Multi-sensor information fusion method and system based on rear wheel steering vehicle

Abstract

The invention discloses a vehicle multi-sensor information fusion method and system based on rear wheel steering, and relates to the technical field of sensor information processing. The vehicle multi-sensor information fusion method based on rear wheel steering comprises the following steps: data acquisition; evaluating influence conditions; vehicle state adjustment; and information fusion. According to the method, the inertia condition interference coefficient, the position state interference coefficient and the mileage state interference coefficient are obtained through the obtained multi-sensor data, whether the vehicle state is adjusted or not is judged, and finally the inertia condition interference coefficient, the position state interference coefficient and the mileage state interference coefficient after the vehicle state is adjusted are obtained. And information fusion is carried out to obtain the vehicle three-dimensional speed model, so that the effect of improving the accuracy of multi-sensor information fusion based on the rear-wheel steering vehicle is achieved, and the problem of low accuracy of multi-sensor information fusion based on the rear-wheel steering vehicle in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of sensor information processing technology, and in particular to a multi-sensor information fusion method and system based on a rear-wheel steering vehicle. Background Art

[0002] With the continuous development of integrated circuits, micro-electromechanical systems, navigation systems and other fields, as well as the continuous improvement of the market demand for safe driving of automobiles, the requirements for the accuracy, safety, real-time and reliability of positioning and attitude determination of vehicle-mounted integrated navigation products are becoming higher and higher. Nowadays, the development of autonomous driving technology is in full swing. With the different levels of autonomous driving from L1 to L5, the requirements for high-performance integrated navigation are further improved. At present, vehicle-mounted integrated navigation technology generally adopts the strategy of IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System) fusion based on vehicle kinematic model or IMU, GNSS and odometer fusion. The positioning and attitude determination information of IMU and GNSS fusion technology on open roads is reliable. The development of IMU, GNSS and odometer fusion technology is also relatively mature. It is a method that combines the vehicle kinematic model to set the lateral and celestial speeds of the rear wheels of the vehicle to zero for constraint. This method can provide relatively reliable positioning information within a certain period of time in complex scenarios such as tree-lined areas, tunnels, underground garages, and urban canyons when the rear wheels are fixed and there is no steering. However, for vehicles with steerable rear wheels, the traditional method has disadvantages because the rear wheels will also rotate when the vehicle turns. In this case, there will be a lateral speed. If it is not handled properly, the positioning error will increase rapidly when turning.

[0003] Existing multi-sensor information fusion methods for rear-wheel steering vehicles are mainly based on the commonly used strategies of IMU and GNSS fusion based on vehicle kinematic models or IMU, GNSS and odometer fusion.

[0004] For example, the invention patent with publication number: CN113269260A discloses a multi-sensor target fusion and tracking method and system for an intelligent driving vehicle, including: each time a frame of sensor data is received, a timestamp is added to the sensor data, and a first list is generated, the first list includes each target in the sensor data and its attribute information; the existing target list is predicted and updated according to the timestamp corresponding to the first list to obtain a predicted list; the predicted list is associated and matched with the first list, and a new target list is generated; the target and its attribute information corresponding to the preset output strategy are output from the new target list.

[0005] For example, the invention patent with announcement number: CN105160356B announces a vehicle active safety system sensor data fusion method and system, including: obtaining multiple groups of sensor trajectory point information groups from different sensors; judging whether the multiple groups of sensor trajectory point information groups refer to the same target, and if so, performing comprehensive calculation on the measured parameter values ​​of each trajectory point parameter in the multiple groups of sensor trajectory point information groups referring to the same target, obtaining the target parameter value of each trajectory point parameter, and generating a target trajectory point information group including multiple target parameter values.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, for vehicles with steerable rear wheels, the motion state of the rear wheels changes significantly when turning, which directly affects the positioning accuracy of the vehicle. Due to the rear-wheel steering, the vehicle's driving trajectory and posture will undergo more complex changes, resulting in the problem of low accuracy of multi-sensor information fusion based on rear-wheel steering vehicles. Summary of the invention

[0008] The embodiments of the present application solve the problem of low accuracy of multi-sensor information fusion for rear-wheel steering vehicles in the prior art by providing a method and system for multi-sensor information fusion for rear-wheel steering vehicles, thereby improving the accuracy of multi-sensor information fusion for rear-wheel steering vehicles.

[0009] The embodiment of the present application provides a multi-sensor information fusion method for a rear-wheel steering vehicle, comprising the following steps: S1, acquiring multi-sensor data, wherein the multi-sensor data includes IMU data, GNSS data and odometer data; S2, acquiring an inertial interference coefficient according to the IMU data, acquiring a position state interference coefficient according to the GNSS data, and acquiring a mileage state interference coefficient according to the odometer data, wherein the inertial interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle inertia at a preset time, the position state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle position at a preset time, and the mileage state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle driving distance at a preset time; S3, judging whether to adjust the vehicle state based on the inertial interference coefficient, the position state interference coefficient and the mileage state interference coefficient; S4, acquiring the inertial interference coefficient, the position state interference coefficient and the mileage state interference coefficient after the vehicle state adjustment, and inputting them into a Kalman filter for information fusion to obtain the fused wheel deflection angle information, and obtaining the vehicle three-dimensional speed model based on the wheel deflection angle information.

[0010] Furthermore, the multi-sensor data is obtained by measuring the steering condition of the vehicle's rear wheels at a preset moment through IMU measuring equipment, GNSS measuring equipment and mileage measuring equipment; the IMU measuring equipment includes IMU, integrated accelerometer, gyroscope and laser rangefinder; the IMU data includes vehicle linear speed, turning radius and vehicle wheelbase; the GNSS measuring equipment includes a GNSS receiver and a tire stiffness testing machine; the GNSS data includes rear wheel cornering stiffness and rear wheel cornering angle; the mileage measuring equipment includes a wheel diameter measuring instrument and a magnetic compass; the mileage data includes rear wheel radius and vehicle yaw angle.

[0011] Furthermore, the specific process of obtaining the inertial interference coefficient based on IMU data is as follows: combining the IMU data and the reference IMU data obtained from the database to obtain the lateral acceleration interference ratio and the rear wheel turning angle interference ratio, the reference IMU data including a preset lateral acceleration threshold and a preset rear wheel turning angle threshold; combining the lateral acceleration interference ratio and the rear wheel turning angle interference ratio to obtain the inertial interference coefficient; the lateral acceleration interference ratio is obtained by the following steps: A1, obtaining the initial lateral acceleration by performing a ratio operation on the square of the vehicle linear velocity and the turning radius; A2, obtaining the lateral acceleration interference ratio by performing a ratio operation on the preset lateral acceleration threshold and the initial lateral acceleration; the rear wheel turning angle interference ratio is obtained by the following steps: B1, obtaining the initial rear wheel turning angle by performing a ratio operation on the vehicle wheelbase and the turning radius and then performing an inverse tangent function operation; B2, obtaining the rear wheel turning angle interference ratio by performing a ratio operation on the preset rear wheel turning angle threshold and the initial rear wheel turning angle.

[0012] Furthermore, the limiting expression of the inertial interference coefficient is as follows:

[0013]

[0014] Wherein, GX represents the inertia interference coefficient of the vehicle, m represents the time variable, m∈[m0,m1], m0 represents the monitoring start time point, m1 represents the monitoring current time point, A(m) represents the lateral acceleration interference ratio of the vehicle at time m, ZJ(m) represents the rear wheel turning angle interference ratio of the vehicle at time m, V(m) represents the vehicle linear speed of the vehicle at time m, R(m) represents the turning radius of the vehicle at time m, L(m) represents the vehicle wheelbase of the vehicle at time m, A0 represents the preset lateral acceleration threshold, ZJ0 represents the preset rear wheel turning angle threshold, e represents the natural constant, ΔA represents the preset lateral acceleration interference range of the vehicle, and ΔZJ represents the preset rear wheel turning angle interference range of the vehicle.

[0015] Furthermore, the specific process of obtaining the position state interference coefficient based on GNSS data is as follows: combining the GNSS data and the reference GNSS data obtained from the database to obtain the cornering stiffness interference ratio and the cornering angle interference ratio, the reference GNSS data including a preset wheel cornering stiffness threshold and a preset rear wheel cornering angle threshold; combining the cornering stiffness interference ratio, the cornering angle interference ratio and the rear wheel turning angle interference ratio to obtain the position state interference coefficient; the cornering stiffness interference ratio is represented by the result of a ratio operation between the rear wheel cornering stiffness and the preset wheel cornering stiffness threshold; the cornering angle interference ratio is represented by the result of a ratio operation between the preset rear wheel cornering angle threshold and the rear wheel cornering angle.

[0016] Furthermore, the specific process of obtaining the mileage state interference coefficient based on the odometer data is as follows: combining the odometer data and the reference odometer data obtained from the database to obtain the angular velocity interference ratio and the yaw angle interference ratio; combining the angular velocity interference ratio, the yaw angle interference ratio and the rear wheel turning angle interference ratio to obtain the mileage state interference coefficient; the reference odometer data includes a preset vehicle angular velocity threshold and a preset vehicle yaw angle threshold; the yaw angle interference ratio is represented by the result of a ratio operation between a preset vehicle yaw angle threshold and a vehicle yaw angle; the angular velocity interference ratio is obtained by the following steps: C1, obtaining an initial angular velocity by performing a ratio operation between the vehicle linear velocity and the rear wheel radius, and the initial angular velocity is used to reflect the offset of the vehicle's rear wheel rotation direction; C2, obtaining an angular velocity interference ratio by performing a ratio operation between the preset vehicle angular velocity threshold and the initial angular velocity, and the angular velocity interference ratio is used to reflect the offset of the vehicle's rear wheel rotation direction.

[0017] Furthermore, the vehicle state adjustment includes vehicle adjustment and signal interference adjustment, and the specific process of the vehicle adjustment is as follows: step one, judging whether the inertia interference coefficient, the position state interference coefficient and the mileage state interference coefficient meet condition one, if condition one is met, it indicates that the vehicle is qualified, and information fusion is directly performed without vehicle adjustment, otherwise step two is executed; step two, sending a prompt to a preset person to adjust the tire pressure, the tire pressure adjustment is used to prevent the tire from aggravating wear, when the monitored inertia interference coefficient, the position state interference coefficient and the mileage state interference coefficient meet condition one, the vehicle adjustment is stopped, otherwise step three is executed; step three Step three, perform multi-channel data stream transmission. When the monitored inertial situation interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition one, stop vehicle adjustment. Otherwise, send an alarm prompt to the preset personnel. The multi-channel data stream transmission means that multiple independent data stream transmissions are realized under the same spectrum resources through a multi-input multi-output method to improve spectrum efficiency. Condition one means that the inertial situation interference coefficient is not higher than the preset inertial interference threshold obtained from the database, the position state interference coefficient is not higher than the preset position interference threshold obtained from the database, and the mileage state interference coefficient is not higher than the preset state interference threshold obtained from the database.

[0018] Furthermore, the specific process of obtaining the vehicle three-dimensional speed model based on the wheel deflection angle information is as follows: obtaining an inertial situation interference coefficient not higher than a preset inertial interference threshold, a position state interference coefficient not higher than a preset position interference threshold, and a mileage state interference coefficient not higher than a preset state interference threshold; inputting the obtained inertial situation interference coefficient not higher than the preset inertial interference threshold, the position state interference coefficient not higher than the preset position interference threshold, and the mileage state interference coefficient not higher than the preset state interference threshold into a Kalman filter for information fusion to obtain wheel deflection angle information; constructing a vehicle three-dimensional speed model according to the wheel deflection angle information, and the vehicle three-dimensional speed model is used to obtain the vehicle lateral speed, forward speed and vertical speed.

[0019] The embodiment of the present application provides a multi-sensor information fusion system based on a rear-wheel steering vehicle, including a multi-sensor data acquisition module, an impact condition assessment module, a vehicle state adjustment module and an information fusion module: wherein the multi-sensor data acquisition module is used to acquire multi-sensor data, and the multi-sensor data includes IMU data, GNSS data and odometer data; the impact condition assessment module is used to acquire an inertial condition interference coefficient according to the IMU data, acquire a position state interference coefficient according to the GNSS data, and acquire a mileage state interference coefficient according to the odometer data, and the inertial condition interference coefficient is used to assess the impact of the rear wheel deflection on the vehicle inertia at a preset time The position state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle position at a preset time, and the mileage state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle driving distance at a preset time; the vehicle state adjustment module is used to determine whether to adjust the vehicle state based on the inertia interference coefficient, the position state interference coefficient and the mileage state interference coefficient; the information fusion module is used to obtain the inertia interference coefficient, the position state interference coefficient and the mileage state interference coefficient after the vehicle state adjustment, and input them into the Kalman filter for information fusion to obtain the fused wheel deflection angle information, and obtain the vehicle three-dimensional speed model based on the wheel deflection angle information.

[0020] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] 1. The inertial interference coefficient, position state interference coefficient and mileage state interference coefficient are obtained by acquiring multi-sensor data and judging whether to adjust the vehicle state. Finally, the inertial interference coefficient, position state interference coefficient and mileage state interference coefficient after the vehicle state adjustment are obtained, and information fusion is performed to obtain the three-dimensional speed model of the vehicle, thereby improving the reliability of multi-sensor information fusion, and then improving the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles, effectively solving the problem of low accuracy of multi-sensor information fusion based on rear-wheel steering vehicles in the prior art.

[0022] 2. The inertia interference coefficient is obtained through the lateral acceleration interference ratio and the rear wheel turning angle interference ratio, and then the position state interference coefficient is obtained by combining the cornering stiffness interference ratio, the sideslip angle interference ratio and the rear wheel turning angle interference ratio. Finally, the mileage state interference coefficient is obtained by combining the angular velocity interference ratio, the yaw angle interference ratio and the rear wheel turning angle interference ratio, thereby achieving the improvement of the reliability of the information fusion data, and then achieving the improvement of the accuracy of the information fusion data.

[0023] 3. By judging whether the inertial interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition one, if not, vehicle adjustment and signal interference adjustment are performed, thereby achieving dynamic adjustment of information fusion data, and further achieving comprehensive improvement of multi-sensor information fusion based on rear-wheel steering vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a multi-sensor information fusion method based on a rear-wheel steering vehicle provided in an embodiment of the present application;

[0025] Figure 2 An overall flow chart provided for the embodiments of the present application;

[0026] Figure 3 A statistical diagram of changes in the cornering stiffness interference ratio-rear wheel cornering stiffness provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the structure of a multi-sensor information fusion system based on a rear-wheel steering vehicle provided in an embodiment of the present application;

[0028] Figure 5 A block diagram of the Kalman filter provided in the embodiment of the present application;

[0029] Figure 6 A horizontal position result diagram provided for an embodiment of the present application;

[0030] Figure 7 This is a graph of horizontal error results provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The embodiments of the present application solve the problem of low accuracy of multi-sensor information fusion for rear-wheel steering vehicles in the prior art by providing a method and system for multi-sensor information fusion for rear-wheel steering vehicles. The method obtains IMU data, GNSS data and odometer data, and then obtains the inertial interference coefficient according to the IMU data, obtains the position state interference coefficient according to the GNSS data, obtains the mileage state interference coefficient according to the odometer data, and then determines whether to adjust the vehicle state based on the inertial interference coefficient, the position state interference coefficient and the mileage state interference coefficient. Finally, the inertial interference coefficient, the position state interference coefficient and the mileage state interference coefficient after the vehicle state adjustment are obtained, and the information is input into a Kalman filter for information fusion to obtain the fused wheel deflection angle information, and the three-dimensional speed model of the vehicle is obtained based on the wheel deflection angle information, thereby improving the accuracy of multi-sensor information fusion for rear-wheel steering vehicles.

[0032] The technical solution in the embodiment of the present application is to solve the problem of low accuracy of multi-sensor information fusion based on rear-wheel steering vehicles. The overall idea is as follows:

[0033] Whether to adjust the vehicle state is determined by the obtained inertial interference coefficient, position state interference coefficient and mileage state interference coefficient. At the same time, the inertial interference coefficient, position state interference coefficient and mileage state interference coefficient after the vehicle state adjustment are obtained, and information fusion is performed to obtain the vehicle's three-dimensional speed model, thereby achieving the effect of improving the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles.

[0034] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0035] like Figure 1 As shown, it is a flow chart of a multi-sensor information fusion method for a rear-wheel steering vehicle provided in an embodiment of the present application, the method comprising the following steps: S1, data acquisition: acquiring multi-sensor data, the multi-sensor data comprising IMU data, GNSS data and odometer data, the multi-sensor data being obtained by measuring the rear-wheel steering condition of the vehicle at a preset time through an IMU measuring device, a GNSS measuring device and a mileage measuring device; S2, impact condition assessment: acquiring an inertial condition interference coefficient according to the IMU data, acquiring a position state interference coefficient according to the GNSS data, acquiring a mileage state interference coefficient according to the mileage data, the inertial condition interference coefficient being used to assess the impact of the rear wheel deflection on the vehicle inertia at a preset time, the position state The interference coefficient is used to evaluate the impact of rear wheel deflection on the vehicle position at a preset time, and the mileage state interference coefficient is used to evaluate the impact of rear wheel deflection on the vehicle's driving distance at a preset time; S3, vehicle state adjustment: determine whether to adjust the vehicle state based on the inertia interference coefficient, position state interference coefficient and mileage state interference coefficient. The vehicle state adjustment is used to reduce interference during vehicle steering to improve the accuracy of multi-sensor data; S4, information fusion: obtain the inertia interference coefficient, position state interference coefficient and mileage state interference coefficient after vehicle state adjustment, and input them into the Kalman filter for information fusion to obtain the fused wheel deflection angle information, and obtain the vehicle's three-dimensional speed model based on the wheel deflection angle information.

[0036] like Figure 2 As shown, it is an overall flow chart provided by the embodiment of the present application, which is composed of Figure 2 It can be seen that the process of the embodiment of the present application is demonstrated.

[0037] When the rear wheel of the vehicle deflects, the increase in the inertial state interference coefficient may cause changes in the vehicle speed and acceleration, which in turn affects the actual position of the vehicle. For example, if the rear wheel deflection causes the vehicle to accelerate or decelerate, then the actual position of the vehicle will deviate from the preset path, resulting in an increase in the position state interference coefficient. The change in inertial state (such as acceleration change) caused by the rear wheel deflection will directly affect the vehicle's driving distance. For example, if the rear wheel deflection causes the vehicle to accelerate, then the vehicle will travel a longer distance in the same time, thereby increasing the mileage state interference coefficient. There is a close mutual influence relationship between the inertial state interference coefficient, the position state interference coefficient, and the mileage state interference coefficient. In the evaluation of vehicle dynamic performance, it is necessary to comprehensively consider these three coefficients and their mutual influence relationship; the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles is improved.

[0038] It should be added that the IMU measurement equipment includes IMU, integrated accelerometer, gyroscope and laser rangefinder; IMU data includes vehicle linear speed, turning radius and vehicle wheelbase; at a preset time, the vehicle linear speed is measured by an integrated accelerometer deployed at a preset position point in the vehicle passing area, the preset position point represents a plurality of position points marked by a preset person in the preset area, the IMU measurement equipment, GNSS measurement equipment and mileage measurement equipment can be deployed at different position points, the IMU will detect the change in the angular velocity of the rear wheel, at the preset time, the gyroscope deployed at the preset position point in the vehicle passing area combined with the change in the angular velocity of the rear wheel is used to obtain the turning radius of the vehicle, and at the preset time, the laser rangefinder deployed at the preset position point in the vehicle passing area is used to measure the vehicle wheelbase.

[0039] The GNSS measuring equipment includes a GNSS receiver and a tire stiffness testing machine; the GNSS data includes rear wheel cornering stiffness and rear wheel cornering angle; the rear wheel cornering stiffness of the vehicle is measured at a preset time by the tire stiffness testing machine deployed at a preset position point in the vehicle passing area, and the rear wheel cornering angle of the vehicle is measured at a preset time by the GNSS receiver deployed at a preset position point in the vehicle passing area.

[0040] The mileage measurement equipment includes a wheel diameter measuring instrument and a magnetic compass; the mileage data includes the rear wheel radius and the vehicle yaw angle. The rear wheel radius of the vehicle is measured by the wheel diameter measuring instrument at a preset time, and the vehicle yaw angle of the vehicle is measured by a magnetic compass deployed at a preset position point in the area where the vehicle passes at a preset time; thereby improving the accuracy of obtaining information fusion-related data, and further improving the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles.

[0041] Furthermore, the specific process of obtaining the inertial situation interference coefficient based on the IMU data is as follows: the lateral acceleration interference ratio and the rear wheel angle interference ratio are obtained by combining the IMU data and the reference IMU data obtained from the database, the reference IMU data includes a preset lateral acceleration threshold and a preset rear wheel angle threshold; the inertial situation interference coefficient is obtained by combining the lateral acceleration interference ratio and the rear wheel angle interference ratio.

[0042] The lateral acceleration interference ratio is obtained by the following steps: A1, the initial lateral acceleration (i.e., the inertial interference coefficient in the limiting expression) is obtained by performing a ratio operation by the square of the vehicle linear velocity and the turning radius. ), the initial lateral acceleration is used to reflect the dynamic performance of the vehicle when the rear wheels are turning; A2, the lateral acceleration interference ratio (i.e. A(m) in the limiting expression of the inertia interference coefficient) is obtained by performing a ratio operation between the preset lateral acceleration threshold and the initial lateral acceleration. The lateral acceleration interference ratio is used to reflect the dynamic performance of the vehicle when the rear wheels are turning.

[0043] The rear wheel turning angle interference ratio is obtained by the following steps: B1, the initial rear wheel turning angle (i.e., the inertia interference coefficient in the restriction expression) is obtained by performing a ratio operation on the vehicle wheelbase and the turning radius and then performing an inverse tangent function operation. ), the initial rear wheel steering angle is used to reflect the rear wheel deflection condition of the vehicle at a preset moment; B2, the rear wheel steering angle interference ratio (i.e. ZJ(m) in the restriction expression of the inertia interference coefficient) is obtained by ratio calculation between the preset rear wheel steering angle threshold and the initial rear wheel steering angle, and the rear wheel steering angle interference ratio is used to reflect the compliance of the rear wheel deflection of the vehicle at a preset moment.

[0044] Among them, the limiting expression of the inertial interference coefficient is as follows:

[0045]

[0046] Wherein, GX represents the inertia interference coefficient of the vehicle, m represents the time variable, m∈[m0,m1], m0 represents the monitoring start time point, m1 represents the monitoring current time point, A(m) represents the lateral acceleration interference ratio of the vehicle at time m, ZJ(m) represents the rear wheel turning angle interference ratio of the vehicle at time m, V(m) represents the vehicle linear speed of the vehicle at time m, R(m) represents the turning radius of the vehicle at time m, L(m) represents the vehicle wheelbase of the vehicle at time m, A0 represents the preset lateral acceleration threshold, ZJ0 represents the preset rear wheel turning angle threshold, e represents the natural constant, ΔA represents the preset lateral acceleration interference range of the vehicle, and ΔZJ represents the preset rear wheel turning angle interference range of the vehicle.

[0047] The aforementioned database is a database for storing various types of setting data established before the design of the multi-sensor information fusion method for rear-wheel steering vehicles provided in the embodiment of the present application. The database includes but is not limited to lateral acceleration, turning radius, rear wheel radius, etc., and the various values ​​therein are directly set by technical personnel. For example, the preset lateral acceleration threshold is represented by the average value of the vehicle lateral acceleration in the historical time period in the database, and the preset rear wheel turning angle threshold is represented by the average value of the vehicle rear wheel turning angle in the historical time period in the database.

[0048] In this embodiment, the range of the preset lateral acceleration interference range ΔA is that ΔA is greater than 1. When ΔA is less than or equal to 1, that is, the initial lateral acceleration (the inertia interference coefficient in the restriction expression) is ) is less than or equal to the preset lateral acceleration threshold, indicating that the dynamic performance compliance of the vehicle during rear wheel steering is high, and the lateral acceleration interference ratio can be regarded as 0.

[0049] In this embodiment, the range of the preset rear wheel steering angle interference range ΔZJ is that ΔZJ is greater than 1. When ΔZJ is less than or equal to 1, that is, the initial rear wheel steering angle (arctan in the restriction expression of the inertia interference coefficient) ) is less than or equal to the preset rear wheel steering angle threshold, indicating that the compliance degree of the vehicle's rear wheel deflection is high, and the rear wheel steering angle interference ratio can be regarded as 0.

[0050] It should be understood that the algorithm of this embodiment combines the IMU data for comprehensive analysis to obtain the inertial interference coefficient. The IMU data in the algorithm of this embodiment does not exist independently, but is interrelated. When the vehicle linear speed increases, the turning radius may decrease accordingly. The reduction in the turning radius means that the vehicle needs to complete the turning action in a smaller space, which may cause the vehicle's inertial interference coefficient to increase. When the turning radius decreases, the vehicle needs to change its motion state faster, which may result in greater lateral acceleration and more severe tire wear, thereby increasing the inertial interference coefficient. The parameters of the algorithm of this embodiment need to be considered together and their impact on the results.

[0051] Specifically, assuming that the range of the lateral acceleration interference ratio A(m) is 1-2, the range of the rear wheel steering angle interference ratio ZJ(m) is 1-2, the monitoring start time point m0 ​​is 12:01, the monitoring current time point m1 is 12:10, and the difference between the monitoring start time point and the monitoring current time point is 9 minutes, as shown in Table 1, which is a statistical table of changes in the inertia interference coefficient provided in the embodiment of the present application:

[0052] Table 1 Statistics of changes in inertial interference coefficient

[0053]

[0054] It can be seen from the above table that with the gradual increase of the lateral acceleration interference ratio A(m), the rear wheel steering angle interference ratio ZJ(m) gradually increases, and the inertia interference coefficient GX gradually decreases, which means that the influence of the rear wheel deflection on the vehicle inertia is reduced, and the accurate quantification of the influence of the rear wheel deflection on the vehicle inertia is achieved, thereby achieving the improvement of the accuracy of multi-sensor information fusion based on the rear-wheel steering vehicle.

[0055] Furthermore, the specific process of obtaining the position state interference coefficient based on the GNSS data is as follows: the cornering stiffness interference ratio and the slip angle interference ratio are obtained by combining the GNSS data and the reference GNSS data obtained from the database, the reference GNSS data including a preset wheel cornering stiffness threshold and a preset rear wheel slip angle threshold; the position state interference coefficient is obtained by combining the cornering stiffness interference ratio, the slip angle interference ratio and the rear wheel steering angle interference ratio.

[0056] The cornering stiffness interference ratio is expressed by the ratio operation between the rear wheel cornering stiffness and the preset wheel cornering stiffness threshold (i.e., CPJ(m) in the restriction expression of the position state interference coefficient). The cornering stiffness interference ratio is used to reflect the compliance of the vehicle's rear wheel resistance to cornering deformation when turning.

[0057] The sideslip angle interference ratio is expressed by the ratio operation between the preset rear wheel sideslip angle threshold and the rear wheel sideslip angle (i.e., CGD(m) in the restriction expression of the position state interference coefficient). The sideslip angle interference ratio is used to reflect the compliance of the lateral tilt degree of the vehicle's rear wheels when turning.

[0058] Among them, when CPJ(m)∈ΔCPJ and CGD(m)∈ΔCGD are satisfied, the restriction expression of the position state interference coefficient is as follows:

[0059]

[0060] Wherein, WZ represents the vehicle's position state interference coefficient, m∈[m0,m1], m0 represents the monitoring start time point, m1 represents the monitoring current time point, CPJ(m) represents the vehicle's cornering stiffness interference ratio at time m, CGD(m) represents the vehicle's cornering angle interference ratio at time m, ZJ(m) represents the vehicle's rear wheel steering angle interference ratio at time m, C(m) represents the vehicle's rear wheel cornering stiffness at time m, α(m) represents the vehicle's rear wheel cornering angle at time m, CPJ0 represents the preset wheel cornering stiffness threshold, CGD0 represents the preset rear wheel cornering angle threshold, e represents a natural constant, ΔCPJ represents the vehicle's preset cornering stiffness interference range, and ΔCGD represents the vehicle's preset cornering angle interference range.

[0061] In the present embodiment, the preset wheel cornering stiffness threshold is represented by the average value of the vehicle wheel cornering stiffness in the historical time period in the database, and the preset rear wheel slip angle threshold is represented by the average value of the vehicle rear wheel slip angle in the historical time period in the database. In the present embodiment, the range of the preset rear wheel steering angle interference range ΔCPJ is ΔCPJ less than 1. When ΔCPJ is greater than or equal to 1, that is, the rear wheel cornering stiffness (C(m) in the limiting expression of the position state interference coefficient) is greater than or equal to the preset wheel cornering stiffness threshold, indicating that the vehicle has a high degree of compliance with the ability to resist cornering deformation when the rear wheels are turned, and the cornering stiffness interference ratio can be regarded as 1; in the present embodiment, the range of the preset slip angle interference range ΔCGD is ΔCGD greater than 1. When ΔCGD is less than or equal to 1, that is, the rear wheel slip angle (α(m) in the limiting expression of the position state interference coefficient) is less than or equal to the preset rear wheel slip angle threshold, indicating that the vehicle has a high degree of compliance with the lateral tilt degree when the rear wheels are turned, and the slip angle interference ratio can be regarded as 0.

[0062] It should be understood that the algorithm of this embodiment combines GNSS data for comprehensive analysis to obtain the position state interference coefficient. The GNSS data in the algorithm of this embodiment does not exist independently, but is interrelated. The greater the cornering stiffness, the more the rear wheels of the vehicle can maintain the original driving direction when subjected to cornering force, which leads to a decrease in the position state interference coefficient. The rear wheel slip angle and cornering stiffness are interrelated. The change in the slip angle will directly affect the cornering stiffness, and then affect the vehicle's handling performance. The greater the rear wheel slip angle, the easier it is for the vehicle to deviate from the expected driving path, which leads to an increase in the position state interference coefficient. The parameters of the algorithm of this embodiment need to be considered together and at the same time to affect the results.

[0063] Specifically, assuming that the rear wheel cornering stiffness C (m) ranges from 500 to 1000 (N / rad), the preset wheel cornering stiffness threshold CPJ0 is fixed at 500 (N / rad), such as Figure 3 As shown, it is a statistical diagram of the change of the cornering stiffness interference ratio-rear wheel cornering stiffness provided by the embodiment of the present application, Figure 3 It can be seen that as the cornering stiffness of the rear wheels gradually increases, the cornering stiffness interference ratio gradually increases, which means that the compliance of the vehicle's ability to resist cornering deformation when the rear wheels are turned gradually improves, achieving accurate quantification of the compliance of the vehicle's ability to resist cornering deformation when the rear wheels are turned, and thus achieving improved accuracy of multi-sensor information fusion based on rear-wheel steering vehicles.

[0064] Furthermore, the specific process of obtaining the mileage state interference coefficient based on the odometer data is as follows: the angular velocity interference ratio and the yaw angle interference ratio are obtained by combining the odometer data and the reference odometer data obtained from the database; the mileage state interference coefficient is obtained by combining the angular velocity interference ratio, the yaw angle interference ratio and the rear wheel turning angle interference ratio; the reference odometer data includes a preset vehicle angular velocity threshold and a preset vehicle yaw angle threshold; the yaw angle interference ratio is represented by the result of a ratio operation between a preset vehicle yaw angle threshold and a vehicle yaw angle (i.e., PH(m) in the restriction expression of the mileage state interference coefficient), and the preset vehicle yaw angle threshold is used to reflect the compliance of the vehicle's driving direction change when the vehicle's rear wheels are turned.

[0065] The angular velocity interference ratio is obtained by the following steps: C1, the initial angular velocity (i.e., the value in the limiting expression of the mileage state interference coefficient) is obtained by performing a ratio operation between the vehicle linear velocity and the rear wheel radius. ), the initial angular velocity is used to reflect the deviation of the rotation direction of the rear wheels of the vehicle; C2, the angular velocity interference ratio (i.e. J(m) in the restriction expression of the mileage state interference coefficient) is obtained by ratio calculation between the preset vehicle angular velocity threshold and the initial angular velocity, and the angular velocity interference ratio is used to reflect the deviation of the rotation direction of the rear wheels of the vehicle.

[0066] Among them, the limiting expression of the mileage state interference coefficient is as follows:

[0067]

[0068] Wherein, LC represents the mileage state interference coefficient of the vehicle, m∈[m0,m1], m0 represents the monitoring start time point, m1 represents the monitoring current time point, J(m) represents the angular velocity interference ratio of the vehicle at time m, PH(m) represents the yaw angle interference ratio of the vehicle at time m, ZJ(m) represents the rear wheel turning angle interference ratio of the vehicle at time m, V(m) represents the vehicle linear velocity of the vehicle at time m, RL(m) represents the rear wheel radius of the vehicle at time m, θ(m) represents the vehicle yaw angle of the vehicle at time m, J0 represents the preset vehicle angular velocity threshold, PH0 represents the preset vehicle yaw angle threshold, e represents the natural constant, ΔJ represents the preset angular velocity interference range of the vehicle, and ΔPH represents the preset yaw angle interference range of the vehicle.

[0069] In this embodiment, the preset vehicle angular velocity threshold is represented by the average value of the vehicle angular velocity in the historical time period in the database, and the preset vehicle yaw angle threshold is represented by the average value of the vehicle yaw angle in the historical time period in the database. In this embodiment, the range of the preset angular velocity interference range ΔJ is ΔJ greater than 1. When ΔJ is less than or equal to 1, that is, the initial angular velocity (the limit expression of the mileage state interference coefficient) ) is less than or equal to the preset vehicle angular velocity threshold, indicating that the compliance degree of the deviation of the rotation direction of the rear wheels of the vehicle is high, and the angular velocity interference ratio can be regarded as 0; the range of the preset yaw angle interference range PH in this embodiment is PH greater than 1. When PH is less than or equal to 1, that is, the vehicle yaw angle (θ(m) in the restriction expression of the mileage state interference coefficient) is less than or equal to the preset vehicle yaw angle threshold, indicating that the compliance degree of the change of the vehicle's driving direction when the rear wheels of the vehicle are turned is high, and the yaw angle interference ratio can be regarded as 0.

[0070] It should be understood that the algorithm of this embodiment combines the odometer data for comprehensive analysis to obtain the mileage state interference coefficient. The odometer data in the algorithm of this embodiment does not exist independently, but is interrelated. The change in the vehicle's yaw angle reflects the vehicle's steering performance. The change in the rear wheel angle will directly affect the vehicle's steering performance and driving path. The change in the rear wheel radius will directly affect the vehicle's driving distance and speed. If the rear wheel radius decreases, the vehicle's driving distance will decrease in the same time, resulting in an increase in the mileage state interference coefficient. An increase in the vehicle's yaw angle means that the vehicle has deviated from the preset driving path, which will lead to an increase in the driving distance and an increase in the mileage state interference coefficient. The parameters of the algorithm of this embodiment need to jointly consider their impact on the results; it achieves accurate quantification of the impact of rear wheel deflection on the vehicle's driving distance, thereby achieving an improvement in the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles.

[0071] Furthermore, vehicle status adjustment includes vehicle adjustment and signal interference adjustment. The specific process of vehicle adjustment is as follows: Step 1, determine whether the inertia interference coefficient, position status interference coefficient and mileage status interference coefficient meet condition 1. If condition 1 is met, it indicates that the vehicle is qualified, and information fusion is directly performed without vehicle adjustment. Otherwise, step 2 is executed; Step 2, send a prompt to the preset personnel to adjust the tire pressure. Tire pressure adjustment is used to prevent increased tire wear. When the monitored inertia interference coefficient, position status interference coefficient and mileage status interference coefficient meet condition 1, stop vehicle adjustment. Otherwise, step 3 is executed; Step 3. Perform multi-channel data stream transmission. When the monitored inertial interference coefficient, position status interference coefficient and mileage status interference coefficient meet condition one, stop making vehicle adjustments. Otherwise, send an alarm to the preset personnel. Multi-channel data stream transmission means realizing multi-channel independent data stream transmission under the same spectrum resources through the multi-input multi-output method to improve spectrum efficiency. Condition one means that the inertial interference coefficient is not higher than the preset inertial interference threshold obtained from the database, the position status interference coefficient is not higher than the preset position interference threshold obtained from the database, and the mileage status interference coefficient is not higher than the preset status interference threshold obtained from the database.

[0072] In this embodiment, the preset inertial interference threshold is represented by the average value of the inertial interference coefficient of the historical time period in the database, the preset position interference threshold is represented by the average value of the historical position state interference coefficient in the database, and the preset state interference threshold is represented by the average value of the mileage state interference coefficient of the historical time period in the database. The preset personnel adjust the tire pressure of the vehicle tire according to the prompt, increase or decrease the tire pressure of the wheel tire to prevent increased tire wear, the system starts multi-channel data stream transmission, and uses multi-input multi-output technology to realize multi-channel independent data stream transmission under the same spectrum resources to improve spectrum efficiency; the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles is improved.

[0073] Furthermore, the specific process of signal interference adjustment is as follows: the first step is to perform signal separation. When the monitored inertial interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition one, stop performing signal interference adjustment, otherwise execute the second step. Signal separation means reducing the interference of multipath effect through mediation algorithm; the second step is to perform filtering processing. When the monitored inertial interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition one, stop performing signal interference adjustment, otherwise execute the third step. Filtering processing means absorbing the surge voltage generated by the preset interference equipment and the peak voltage of the main power supply through filtering method; the third step is to perform signal range coverage adjustment. When the monitored inertial interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition one, stop performing signal interference adjustment, otherwise send an alarm prompt to the preset personnel. Signal range coverage adjustment means improving the signal coverage range through beamforming method.

[0074] In this embodiment, the interference of multipath effect is reduced by mediation algorithm. Multipath effect refers to the fact that during the transmission process of a signal, multiple paths are transmitted due to obstacles on different paths, resulting in mutual interference of signals. Signal separation aims to separate these interference signals and improve signal quality: the surge voltage generated by the preset interference device and the peak voltage of the main power supply are absorbed by filters (such as surge absorbers and band-stop filters). Interference usually comes from the external environment or the inside of the device, which will cause additional interference to the signal. The coverage range of the signal is increased to the maximum coverage range set by the preset personnel through the beamforming method. By sending prompts to the preset personnel, the phase and amplitude between different elements in the sensor array are gradually increased or decreased by preset multiples. Beamforming increases the signal strength at the receiving end by concentrating the signal energy in the preset direction; the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles is improved.

[0075] like Figure 4As shown, it is a structural schematic diagram of a multi-sensor information fusion system for a rear-wheel steering vehicle provided in an embodiment of the present application. The multi-sensor information fusion system for a rear-wheel steering vehicle provided in an embodiment of the present application includes: a multi-sensor data acquisition module, an impact condition evaluation module, a vehicle state adjustment module and an information fusion module: wherein the multi-sensor data acquisition module is used to acquire multi-sensor data, and the multi-sensor data includes IMU data, GNSS data and odometer data. The multi-sensor data is obtained by measuring the rear-wheel steering conditions of the vehicle at a preset time through an IMU measurement device, a GNSS measurement device and a mileage measurement device; the impact condition evaluation module is used to acquire an inertial condition interference coefficient according to the IMU data, acquire a position state interference coefficient according to the GNSS data, acquire a mileage state interference coefficient according to the odometer data, and acquire an inertial condition interference coefficient according to the mileage state interference coefficient. The condition interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle inertia at a preset moment, the position state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle position at a preset moment, and the mileage state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle driving distance at a preset moment; the vehicle state adjustment module is used to determine whether to adjust the vehicle state based on the inertia condition interference coefficient, the position state interference coefficient and the mileage state interference coefficient. The vehicle state adjustment is used to reduce the interference during vehicle steering to improve the accuracy of multi-sensor data; the information fusion module is used to obtain the inertia condition interference coefficient, the position state interference coefficient and the mileage state interference coefficient after the vehicle state adjustment, and input them into the Kalman filter for information fusion to obtain the fused wheel deflection angle information, and obtain the vehicle three-dimensional speed model based on the wheel deflection angle information.

[0076] Specifically, the specific process of obtaining the vehicle three-dimensional speed model based on the wheel deflection angle information is as follows: obtaining an inertial situation interference coefficient not higher than a preset inertial interference threshold, a position state interference coefficient not higher than a preset position interference threshold, and a mileage state interference coefficient not higher than a preset state interference threshold; inputting the obtained inertial situation interference coefficient not higher than the preset inertial interference threshold, the position state interference coefficient not higher than the preset position interference threshold, and the mileage state interference coefficient not higher than the preset state interference threshold into the Kalman filter for information fusion to obtain the wheel deflection angle information; constructing the vehicle three-dimensional speed model according to the wheel deflection angle information, and the vehicle three-dimensional speed model is used to obtain the vehicle lateral speed, forward speed and vertical speed.

[0077] In this embodiment, the Kalman filter can suppress noise and optimize the measurement results by fusing multi-source information. The rear wheels of traditional passenger vehicles are fixed wheels, and the vehicle speed value v is obtained. dIn actual situations, some vehicle models are equipped with four-wheel steering, which has the advantage of reducing the centripetal force of the vehicle when turning at high speed to prevent the vehicle from rolling over; providing a smaller turning radius for the vehicle at low speed, reducing the operating difficulty of the driver and improving the driving experience. For vehicles with four-wheel steering, it is not applicable to model the three-dimensional velocity of the vehicle under the flight integrity constraint. Therefore, the embodiment of the present application will extract the rear wheel deflection angle information θ, calculate the lateral, forward and vertical velocities of the vehicle, and the specific expression of the vehicle three-dimensional velocity model is as follows:

[0078] v v odo =[v d *sinθ,v d *cosθ,0] T , θ represents the rear wheel steering angle information extracted by the Kalman filter, v d Indicates the speed of the car when the rear wheels are fixed, v v odo Represents the three-dimensional velocity of the vehicle.

[0079] like Figure 5 As shown, it is a block diagram of Kalman filtering provided in an embodiment of the present application, and the steps of Kalman filtering are as follows:

[0080] 1) Time update:

[0081] State one-step prediction:

[0082] State one-step prediction mean square error: P k / k-1 =φ k / k-1 *P k-1 *φ k / k-1 T +Q k-1 ;

[0083] 2) Measurement update:

[0084] Filter gain: K k =P k / k-1 *H k T *[H k *P k / k-1 *H k T +R k ] -1 ;

[0085] State Estimation:

[0086] Variance estimation: P k =(1-K k *H k )*P k / k-1;

[0087] The error-based state quantity X is selected to have a total of 18 dimensions:

[0088] The state quantities are expressed as the three-dimensional misalignment angle φ, velocity error δ v , position error δ p , gyro bias ε b , plus zero bias Odometer external parameter k D . Among them, the odometer external parameter k D It represents the pitch installation deflection angle between the IMU body coordinate system and the body coordinate system, the odometer scale coefficient, and the heading installation deflection angle between the IMU body coordinate system and the body coordinate system. k represents the current time step of the state estimation (one time step represents one update of the state estimation). For example, K=1 represents the first time step, k=2 represents the second time step, and k-1 represents the previous time step of the state estimation. In each time step of the Kalman filter, the current state estimation is updated according to the state estimation of the previous time step and the current observation value. Here, the roll installation deflection angle does not affect the speed measurement value of the odometer, so it can be ignored here.

[0089] The observed quantity is: Z1=[v ins -v gnns ,p ins -p gnns ] T ;

[0090] Z2=[v ins -v n odo ] T ;

[0091] Z1 and Z2 represent the observation information of GNSS and odometer respectively at different times. n odo Represents the speed v provided by the odometer v odo From the vehicle coordinate system v system to the navigation coordinate system n system.

[0092] like Figure 6 As shown, it is a horizontal position result diagram provided by the embodiment of the present application, Figure 6 It can be seen that the horizontal position before optimization is smaller than the horizontal position corresponding to the true value, such as Figure 7 As shown, it is a horizontal error result diagram provided by the embodiment of the present application, Figure 7 It can be seen that the optimized horizontal error is close to 0.

[0093] In the strapdown inertial navigation algorithm (attitude update algorithm, velocity update algorithm and position update algorithm), the attitude update solution of the vehicle is performed. The dead reckoning algorithm based on the odometer can directly use the attitude matrix calculated by inertial navigation to perform coordinate transformation on the odometer measurement. The acceleration and deceleration of the vehicle during movement can help to reduce the error k. D Identification.

[0094] Specifically, the strapdown inertial navigation algorithm relies on the output of the gyroscope and accelerometer to accurately measure and update the position, speed and attitude information of the vehicle. The attitude update algorithm in the strapdown inertial navigation algorithm updates the direction cosine matrix (or quaternion) by using the angular velocity output of the gyroscope, thereby obtaining the attitude change of the carrier at different time points; the speed update algorithm in the strapdown inertial navigation algorithm integrates the acceleration of the vehicle to obtain the speed estimate, that is, the acceleration of the vehicle in the inertial space; the position update algorithm in the strapdown inertial navigation algorithm continuously integrates the real-time speed of the vehicle to obtain the displacement change of the vehicle relative to the initial position, and then obtain the current position information of the vehicle.

[0095] In summary, the embodiment of the present application obtains the inertial interference coefficient, the position state interference coefficient and the mileage state interference coefficient through the acquired multi-sensor data and determines whether to adjust the vehicle state, and finally obtains the inertial interference coefficient, the position state interference coefficient and the mileage state interference coefficient after the vehicle state adjustment, and performs information fusion to obtain the vehicle's three-dimensional speed model, thereby improving the reliability of multi-sensor information fusion, and further improving the accuracy of multi-sensor information fusion based on rear-wheel steering vehicles, effectively solving the problem of low accuracy of multi-sensor information fusion based on rear-wheel steering vehicles in the prior art.

[0096] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0101] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-sensor information fusion method based on a rear-wheel steering vehicle, characterized in that: The following steps are involved: S1, acquiring multi-sensor data, where the multi-sensor data includes IMU data, GNSS data, and odometer data; S2, obtaining an inertial interference coefficient according to the IMU data, obtaining a position state interference coefficient according to the GNSS data, and obtaining a mileage state interference coefficient according to the odometer data, wherein the inertial interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle inertia at a preset time, the position state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle position at a preset time, and the mileage state interference coefficient is used to evaluate the influence of the rear wheel deflection on the vehicle travel distance at a preset time; S3, judging whether to adjust the vehicle state based on the inertia condition interference coefficient, the position state interference coefficient and the mileage state interference coefficient; S4, obtaining the inertial interference coefficient, position interference coefficient and mileage interference coefficient after the vehicle state adjustment, and inputting them into the Kalman filter for information fusion to obtain the fused wheel deflection angle information, and obtaining the vehicle's three-dimensional speed model based on the wheel deflection angle information.

2. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 1, characterized in that: The multi-sensor data is obtained by measuring the rear wheel steering condition of the vehicle at a preset time through an IMU measuring device, a GNSS measuring device and an mileage measuring device; The IMU measurement device includes an IMU, an integrated accelerometer, a gyroscope and a laser rangefinder; The IMU data includes vehicle linear speed, turning radius and vehicle wheelbase; The GNSS measurement equipment includes a GNSS receiver and a tire stiffness tester The GNSS data includes rear wheel cornering stiffness and rear wheel cornering angle; The mileage measuring equipment includes a wheel diameter measuring instrument and a magnetic compass; The odometer data includes rear wheel radius and vehicle yaw angle.

3. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 2, characterized in that: The specific process of obtaining the inertial interference coefficient according to the IMU data is as follows: Acquire a lateral acceleration interference ratio and a rear wheel turning angle interference ratio by combining the IMU data and reference IMU data obtained from a database, wherein the reference IMU data includes a preset lateral acceleration threshold and a preset rear wheel turning angle threshold; The inertia disturbance coefficient is obtained by combining the lateral acceleration disturbance ratio and the rear wheel turning angle disturbance ratio; The lateral acceleration disturbance ratio is obtained by the following steps: A1, the initial lateral acceleration is obtained by calculating the ratio of the square of the vehicle linear velocity and the turning radius; A2, obtaining a lateral acceleration interference ratio by performing a ratio operation between a preset lateral acceleration threshold and an initial lateral acceleration; The rear wheel steering angle interference ratio is obtained by the following steps: B1, calculate the ratio of the vehicle wheelbase and the turning radius and then perform an inverse tangent function calculation to obtain the initial rear wheel turning angle; B2, obtaining the rear wheel turning angle interference ratio by performing ratio calculation on the preset rear wheel turning angle threshold and the initial rear wheel turning angle.

4. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 3, characterized in that: The limiting expression of the interference coefficient of the inertial situation is as follows: Wherein, GX represents the inertia interference coefficient of the vehicle, m represents the time variable, m∈[m 0,m1], m0 represents the monitoring start time point, m1 represents the monitoring current time point, A(m) represents the lateral acceleration interference ratio of the vehicle at time m, ZJ(m) represents the rear wheel turning angle interference ratio of the vehicle at time m, V(m) represents the vehicle linear speed of the vehicle at time m, R(m) represents the turning radius of the vehicle at time m, L(m) represents the vehicle wheelbase of the vehicle at time m, A0 represents the preset lateral acceleration threshold, ZJ0 represents the preset rear wheel turning angle threshold, e represents the natural constant, ΔA represents the preset lateral acceleration interference range of the vehicle, and ΔZJ represents the preset rear wheel turning angle interference range of the vehicle.

5. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 2, characterized in that: The specific process of obtaining the position state interference coefficient according to GNSS data is as follows: Acquire a cornering stiffness interference ratio and a slip angle interference ratio by combining the GNSS data and reference GNSS data obtained from a database, wherein the reference GNSS data includes a preset wheel cornering stiffness threshold and a preset rear wheel slip angle threshold; The position state interference coefficient is obtained by combining the cornering stiffness interference ratio, the sideslip angle interference ratio and the rear wheel turning angle interference ratio; The cornering stiffness interference ratio is represented by a result of a ratio calculation between the rear wheel cornering stiffness and a preset wheel cornering stiffness threshold; The sideslip angle interference ratio is represented by a result of a ratio calculation between a preset rear wheel sideslip angle threshold and the rear wheel sideslip angle.

6. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 2, characterized in that: The specific process of obtaining the mileage state interference coefficient according to the odometer data is as follows: Combining the odometer data with the reference odometer data obtained from the database to obtain the angular velocity interference ratio and the yaw angle interference ratio; The mileage state interference coefficient is obtained by combining the angular velocity interference ratio, the yaw angle interference ratio and the rear wheel turning angle interference ratio; The reference odometer data includes a preset vehicle angular velocity threshold and a preset vehicle yaw angle threshold; The yaw angle interference ratio is represented by a result of a ratio calculation between a preset vehicle yaw angle threshold and the vehicle yaw angle; The angular velocity interference ratio is obtained by the following steps: C1, obtaining the initial angular velocity by calculating the ratio of the vehicle linear velocity and the rear wheel radius, wherein the initial angular velocity is used to reflect the deviation of the rotation direction of the rear wheel of the vehicle; C2, obtaining an angular velocity interference ratio by performing ratio calculation on a preset vehicle angular velocity threshold and an initial angular velocity, wherein the angular velocity interference ratio is used to reflect the offset compliance of the rotation direction of the rear wheels of the vehicle.

7. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 1, characterized in that: The vehicle state adjustment includes vehicle adjustment and signal interference adjustment. The specific process of the vehicle adjustment is as follows: Step 1: determine whether the inertia interference coefficient, position interference coefficient and mileage interference coefficient meet condition 1. If condition 1 is met, it indicates that the vehicle is qualified, and information fusion is directly performed without vehicle adjustment. Otherwise, step 2 is executed; Step 2: Send a reminder to a preset person to adjust the tire pressure, and the tire pressure adjustment is used to prevent the tire from wearing out. When the monitored inertia interference coefficient, position interference coefficient, and mileage interference coefficient meet condition 1, stop adjusting the vehicle, otherwise, execute step 3; Step 3: Perform multi-channel data stream transmission. When the monitored inertial interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition 1, stop the vehicle adjustment. Otherwise, send an alarm prompt to the preset personnel. The multi-channel data stream transmission means that multiple independent data streams are transmitted under the same spectrum resources through a multi-input multi-output method to improve spectrum efficiency. The condition one indicates that the inertia condition interference coefficient is not higher than the preset inertia interference threshold obtained from the database, the position state interference coefficient is not higher than the preset position interference threshold obtained from the database, and the mileage state interference coefficient is not higher than the preset state interference threshold obtained from the database.

8. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 7, characterized in that: The specific process of signal interference adjustment is as follows: The first step is to perform signal separation. When the monitored inertial interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition 1, stop signal interference adjustment. Otherwise, perform the second step. The signal separation means reducing the interference of multipath effect through the mediation algorithm. The second step is to perform filtering. When the monitored inertial interference coefficient, position state interference coefficient and mileage state interference coefficient meet condition 1, stop signal interference adjustment. Otherwise, execute In the third step, the filtering process means absorbing the surge voltage generated by the preset interference device and the peak voltage of the main power supply by a filtering method; The third step is to adjust the signal range coverage. When the monitored inertial interference coefficient, position status interference coefficient and mileage status interference coefficient meet condition one, stop adjusting the signal interference. Otherwise, send an alarm to the preset personnel. The signal range coverage adjustment means improving the signal coverage by beamforming method.

9. The multi-sensor information fusion method based on rear-wheel steering vehicle as claimed in claim 1, characterized in that: The specific process of obtaining the vehicle three-dimensional velocity model based on the wheel deflection angle information is as follows: Obtaining an inertia condition interference coefficient not higher than a preset inertia interference threshold, a position state interference coefficient not higher than a preset position interference threshold, and a mileage state interference coefficient not higher than a preset state interference threshold; Inputting the obtained inertial interference coefficient not higher than the preset inertial interference threshold, the position state interference coefficient not higher than the preset position interference threshold, and the mileage state interference coefficient not higher than the preset state interference threshold into the Kalman filter for information fusion to obtain the wheel deflection angle information; A three-dimensional vehicle speed model is constructed according to the wheel deflection angle information, and the three-dimensional vehicle speed model is used to obtain the lateral speed, forward speed and vertical speed of the vehicle.

10. Based on the multi-sensor information fusion system of rear-wheel steering vehicle, it is characterized by: It includes multi-sensor data acquisition module, impact condition assessment module, vehicle status adjustment module and information fusion module: Wherein, the multi-sensor data acquisition module is used to acquire multi-sensor data, and the multi-sensor data includes IMU data, GNSS data and odometer data; The impact condition evaluation module is used to obtain an inertia condition interference coefficient according to IMU data, obtain a position state interference coefficient according to GNSS data, and obtain a mileage state interference coefficient according to odometer data. The inertia condition interference coefficient is used to evaluate the impact of rear wheel deflection on vehicle inertia at a preset time, the position state interference coefficient is used to evaluate the impact of rear wheel deflection on vehicle position at a preset time, and the mileage state interference coefficient is used to evaluate the impact of rear wheel deflection on vehicle travel distance at a preset time; The vehicle state adjustment module is used to determine whether to adjust the vehicle state based on the inertia condition interference coefficient, the position state interference coefficient and the mileage state interference coefficient; The information fusion module is used to obtain the inertia interference coefficient, position state interference coefficient and mileage state interference coefficient after the vehicle state is adjusted, and input them into the Kalman filter for information fusion to obtain the fused wheel deflection angle information, and obtain the vehicle's three-dimensional speed model based on the wheel deflection angle information.

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