of this application provide a method for estimating the longitudinal speed of a vehicle based on multi-sensor fusion.
[0025] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps of a vehicle longitudinal speed estimation method based on multi-sensor fusion in an embodiment of this application, including: S1: Acquire multi-source sensor data of the vehicle, including GPS speed data, inertial measurement unit (IMU) data, wheel speed sensor data, and vehicle speed data output by the drive and braking system controller; S2: Calculate the vehicle's longitudinal acceleration based on the GPS speed data and the inertial measurement unit (IMU) data; Step S2 includes: Let the road slope angle be... :
[0026] in This represents the vehicle's forward acceleration calculated from data from the vehicle's inertial measurement unit (IMU). This represents acceleration data calculated from GPS velocity data; It represents the acceleration due to gravity.
[0027] Step S2 also includes: S21: Assuming the slope is zero, calculate the first slope estimation angle. :
[0028] S22: Estimate the first slope angle Substitute into the formula ,get ; S23: Calculation and The absolute value of the difference is compared with the calibration value; S24: If the absolute value is greater than the calibration value, let Repeat steps S22 to S23 until the absolute value is less than the calibration value, then proceed to step S25. S25: If the absolute value is less than or equal to the calibration value, let Determine the current slope angle ; Using the current slope angle Calculate the vehicle's longitudinal acceleration as follows:
[0029] in This indicates the longitudinal acceleration of the vehicle.
[0030] S3: Calculate the longitudinal speed of the first vehicle and the longitudinal speed of the second vehicle based on the GPS speed data and the vehicle speed data output by the drive braking system controller. Step S3 includes: The longitudinal speed of the first vehicle was obtained using GPS speed data, as follows:
[0031] in, This represents the vehicle's original speed value calculated from GPS speed data without slope compensation. The longitudinal speed of the second vehicle is obtained by using data from the wheel speed sensors and the vehicle speed data output by the drive braking system controller, as follows:
[0032] in, Indicates wheel speed. This indicates the effective radius of the wheel.
[0033] S4: Determine the longitudinal speed of the third vehicle based on the vehicle's longitudinal acceleration; Step S4 includes: Let the estimated longitudinal speed of the vehicle at the previous moment be... Then, based on the vehicle's acceleration, the longitudinal speed of the third vehicle is calculated as follows:
[0034] in Indicates a time interval.
[0035] S5: Use Kalman filtering or weighted fusion algorithm to fuse the longitudinal speeds of the first vehicle, the second vehicle, and the third vehicle, and output the final longitudinal speed of the vehicle.
[0036] Step S5 includes: The fusion algorithm employs a weighted fusion algorithm.
[0037] in Indicates the final longitudinal speed of the vehicle; , and Indicates the weighting coefficient; When GPS fails The weight is zero; when the wheels lock up or slip, The weight is zero; when hour, The value is 1.
[0038] In one embodiment, this embodiment uses a driving scenario of an electric SUV equipped with a standard sensor suite on a mountainous and complex road as an example to illustrate the implementation process of the method of the present invention in detail. The sensors on the vehicle include: a civilian single-frequency GPS receiver with a refresh rate of 10Hz (model example: U-blox NEO-M9N), a six-axis inertial measurement unit (IMU) (model example: ADI ADXL355, integrated into the vehicle domain controller), four magnetoelectric wheel speed sensors, and a vehicle control unit (VCU) responsible for drive and braking. The system runs in the vehicle chassis domain controller (computing unit model example: NXP S32G274A), and the software algorithm is implemented in C language, with a control cycle... t is set to 10ms.
[0039] In another embodiment, multi-source sensor data acquisition and preprocessing During each control cycle, the chassis domain controller synchronously acquires the following raw data via a high-speed controller area network (CAN FD bus): GPS data: Velocity vector (unit: m / s) in geographic coordinate system obtained directly from GPS receiver messages, along with positioning status indicators (such as satellite count and horizontal accuracy factor HDOP). Validity is verified: if the satellite count is greater than 6 and HDOP is less than 2.0, the signal is considered good, and the magnitude of the vector is used as the original velocity value at the current moment; otherwise, the GPS data is marked as invalid.
[0040] IMU Data: Read the three-axis acceleration and three-axis angular velocity output from the IMU. To obtain the vehicle's forward acceleration, the vehicle's yaw rate and lateral acceleration need to be used, and the data needs to be transformed from the vehicle coordinate system to a coordinate system aligned with the vehicle's longitudinal axis through coordinate transformation. In this embodiment, a simplified low-pass filter is used to process the IMU data to reduce high-frequency noise.
[0041] Wheel speed data: The system receives pulse frequencies from four wheel speed sensors and dynamically calculates the effective rolling radius and wheel speed (unit: r / min) of each wheel based on the tire rolling radius calibration value (cold-state calibration value) and the real-time tire pressure and temperature compensation model. In this embodiment, the average wheel speed of non-drive wheels (such as rear wheels) is used as the reference wheel speed to minimize the impact of drive slippage.
[0042] Controller vehicle speed data: This is a reference vehicle speed calculated internally by the vehicle controller based on motor speed and a fixed gear ratio. This data is typically used as an input for wheel speed calculations or for cross-validation.
[0043] To verify the effectiveness of the method in this embodiment, comparative tests were conducted on a real-world road containing long uphill slopes, downhill slopes, sharp bends, and short tunnels (simulating 20 seconds of GPS loss). The method of this invention (denoted as the "fusion-slope compensation method") was compared with two traditional methods: 1) the pure wheel speed method; and 2) the simple switching method of IMU integration and wheel speed without slope compensation.
[0044] The test results show that: On straight and good roads: the three methods are equally accurate, with errors all within ±0.3 km / h.
[0045] On roads with a continuous gradient (such as a 5% uphill): the pure wheel speed method, because it does not consider the radius change caused by tire deformation, has an error of +1.5 km / h; the IMU integral method without gradient compensation... Includes This resulted in a continuously high vehicle speed, with the error accumulating to +2.8 km / h; however, the method of this invention stabilizes the error within ±0.5 km / h through real-time slope compensation.
[0046] Inside the tunnel (GPS out of service): The method of this invention smoothly transitions to... Main (combined with the state before failure) The fusion mode of the vehicle speed drift is less than 0.8 km / h within 20 seconds, which is significantly better than the pure IMU integration method (drift exceeds 3 km / h).
[0047] Starting / Braking on Low-Friction Surfaces: The method of this invention, upon detecting drive wheel slippage or brake lock-up, quickly... Reducing the speed to zero avoids the influence of incorrect wheel speed data, and the fusion result is closer to the actual vehicle speed. Figure 3 This is a comparison chart of vehicle speed estimation in the embodiments of this application.
[0048] This application also discloses an electronic device. (See reference...) Figure 2 , Figure 2 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.
[0049] The communication bus 502 is used to enable communication between these components.
[0050] The user interface 503 may include a display screen, and optionally, the user interface 503 may also include a standard wired interface or a wireless interface.
[0051] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).
[0052] This application also discloses a computer-readable storage medium storing multiple instructions adapted for loading by a processor to execute the above-described method for estimating vehicle longitudinal speed based on multi-sensor fusion.
[0053] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure.
[0054] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.
Claims
1. A method for estimating longitudinal vehicle speed based on multi-sensor fusion, characterized in that, The method includes the following steps: S1: Acquire multi-source sensor data of the vehicle, including GPS speed data, inertial measurement unit (IMU) data, wheel speed sensor data, and vehicle speed data output by the drive and braking system controller; S2: Calculate the vehicle's longitudinal acceleration based on the GPS speed data and the inertial measurement unit (IMU) data; S3: Calculate the longitudinal speed of the first vehicle and the longitudinal speed of the second vehicle based on the GPS speed data and the vehicle speed data output by the drive braking system controller. S4: Determine the longitudinal speed of the third vehicle based on the vehicle's longitudinal acceleration; S5: Use Kalman filtering or weighted fusion algorithm to fuse the longitudinal speeds of the first vehicle, the second vehicle, and the third vehicle, and output the final longitudinal speed of the vehicle.
2. The vehicle longitudinal speed estimation method based on multi-sensor fusion as described in claim 1, characterized in that, Step S2 includes: Let the road slope angle be... : in This represents the vehicle's forward acceleration calculated from data from the vehicle's inertial measurement unit (IMU). This represents acceleration data calculated from GPS velocity data; It represents the acceleration due to gravity.
3. The vehicle longitudinal speed estimation method based on multi-sensor fusion as described in claim 2, characterized in that, Step S2 also includes: S21: Assuming the slope is zero, calculate the first slope estimation angle. : S22: Estimate the first slope angle Substitute into the formula ,get ; S23: Calculation and The absolute value of the difference is compared with the calibration value; S24: If the absolute value is greater than the calibration value, let Repeat steps S22 to S23 until the absolute value is less than the calibration value, then proceed to step S25. S25: If the absolute value is less than or equal to the calibration value, let Determine the current slope angle ; Using the current slope angle Calculate the vehicle's longitudinal acceleration as follows: in This indicates the longitudinal acceleration of the vehicle.
4. The vehicle longitudinal speed estimation method based on multi-sensor fusion as described in claim 3, characterized in that, Step S3 includes: The longitudinal speed of the first vehicle was obtained using GPS speed data, as follows: in, This represents the vehicle's original speed value calculated from GPS speed data without slope compensation. The longitudinal speed of the second vehicle is obtained by using data from the wheel speed sensors and the vehicle speed data output by the drive braking system controller, as follows: in, Indicates wheel speed. This indicates the effective radius of the wheel.
5. The method for estimating vehicle longitudinal speed based on multi-sensor fusion as described in claim 1, characterized in that, Step S4 includes: Let the estimated longitudinal speed of the vehicle at the previous moment be... Then, based on the vehicle's acceleration, the longitudinal speed of the third vehicle is calculated as follows: in Indicates a time interval.
6. The vehicle longitudinal speed estimation method based on multi-sensor fusion as described in claim 1, characterized in that, Step S5 includes: The fusion algorithm employs a weighted fusion algorithm. in Indicates the final longitudinal speed of the vehicle; , and Indicates the weighting coefficient; When GPS fails The weight is zero; when the wheels lock up or slip, The weight is zero; when hour, The value is 1.
7. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to enable the electronic device to perform the vehicle longitudinal speed estimation method based on multi-sensor fusion as described in any one of claims 1-6.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a computer, perform the vehicle longitudinal speed estimation method based on multi-sensor fusion as described in any one of claims 1-6.