DE102018000599A1 - Method and control unit for estimating the deviation of a yaw rate sensor - Google Patents

Abstract

A method (400) and control unit (310) in a vehicle (100) for estimating the deviation of a yaw rate sensor (110) on board the vehicle (100). The method (500) comprises: measuring (501) a yaw rate with the yaw rate sensor (110) during a time period (T) when passing a link (200); calculating (502) a yaw rate by integrating the (501) yaw rate measured by the yaw rate sensor (110) over the time period (T); determining (503) the vehicle direction change speed while the vehicle (100) is passing the route section (200) with a vehicle direction detecting device (120); calculating (505) a vehicle direction change of the vehicle (100); and estimating (506) the yaw rate sensor offset by comparing the calculated (502) yaw angle with the calculated (505) vehicle direction change.

Description

TECHNICAL AREA

The present document relates to a method and a control unit in a vehicle. In particular, a method and a control unit for estimating the deviation of a yaw rate sensor on board the vehicle are described.

BACKGROUND

The inertial sensors of a vehicle can be used for numerous applications, such as safety systems and driver assistance systems.

For some functions, the accuracy of the inertial sensor is not necessarily very important, for example in an electronic stability program (ESP). For other functions, however, for example, when calculating the absolute speed of an object, the longitudinal and lateral speeds of the own vehicle as well as the rotational speed, that is, the yaw rate, must be known with high accuracy in order to make calculations and predictions.

Similarly, for locating the own vehicle, the movement of the own vehicle must be known or estimated with high accuracy between points satellite reception (dead reckoning). Dead reckoning is also used in other applications, such as storing the lane of a preceding vehicle. Subsequently, the movement of the own vehicle must be considered.

In yaw rate sensors, the problem generally arises that they often have a deviation, that is, the sensor reports a small value when driving straight ahead, and thus it is expected that the yaw rate sensor reports a zero value. The reason for the deviation may be, for example, a fluctuating temperature.

One prior art method of estimating and eliminating the yaw rate sensor deviation is to pass-filter the value reported by the yaw rate sensor when it is confirmed that the vehicle is stationary, for example, when starting the vehicle. However, the vehicle can be driven between stops over a long distance, for example, in commercial long-distance vehicles with two drivers.

Another prior art method is low pass filtering the value while driving, which requires very slow filtering. A problem with this method is when roads run in the same direction for a long time, which is not uncommon on highways, because sharp turns are typically to be avoided.

The document US 2014/0163808 A1 discloses a method for detecting the deviation of a yaw rate sensor for a vehicle. The method includes tuning global positioning system (GPS) -based map information and vehicle speed information, determining the curvature of the road, and calculating a map-based yaw rate based on the steering angle information. The estimated curvature is then compared to the yaw rate determined by the yaw rate sensor. The difference between the values is considered to represent the yaw rate deviation of the yaw rate sensor.

The problem with this method is that it relies on map data, which is often incorrect, speed sensor information, which is also often incorrect, and steering angle information as determined by a steering angle sensor, which are also prone to error. Thus, the determined difference between the estimated curvature and the curvature may be a result of the speed sensor deviation, the steering angle sensor deviation, and / or map data errors.

Thus, the prior art techniques for calibration and deviation detection while driving have significant deficiencies. It would be desirable to improve the calibration of the onboard yaw rate sensor of a vehicle to further improve various calculations and predictions based on yaw rate sensor measurements.

SUMMARY

An object of the present invention is thus to solve at least some of the problems described above and to improve the yaw rate sensor measurements.

According to a first aspect of the invention, this object is achieved by a method in a vehicle for estimating the deviation of a yaw rate sensor on board the vehicle. The method includes measuring a yaw rate with the yaw rate sensor during a time period passing a link. The method further includes calculating a yaw angle by integrating the yaw rate measured by the yaw rate sensor over the time period. The method also includes determining the vehicle direction change speed while the vehicle passes the route section with a vehicle direction determination device. Further, the method also includes calculating a vehicle direction change of the vehicle. The method includes estimating the yaw rate sensor deviation by comparing the calculated yaw rate with the calculated vehicle direction change.

According to a second aspect of the invention, this object is achieved by a control unit in a vehicle. The control unit is intended to detect the deviation of a yaw rate sensor on board the vehicle. The control unit is configured to measure a yaw rate with the yaw rate sensor during a period when passing a link. The control unit is further configured to calculate a yaw rate by integrating the yaw rate measured by the yaw rate sensor over the time period. The control unit is formed with a vehicle direction detecting device in addition to determining the vehicle direction changing speed while the vehicle is passing the route section. Further, the control unit is configured to calculate a direction change of the vehicle. The control unit is in addition to detecting the yaw rate sensor deviation by comparing the calculated yaw rate with the calculated vehicle direction change.

Based on the described aspects, by measuring the yaw rate with the yaw rate sensor and comparing it to the curvature of a lane as either from a camera or through a global positioning system / digital compass on board, the yaw rate sensor can be adjusted while driving with the vehicle. Thereby, a more accurate output of the yaw rate sensor can be obtained, which leads to better forecasts in predictions / calculations in which the yaw rate of the vehicle is required.

Further advantages and additional novel features will become apparent from the following detailed description.

list of figures

• 1A zeigt ein Fahrzeug gemäß einer Ausführungsform der Erfindung.
• 1B zeigt ein Beispiel des Fahrzeugs gemäß 1A von oben betrachtet gemäß einer Ausführungsform.
• 2 zeigt ein Beispiel eines Verkehrsszenarios und eine Ausführungsform der Erfindung.
• 3 zeigt ein Beispiel eines Fahrzeuginneren gemäß einer Ausführungsform.
• 4 zeigt ein eine Straßenkurvenüberhöhung passierendes Fahrzeug von hinten betrachtet gemäß einer Ausführungsform.
• 5 zeigt ein Fließbild zur Darstellung einer Ausführungsform des Verfahrens.
• 6 zeigt eine Darstellung eines Systems gemäß einer Ausführungsform.

Hereinafter, embodiments of the invention will be described in detail with reference to the accompanying drawings.

• 1A shows a vehicle according to an embodiment of the invention.
• 1B shows an example of the vehicle according to 1A viewed from above according to an embodiment.
• 2 shows an example of a traffic scenario and an embodiment of the invention.
• 3 shows an example of a vehicle interior according to an embodiment.
• 4 FIG. 10 shows a vehicle passing a road curve elevation as viewed from behind according to one embodiment. FIG.
• 5 shows a flowchart illustrating an embodiment of the method.
• 6 shows an illustration of a system according to an embodiment.

DETAILED DESCRIPTION

Embodiments of the invention as described herein are defined as a method and a control unit that may be implemented in the embodiments described below. However, these embodiments may be embodied and practiced in many different forms and are not limited to the examples described herein; These illustrative examples of embodiments are embodied to make the disclosure complete and complete.

Other objects and features will become apparent from the following detailed description when taken in conjunction with the accompanying drawings. The drawings are for purposes of illustration only, and not as a definition of the limits of the embodiments disclosed herein, with reference to the appended claims. Furthermore, the drawings are not necessarily to scale and are merely illustrative of the structures and methods described herein.

1A shows a scenario with a vehicle 100. The vehicle 100 travels on a road in a direction of travel 105.

The vehicle 100 may include, for example, a truck, a bus, a car, a motorcycle, an airplane, a watercraft, a train, a spacecraft, or any similar vehicles or other means of transportation.

The vehicle 100 may be driver-controlled or driverless (ie, autonomously controlled) in various embodiments. For simplicity, the vehicle 100 will be described below as having a driver.

The vehicle 100 includes a yaw rate sensor 110. The yaw rate sensor 110 is a gyroscope device that measures the angular velocity of the vehicle about its vertical axis. The angle between the heading of the vehicle and the actual direction of movement of the vehicle is called the slip angle associated with the yaw rate. The yaw rate sensor 110 may include a piezoelectric type or a micromechanical type in various embodiments.

In the piezoelectric embodiment of the yaw rate sensor 110, the yaw rate sensor 110 is a tuning fork-shaped structure having four piezo elements (two at the top and two at the bottom). When driving straight ahead, the upper ones generate no tension, because no Coriolis force acts. When cornering, the rotational motion causes the top of the tuning fork to leave the vibration plane, producing an AC voltage that is proportional to the yaw rate and vibration velocity. The sign of the output signal depends on the direction, that is left or right.

In the micromechanical embodiment of the yaw rate sensor 110, the Coriolis acceleration is measured by a micromechanical capacitive acceleration sensor arranged on a vibration element. This acceleration is proportional to the product of yaw rate and vibration velocity, which is kept electronically at a constant value.

Frequently, sensors with a micro-electro-mechanical system (MEMS) can be used as the yaw rate sensor 110 in the vehicle 100. However, such MEMS yaw rate sensors may have a bias due to thermal disturbances and, optionally, other factors that may vary over time. Thus, it would be an advantage to be able to determine the yaw rate deviation of the sensor 110 repeatedly, on a regular basis or continuously (again).

The yaw rate sensor 110 is used in the vehicle 100 for various applications in the vehicle 100 as previously discussed in the Background section.

The vehicle 100 may include a travel direction changing device 120. In some embodiments, the vehicle direction changing device 120 may include a global satellite navigation system or digital compass device, as discussed below in connection with FIG 3 described. In the in 1A In the illustrated embodiment, the vehicle direction determination device 120 includes a forward-looking sensor, such as a camera. A common concept of the various embodiments disclosed herein is that a deviation of the yaw rate sensor 110 is estimated by measuring the yaw rate with the yaw rate sensor 110, calculating a heading change of the vehicle 100, and calculating a difference between the corresponding values. If the difference between them exceeds a threshold limit, the yaw rate sensor 110 may be adjusted by the calculated difference.

The vehicle direction determination device 120 in FIG. 1A, that is, the forward-looking sensor 120, may be configured to capture images of the vehicle surroundings in front of the vehicle 100 and thereby determine the road curvature, in some embodiments. For example, the vehicle direction determining device 120 / sensor may detect and detect images of road markings.

For example, in the illustrated embodiment, which is merely an arbitrary example, the forward vehicle direction sensing device 120 / sensor may be located at the front of the vehicle 100, behind the windshield of the vehicle 100.

Mounting the forward vehicle direction sensing device 120 / the sensor behind the windshield offers some advantages over externally mounted sensor systems. These advantages include the ability to use windshield wipers to clean and use the light from headlamps to illuminate objects in the field of view of the sensor. It is also protected from dirt, snow, rain and, to some extent, from damage, vandalism and / or theft. Such a vehicle direction determination device 120 / such a sensor may also be used for a variety of other tasks, such as for a lane departure warning system.

The vehicle direction determination device 120 / the sensor may be directed to the front of the vehicle 100 in the direction of travel 105. The vehicle direction determination device 120 / sensor may include, for example, a camera, a stereo camera, an infrared camera, a video camera, a radar, a lidar, an ultrasound device, a runtime camera, or similar device in various embodiments.

By comparing the yaw rate of the yaw rate sensor 110 to a curvature determined in a reliable manner, that is, a less error prone manner than the yaw rate sensor 110, the yaw rate sensor 110 can be calibrated while traveling with the vehicle 100, for example on a regular basis at predetermined time intervals. This allows better predictions to be made based on vehicle yaw rate.

1B shows the previously in 1A illustrated vehicle 100 viewed from above according to one embodiment.

2 shows an example of a traffic scenario and an embodiment of the invention. The vehicle 100 passes through a section 200 comprising a bend. In the presentation of 2 For example, the vehicle 100 is shown in a first position A at t = 0 and a second position B at t = T.

The vehicle direction determination device 120 / sensor may, in some embodiments, be rotated and / or changed in different directions. The vehicle direction determination device 120 / sensor may estimate a polynomial or a clothoid from the detected lane markers. A clothoid directly contains curvature information and the curvature can be easily extracted from a polynomial track model by the following formula: $$c=\frac{2*d{y}^2\text{/}d{x}^2}{{\left(1+dy\text{/}dx\right)}^{3\text{/}2}}$$

By accumulating the difference between the measured integrated yaw rate γ _meas and the integrated course change of the road γ _road , the deviation of the yaw rate sensor 110 can be calculated if accumulation on the same lane occurs over a longer period of time (~ 60 sec).

The lane markers on the road may be detected by the vehicle direction detecting device 120 / sensor. Furthermore, the lane markings can be interpreted by image recognition. The vehicle direction determination device 120 / the sensor may thus include or be connected to a control unit designed for image recognition / machine image and object recognition.

Machine vision is a technical field comprising methods for acquiring, processing, analyzing and understanding images, and generally high-dimensional data from the real world for generating numerical or symbolic information. One theme in the development of this field has been copying the abilities of human seeing through electronic perception and understanding of a picture. Understanding in this context means transforming visual images (the retina input) into descriptions of the world, which can be linked to other thought processes and generated actions. This understanding of images can be viewed as unraveling symbolic information from image data using models generated with the aid of geometry, physics, statistics, and learning theory. Machine vision can also be the task of automating and integrating a wide variety of processes and representations for visual perception.

The vehicle direction detector 120 / sensor image data may take many forms, such as images, video sequences, multiple camera views, or multi-dimensional data from a scanner.

The following steps may, in some embodiments, be performed during the calculation of the deviation by comparing the yaw rate with the sensor data: If the vehicle is relatively centered in the lane and the speed is relatively high, the initial heading to the road β (0) may be stored. Furthermore, the measured yaw angle can be integrated with the yaw rate sensor: $${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas},}$$

The road curvature can be integrated over the same route: $${\gamma }_{rOad}={\displaystyle {\int }_{s=0}^{s=S}{c}_{rOad}ds}={\displaystyle {\int }_{t=0}^{t=\tau }\left(v_x\left(t\right)*\text{cos}\left(\beta \left(t\right)\right)-v_y\left(t\right)\text{sin}\left(\beta \left(t\right)\right)*c_{rOad}\left(t\right)\right)dt.}$$

Where β is the course of the carrier vehicle to the road. If the measurement ends at position B, the final price for the road is saved; β (T) is stored.

If (T> T _min ) and the end position in the track at position B is relatively centered, the deviation angle is calculated as follows: $$\begin{array}{l}{\omega }_{bias}=\left(\frac{1}{T}\right)({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt}-\left(\beta \left(T\right)-\beta \left(0\right)\right)-{\displaystyle {\int }_{t=0}^{t=T}(v_x\left(t\right)*\text{cos}}\left(\beta \left(t\right)\right)-\\ v_y\text{sin}\left(\beta \left(t\right)\right)*c_{rOad}\left(t\right))dt),\end{array}$$

The integration may also be terminated when T> T _max , and a new integration may commence according to some embodiments if the basic requirements are still met.

3 FIG. 12 shows an example of a vehicle interior of the vehicle 100 as may be perceived by the driver of the vehicle 100.

The vehicle 100 includes a control unit 310 in which various calculations can be performed in estimating the deviation of the yaw rate sensor 110.

The geographic location of the vehicle 100 may be determined by the vehicle direction determination device 120 / positioning unit in the vehicle 100 operating on a satellite navigation system such as Navigation Signal Timing and Ranging (Navstar) Global Positioning System (GPS), Differential GPS (DGPS), Galileo, Glonass o. Ä. can be based.

The geographic location of the vehicle direction sensing device 120 / positioning unit (and thus also the vehicle 100) may be continuously determined at certain predetermined or configurable time intervals according to various embodiments.

Positioning by satellite navigation is based on a triangulation distance measurement of a number of satellites 320-1, 320-2, 320-3, 320-4. In this example, four satellites 320-1, 320-2, 320-3, 320-4 are shown; but this is just an example. More than four satellites 320-1, 320-2, 320-3, 320-4 can be used to improve accuracy or provide redundancy. The satellites 320-1, 320-2, 320-3, 320-4 continuously transmit information about time and data (for example in coded form), identity (which satellite 320-1, 320-2, 320-3, 320-4 status) and where the satellite 320-1, 320-2, 320-3, 320-4 is at that time. The GPS satellites 320-1, 320-2, 320-3, 320-4 transmit coded information using various codes, for example Code Division Multiple Access (CDMA). This allows distinguishing information from a single satellite 320-1, 320-2, 320-3, 320-4 from each other's information based on a unique code for each corresponding satellite 320-1, 320-2, 320-3 , 320-4. This information may then be sent to be received by the appropriately configured positioning unit in the vehicle 100.

The distance measurement may include measuring the difference in the time taken by the satellite signal transmitted by the corresponding satellite 320-1, 320-2, 320-3, 320-4 to reach the vehicle direction determination device 120 / positioning unit, in accordance with some embodiments. Since radio signals are transmitted at the speed of light, the distance to the respective satellite 320-1, 320-2, 320-3, 320-4 can be calculated by measuring the signal propagation time.

The positions of satellites 320-1, 320-2, 320-3, 320-4 are known as they are continuously monitored by about 15-30 ground stations located substantially along or near the earthquake. Thereby, the geographical position, ie latitude and longitude, of the vehicle 100 can be calculated by determining the distance to at least three satellites 320-1, 320-2, 320-3, 320-4 by triangulation. To determine the altitude, signals from four satellites 320-1, 320-2, 320-3, 320-4 may be used, in accordance with some embodiments.

After determining the geographic location of the vehicle direction determination device 120 / positioning unit (or otherwise), the direction of the vehicle 100 may be determined while driving.

Alternatively or additionally (for possible redundancy), the change in course of the road can be performed with a digital on-board compass such as a magnetometer, a fiber optic gyro or the like. be measured.

The rate change over the integrated period can then be derived from the change in price at the time the measurement starts at t = 0 until the end of the measurement at t = T.

Thereafter, the following steps may be performed: If the vehicle speed is relatively high, the initial heading may be stored to a global landmark (eg, north) θ (0). The measured yaw angle relative to the global coordinates is integrated with the yaw rate sensor: $${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}dt,}$$

Upon completion of the measurement, the final global landmark mark can be stored at t = T, θ (T). If (T> T _min ), the deviation angle can be calculated as follows: $${\omega }_{bias}=+\left(\frac{1}{T}\right)\left({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}dt-\left(\theta \left(T\right)-\theta \left(0\right)\right)}\right)=\left(\frac{1}{T}\right)({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt-\left(\mathrm{\Delta }{\mathrm{<figure-callout\; id="0"\; label="\theta "\; filenames="DE102018000599A1\_0025.png"\; state="\{\{state\}\}">\theta </figure-callout>}}_{0\to T}\right)}$$

The control unit 310 may communicate with the vehicle direction determining device 120 / positioning unit and the yaw rate sensor 110 via, for example, a wired or wireless communication bus. For example, the communication bus may include a Controller Area Network (CAN) bus, a Media Oriented Systems Transport (MOST) bus, or the like. include. Alternatively, the communication may include a wireless connection, or at least based on any wireless communication technologies such as Wi-Fi, Wireless Local Area Network (WLAN), Ultra Mobile Broadband (UMB), Bluetooth (BT), Near Field Communication (NFC), Radio -Frequency Identification (RFID), Z-wave, ZigBee, IPv6 over low power Wireless Personal Area Network (6LoWPAN), Wireless Highway Addressable Remote Transducer (HART) protocol, Wireless Universal Serial Bus (USB), one optical communication such as Infrared Data Association (IrDA) or infrared transmission, to name but a few possible examples of wireless communication in some embodiments.

4 FIG. 12 shows a vehicle 100 that is passing a turn having a road curve bank 400. The road curve superelevation 400 has an angle α.

In some embodiments, an approximation may be made such that the road curve peak 400 is zero or negligible when comparing the angle of rotation to a global reference, that is, the effects of a curve 400 road are ignored. Alternatively, the calculation may be aborted when a significant road curve overshoot 400 is detected. If the road curve overshoot 400 can be measured with sufficient accuracy, according to some embodiments, the effects may be taken into account with additional conversions and calculations.

The road curve overshoot 400 may be determined by measuring the lateral acceleration with a sensor in some embodiments. In other embodiments, the size of the road curve overshoot may be stored in a database associated with a geographic location. By determining the geographic location of the vehicle 100, in some embodiments, the magnitude of the road curve overshoot 400 may be extracted.

When the curvature of the road is detected by the sensor 120, the curvature at the level of the road surface 400 is determined irrespective of the size of the road surface superelevation 400.

According to some embodiments, a measurement may be made if T> T _min for the current measurement method, and another weighted accumulation or low pass filtering is performed to calculate a final value of multiple measurement events; or it became a measurement by another method such as a standstill deviation estimation.

wobei w(n) ein mit dem Messverfahren ermitteltes Gewicht ist. Typischerweise würde sich eine Stillstandsmessung als ein höheres Gewicht qualifizieren als eine Qualifizierung auf der Straße, da sie eine kürzere Zeit zum Kalibrieren erfordert.An example of the final yaw rate deviation can be described with the following accumulation:

where w (n) is a weight determined by the measuring method. Typically, a standstill measurement would qualify as a higher weight than a road qualification because it requires a shorter time to calibrate.

Outliers can be filtered out and rejected as needed when a number of measurements have been taken.

5 FIG. 12 shows an example of a method 500 according to one embodiment. The flow chart in 5 FIG. 5 shows the method 500 for use in a vehicle 100. The method 500 is for estimating the deviation of a yaw rate sensor 110 onboard the vehicle 100.

The vehicle 100 may be, for example, a truck, a bus, a passenger car, or similar conveyance.

The yaw rate sensor deviation may be accomplished by measuring the yaw rate with the yaw rate sensor 110 and estimating the yaw rate by another measurement method, such as by a vehicle direction determination device 120. The vehicle direction determination device 120 may include a global satellite navigation system or digital compass device in some embodiments. In other embodiments, the vehicle direction determination device 120 may include a camera.

To properly detect and estimate the yaw rate sensor deviation, the method 500 may include a number of steps 501-508. Some of these steps 501-508 may be performed in various alternative ways. Some method steps, such as steps 504 and 507-508, may be performed only on some optional embodiments. Further, the described steps 501-508 may be performed in a different chronological order than the numbering indicates. The method 500 may include the following steps:

Step 501 includes measuring a yaw rate with the yaw rate sensor 110 during a time T as it passes a link 200.

In some embodiments, the yaw rate may be measured for a time period exceeding a minimum time limit, with no maximum time limit exceeded.

Step 502 includes calculating a yaw rate by integrating the 501 yaw rate measured by yaw rate sensor 110 over time T.

wobei γ_meas die gesamte gemessene Änderung im Gierwinkel ist, ω_meas(t) die gemessene Gierrate ist, 0 der Zeitpunkt ist, zu dem das Fahrzeug 100 in den Streckenabschnitt 200 einfährt, und T der Zeitpunkt ist, zu dem das Fahrzeug 100 den Streckenabschnitt 200 verlässt.In some embodiments, the yaw angle may be calculated by integration: ${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt;}$

where γ _{meas is} the total measured change in yaw angle, ω _meas (t) is the measured yaw rate, 0 is the time at which the vehicle 100 enters the link 200, and T is the time at which the vehicle 100 reaches the link 200 leaves.

wobei γ_meas der Gierwinkel ist, ω_meas eine gemessene Gierrate ist, 0 der Zeitpunkt ist, zu dem das Fahrzeug 100 in den Streckenabschnitt 200 einfährt, und T der Zeitpunkt ist, zu dem das Fahrzeug 100 den Streckenabschnitt 200 verlässt.In some embodiments, the yaw angle may be calculated by integration, where the vehicle direction determination device 120 may include a global satellite navigation system or a digital compass device: ${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt;}$

where γ _{meas is} the yaw rate, ω _{meas is} a measured yaw rate, 0 is the time at which the vehicle 100 enters the link 200, and T is the time at which the vehicle 100 leaves the leg 200.

Step 503 includes determining the vehicle direction change speed while the vehicle 100 is passing the route section 200 with a vehicle direction determination device 120.

The vehicle direction change speed may be determined in some embodiments based on the road curvature of the road section 200, by detecting lane markers of the road section 200 and the vehicle speed of the vehicle 100 while passing the road section 200 as from a tachometer of the vehicle 100, the vehicle direction determination device 120 comprising a camera may include.

In some embodiments, as the vehicle 100 passes the route section 200, the vehicle direction velocity may be determined by determining a change in heading Δθ _{0 → T} over the link 200, where the vehicle direction determination device 120 may include a global satellite navigation system or compass.

The vehicle direction change speed may, in some embodiments, be determined for a time period exceeding a minimum time limit, wherein no maximum time limit is exceeded.

Step 504, which may be included only in some alternative embodiments, includes estimating the road curve cant 400 of the link 200.

In some alternative embodiments, the vehicle direction change rate is determined 503 taking into account the estimated 504 road curve overshoot 400.

wobei (t) der Kurs des Trägerfahrzeugs zum Streckenabschnitt ist, ν_x(t) die Fahrzeuglängsgeschwindigkeit des Fahrzeugs 100 ist, ν_y(t) die Fahrzeugseitengeschwindigkeit des Fahrzeugs 100 ist und c_road die Krümmung der Straße wie von einem Sensor 120 zum Messen von Straßenmarkierungen ist.The vehicle direction change may be calculated by integration over time T in some embodiments: ${\displaystyle {\int }_{t=0}^{t=T}\left(v_x\left(t\right)*\text{cos}\left(\beta \left(t\right)\right)-v_y\left(t\right)\text{sin}\left(\beta \left(t\right)\right)*c_{rOad}\left(t\right)\right)dt;}$

where (t) the course of the host vehicle is to the span, ν _x (t) is the vehicle longitudinal speed of the vehicle 100, ν _y (t) is the vehicle side speed of the vehicle 100, and c _{road is} the curvature of the road as measured by a sensor 120 Road markings is.

Step 505 includes a vehicle direction change of the vehicle 100.

The vehicle direction change may, in some embodiments, be calculated by integrating the determined 503 vehicle direction change rate over the time period T, wherein the vehicle direction determination device 120 may include a camera.

Step 506 includes estimating the yaw rate sensor offset by comparing the calculated 502 yaw angle with the calculated 505 vehicle direction change.

The yaw rate sensor deviation, ω _bias , may be estimated in some embodiments by: $$\begin{array}{l}{\omega }_{bias}=\left(\frac{1}{T}\right)({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt}-\left(\beta \left(T\right)-\beta \left(0\right)\right)-{\displaystyle {\int }_{t=0}^{t=T}(v_x\left(t\right)*\text{cos}}\left(\beta \left(t\right)\right)-\\ v_y\text{sin}\left(\beta \left(t\right)\right)*c_{rOad}\left(t\right))dt,\end{array}$$

wobei T der Zeitpunkt ist, zu dem das Fahrzeug 100 den Streckenabschnitt 200 verlässt.Alternatively, in some embodiments, the yaw rate sensor deviation, ω _bias , may be estimated by: $${\omega }_{bias}=\left(\frac{1}{T}\right)\left({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt-(\mathrm{\Delta }{\mathrm{<figure-callout\; id="0"\; label="\theta "\; filenames="DE102018000599A1\_0025.png"\; state="\{\{state\}\}">\theta </figure-callout>}}_{0\to T}}\right).$$

where T is the time when the vehicle 100 leaves the route section 200.

wobei W(n) ein Gewicht 0 ≤ W(n) ≤ 1, ermittelt durch das verwendete Messverfahren, ist.Step 507, which may be included only in some alternative embodiments, includes combining a plurality of yaw rate sensor deviations based on the (503) vehicle direction change rate determined at different times and / or by using different methods and by stall error estimation:

where W (n) is a weight 0 ≦ W (n) ≦ 1, determined by the measuring method used.

Step 508, which may be included only in some alternative embodiments, includes adjusting the yaw rate sensor 110 with the estimated yaw rate sensor deviation. The yaw rate sensor 110 may thereby be calibrated.

6 1 shows an embodiment of a system 600 in a vehicle 100 for detecting the deviation of a yaw rate sensor 110 aboard the vehicle 100. The system 600 may perform at least some of the previously described steps 501-508 according to what has been described above and in US Pat 5 perform method 500 illustrated.

The system 600 includes at least one control unit 310 in the vehicle 100 for detecting the deviation of the yaw rate sensor 110 aboard the vehicle 100. The control unit 310 is configured to measure a yaw rate with the yaw rate sensor 110 during a time T as it passes a link 200. Further, the control unit 310 is configured to calculate a yaw rate by integrating the yaw rate measured by the yaw rate sensor 110 over the time period T. The control unit 310 is also configured to determine the vehicle direction change speed while the vehicle 100 passes the route section 200 with a vehicle direction determination device 120. The control unit 310 is further configured to calculate a vehicle direction change of the vehicle 100. The control unit 310 is formed in addition to detecting the yaw rate sensor deviation by comparing the calculated yaw rate with the calculated vehicle direction change.

In some embodiments, where the vehicle direction determination device 120 includes a camera, the control unit 310 may further determine the vehicle direction change speed based on the curvature of the road section 200, detecting lane markers of the road section 200, and the measured vehicle speed of the vehicle 100 while passing the road section 200 as determined by a speedometer of the vehicle 100 may be formed. The control unit 310 may be further configured to calculate the vehicle direction change of the vehicle 100 by integrating the determined vehicle direction change speed over the time period T.

wobei γ_{ms as} der Gierwinkel ist, ω_{ms as} eine gemessene Gierrate ist, 0 der Zeitpunkt ist, zu dem das Fahrzeug 100 in den Streckenabschnitt 200 einfährt, und T der Zeitpunkt ist, zu dem das Fahrzeug 100 den Streckenabschnitt 200 verlässt. Die Steuereinheit 310 kann ebenfalls zum Erfassen der Gierratensensor-Abweichung, ω_bias, ausgebildet sein durch: $${\omega }_{bias}=\left(\frac{1}{T}\right)\left({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt-(\mathrm{\Delta }{\theta }_{0\to T}}\right),$$

wobei T der Zeitpunkt ist, zu dem das Fahrzeug 100 den Streckenabschnitt 200 verlässt.In some embodiments, where the vehicle direction determination device 120 includes a global navigation satellite system or a digital compass, the control unit 310 may determine the vehicle direction change speed while the vehicle 100 is passing the route section 200 by determining an initial heading .theta. (0) on entering the Track section 200 and determining a final course θ (T) when leaving the track section 200 may be formed. Furthermore, the control unit 310 for calculating the yaw angle may be formed by integration: ${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right);}$

where γ _{ms as is} the yaw angle, ω _{ms as is} a measured yaw rate, 0 is the time when the vehicle 100 enters the link 200, and T is the time when the vehicle 100 leaves the link 200. The control unit 310 may also be configured to detect the yaw rate sensor deviation, ω _bias , by: $${\omega }_{bias}=\left(\frac{1}{T}\right)\left({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt-(\mathrm{\Delta }{\mathrm{<figure-callout\; id="0"\; label="\theta "\; filenames="DE102018000599A1\_0025.png"\; state="\{\{state\}\}">\theta </figure-callout>}}_{0\to T}}\right).$$

where T is the time when the vehicle 100 leaves the route section 200.

The control unit 310 includes a reception circuit 610 configured to receive a signal from the yaw rate sensor 110 and the vehicle direction determination device 120.

Further, the control unit 310 includes a processor 620 configured to perform at least some steps of the previously described method 500, in accordance with some embodiments.

Such a processor 620 may include one or more objects of a processing circuit, that is, a central processing unit (CPU), a processing unit, a processing circuit, an application specific integrated circuit (ASIC), a microprocessor, or other processing logic Interpret and execute statements. The term "processor" as used herein may thus represent a processing circuit comprising a plurality of processing circuits, such as any, some or all of the aforementioned.

Further, in some embodiments, the controller 310 may include a memory 625. The optional memory 625 may include a physical device for temporarily or permanently storing data or programs, that is, sequences of instructions. According to some embodiments, the memory 625 may include integrated circuits including silicon-based transistors. The memory 625 may include, for example, a memory card, a flash memory, a USB memory, a hard disk, or other similar volatile or non-volatile memory unit for storing data such as ROM (Read-Only Memory), PROM (Programmable Read-Only Memory). , EPROM (Erasable PROM), EEPROM (Electrically Erasable PROM) etc. in various embodiments.

Further, in some embodiments, the controller 310 may include a signal transmitter 630. The signal transmitter 630 may be configured to send a signal to the yaw rate sensor 110, that is, a command to adjust the sensor 110 with the detected deviation, and / or to a display device such as a display, a speaker, etc. to instruct the driver about the detected yaw rate sensor signal. Deviation be formed.

Further, the system 600 also includes at least one yaw rate sensor 110 for determining the yaw rate of the vehicle 100.

The system 600 further includes a vehicle direction determination device 120. The vehicle direction determination device 120 may include, for example, a camera, a stereo camera, an infrared camera, a video camera, a radar, a lidar, an ultrasonic sensor, a time-of-flight camera, a thermal camera, or the like. include. The vehicle direction determination device 120 may alternatively or additionally comprise a device for a global navigation satellite system or a digital compass.

The vehicle direction determining device 120 used to perform at least a portion of the method 500 may, in some embodiments, have a main purpose other than performing the method 500, that is, already in the vehicle 100.

The above described method steps 501-508 for execution in the vehicle 100 may be performed by the one or more processors 620 in the control unit 310 along with a computer program product for performing at least some of the functions of steps 501-508. Thus, a computer program product comprising instructions for performing steps 501-508 in the control unit 310 may execute the method 500 including at least some of the steps 501-508 for detecting the deviation of the yaw rate sensor 110 aboard the vehicle 100 when the computer program enters one or more of the steps a plurality of processors 620 of the control unit 310 is loaded.

Further, some embodiments of the invention may include a vehicle 100 including the control unit 310 for detecting and tracking an object 200 according to at least some of the method steps 501-508.

The computer program product described above may be provided, for example, in the form of a computer program code carrying medium for performing at least some of the steps 501-508 according to some embodiments when loading into the one or more processors 620 of the control unit 310. The data carrier may be, for example, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, or any other suitable medium, such as a disk or tape, that can store machine-readable data in a non-volatile manner. The computer program product may also be provided as computer program code on a server and downloaded remotely to the control unit 310, for example via an internet or intranet connection.

The terminology used in the description of the embodiments as illustrated in the accompanying drawings is not intended to limit the described method 500, the control unit 310, the computer program, the system 600, and / or the vehicle 100. Various changes and / or changes may be made without departing from the inventive embodiments as defined in the appended claims.

As used herein, "and / or" includes any and all combinations of one or more of the corresponding listed items. The term "or" as used herein is to be understood as a mathematical OR, that is, as an inclusive disjunction, rather than as a mathematical exclusive OR (XOR) unless expressly stated otherwise. Further, the singular forms "a", "an", "an" and "the", "the", "the" are to be understood as "at least one" and thus also possibly include a plurality of elements of the same kind unless expressly stated otherwise. It is also to be understood that the terms "comprises" and / or "comprising" refer to the presence of said features, actions, integers, steps, acts, elements and / or components, but not the presence or addition of one or more others Exclude features, actions, integers, steps, operations, items, components, and / or groups thereof. A single unit, such as a processor, may perform the functions of several elements mentioned in the claims. The mere fact that certain measures are mentioned in various dependent claims does not indicate that a combination of these measures can not be used to advantage. A computer program may be stored / distributed on a suitable medium such as an optical storage medium or a solid-state medium provided with or as part of other hardware, but may also be in other forms such as over the Internet or another wired one or wireless communication system.

Claims (13)

A method (500) in a vehicle (100) for estimating the deviation of a yaw rate sensor (110) on board the vehicle (100), the method (500) comprising: measuring (501) a yaw rate with the yaw rate sensor (110) during a time period (T) when passing a link (200); calculating (502) a yaw rate by integrating the (501) yaw rate measured by the yaw rate sensor (110) over the time period (T); determining (503) the vehicle direction change speed while the vehicle (100) is passing the route section (200) with a vehicle direction detecting device (120); calculating (505) a vehicle direction change of the vehicle (100); and estimating (506) the yaw rate sensor offset by comparing the calculated (502) yaw angle with the calculated (505) vehicle direction change. Method (500) Claim 1 wherein the vehicle direction determining device (120) comprises a camera; and wherein the vehicle direction change speed is determined on the basis of the road curvature of the road section (200) by detecting lane markers of the road section (200) and the vehicle speed of the vehicle (100) while passing the road section (200) as determined by the vehicle's tachometer (100) (503) becomes; and wherein the vehicle direction change is calculated (505) by integrating the determined (503) vehicle direction change rate over the time period (T). Method (500) Claim 1 or 2 wherein: the yaw angle is calculated by integrating (502): ${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt;}$

where γ _{ma as is} the total measured change in yaw angle, ω _msas (t) is the measured yaw rate, 0 is the time at which the vehicle (100) enters the link (200), and T is the time at which the vehicle (100) leaves the route section (200); the vehicle direction change calculated by integrated over the period (T) is (504): $${\gamma }_{rOad}={\displaystyle {\int }_{t=0}^{t=T}\left(v_x\left(t\right)*\text{cos}\left(\beta \left(t\right)\right)-v_y\left(t\right)\text{sin}\left(\beta \left(t\right)\right)*c_{rOad}\left(t\right)\right)dt;}$$

where (t) the course of the host vehicle is to the span, ν _x (t) is the vehicle longitudinal speed of the vehicle (100), ν _y (t) is the vehicle side speed of the vehicle (100), and c _{road is} the curvature of the road as from a sensor (120) for measuring road markings; and wherein the yaw rate sensor deviation, (ω _bias ) is estimated (506) by: $$\begin{array}{l}{\omega }_{bias}=\left(\frac{1}{T}\right)({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt}-\left(\beta \left(T\right)-\beta \left(0\right)\right)-{\displaystyle {\int }_{t=0}^{t=T}(v_x\left(t\right)*\text{cos}}\left(\beta \left(t\right)\right)-\\ v_y\text{sin}\left(\beta \left(t\right)\right)*c_{rOad}\left(t\right))dt)\end{array}$$

Method (500) Claim 1 wherein the vehicle direction determining device (120) comprises a global navigation satellite system or a digital compass; and wherein the vehicle direction change speed as the vehicle (100) passes the link (200) is determined (503) by determining a change in the heading Δθ _{0 → T} over the link (200); the yaw angle calculated by integrated (502) is: ${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt;}$

where γ _{ms as is} the yaw angle, ω _{msas is} a measured yaw rate, 0 is the time at which the vehicle (100) enters the link (200), and T is the time at which the vehicle (100) reaches the link (200) leaves; and the yaw rate sensor deviation, ω _bi-as , estimated (506) is given by: $${\omega }_{bias}=\left(\frac{1}{T}\right)\left({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt-}\left(\mathrm{\Delta }{\theta }_{0\to T}\right)\right).$$

where T is the time at which the vehicle (100) leaves the route section (200). Method (500) Claim 3 or 4 , further comprising: estimating (504) the road curve cant (400) of the link (200); and wherein the vehicle direction change speed is determined (503) in consideration of the estimated (504) road surface overshoot (400). Method (500) according to one of Claims 1 - 5 wherein the yaw rate is measured (501) and the vehicle direction change rate is determined (503) becomes for a time period exceeding a minimum time limit, while a maximum time limit is not exceeded. Method (500) according to one of Claims 1 - 6 , further comprising: combining (507) a plurality of yaw rate sensor deviations based on at different times and / or using different methods of any one of Claims 1 - 6 and (503) vehicle direction change rate determined by standstill deviation estimation:

where W (n) is a weight 0 <W (n) ≦ 1, determined by the measuring method used. Method (500) according to one of Claims 1 - 7 further comprising: adjusting (508) the yaw rate sensor (110) with the estimated (506) yaw rate sensor offset. A control unit (310) in a vehicle (100) for detecting the deviation of a yaw rate sensor (110) on board the vehicle (100), wherein the control unit (310) is configured to: measure a yaw rate with the yaw rate sensor (110) during a time period ( T) when passing a section of road (200); Calculating a yaw rate by integrating the yaw rate measured by the yaw rate sensor (110) over the time period (T); Determining the vehicle direction change speed while the vehicle (100) is passing the route section (200) with a vehicle direction detecting device (120); Calculating a vehicle direction change of the vehicle (100); and detecting the yaw rate sensor deviation by comparing the calculated yaw rate with the calculated vehicle direction change. Control unit (310) after Claim 9 wherein the vehicle direction determining device (120) comprises a camera; and wherein the control unit (310) further determines the vehicle direction change speed based on the road curvature of the link section (200) by detecting lane markers of the link section (200) and the measured vehicle speed of the vehicle (100) while passing the link section (200) is determined by the speedometer of the vehicle (100) is determined; and wherein the vehicle direction change of the vehicle (100) is calculated by integrating the determined vehicle direction change speed over the time period (T). Control unit (310) after Claim 9 wherein the vehicle direction determining device (120) comprises a global navigation satellite system or a digital compass; and wherein the control unit (310) for determining the vehicle direction change speed while the vehicle (100) is passing the route section (200) by determining an initial heading θ (0) on entering the route section (200) and determining a final course θ (T) at Leaving the track section (200) is formed; the yaw angle is calculated by integrating: ${\gamma }_{meas}={\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas};}$

where γ _{ms as is} the yaw angle, ω _{ms · as is} a measured yaw rate, 0 is the time at which the vehicle (100) enters the link (200), and T is the time at which the vehicle (100) leaves the route section (200); and wherein the control unit (310) is further configured to detect the yaw rate sensor deviation, ω _bias , by: $${\omega }_{bias}=\left(\frac{1}{T}\right)\left({\displaystyle {\int }_{t=0}^{t=T}{\omega }_{meas}\left(t\right)dt-\left(\mathrm{\Delta }{\theta }_{0\to T}\right)}\right).$$

where T is the time at which the vehicle (100) leaves the route section (200). Computer program comprising program code for carrying out a method (500) according to one of Claims 1 - 8th when the computer program is executed in a control unit (310), according to one of Claims 9 - 11 , A system (600) for detecting the deviation of a yaw rate sensor (110) onboard the vehicle (100), the system (600) comprising: a controller (310) Claim 9 - 11 ; a yaw rate sensor (110); and a vehicle direction detecting device (120).

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