WO2019211052A1

WO2019211052A1 - Method for determining a vehicle position

Info

Publication number: WO2019211052A1
Application number: PCT/EP2019/058143
Authority: WO
Inventors: Ulrich STÄHLIN
Original assignee: Continental Teves Ag & Co. Ohg
Priority date: 2018-05-04
Filing date: 2019-04-01
Publication date: 2019-11-07
Also published as: DE102018206956A1; DE112019000970A5

Abstract

The invention relates to a method for determining the position and/or movement of a vehicle, the vehicle comprising an electronic processing device (20), a status detector (10), and at least one sensor for detecting measured values relating to driving dynamics and for outputting sensor data, - the electronic processing device (20) determining a position and/or movement of the vehicle from the sensor data based on a driving dynamics model, characterised in that - the status detector (10) detects a driving status (1) of the vehicle and - depending upon the detected driving status (1) the processing device (20) uses a first or a second driving dynamics model (21, 22) to determine the position and/or movement of the vehicle.

Description

description

Method for determining a vehicle position

The invention relates to a method for determining the position and / or movement of a vehicle and an associated electronic control device.

For determining the position and / or movement of a vehicle, various approaches are known from the prior art. For example, by means of satellite navigation, also called Global Navigation Satellite System, GNSS for short, a vehicle can be located via a corresponding receiver. Likewise, the odometry of the vehicle can be used. For example, can be used for odometric measurements, the pulses of the wheel angle encoder of all wheels of a vehicle and the wheel steering angle and derive, for example, a longitudinal speed and a rotation angle about the vertical axis. Acceleration and rotation rate sensors, for example an inertial measurement unit, can be used in a so-called strapdown algorithm for determining location and orientation. A sufficiently precise and reliable determination of the position or movement of the vehicle can often only be achieved by combining or merging a plurality of such and similar measurement information. In this case, a model is used which defines calculation rules according to which the position and / or movement of the vehicle is at least approximately determined from the various input variables.

From document WO 2011/098 333 A1 it is known to use different sensor sizes in a vehicle in order to improve existing sensor sizes or to generate new sensor sizes and thus to increase the detectable information. The model, which is used for the large number of sensor input variables for estimating the vehicle movement or position, is usually adapted as well as possible to the respective vehicle and parameterized. This is based on normal driving conditions, possibly taking into account the dynamic driving limit range.

In the meantime, however, there is an increasing number of systems in vehicles that take effect and function after an accident or collision. Typically, then the assumption of the model used is no longer that normal driving conditions exist. Therefore, in the critical phase after an accident, the positron or motion information may be deficient.

It is therefore an object of the invention to provide a method which allows a reliable and / or precise determination of position and / or movement of a vehicle, even in critical situations.

This is inventively achieved by methods and a Steue tion device according to the respective independent claims. Advantageous embodiments can be taken, for example, the respective subclaims. The content of the claims is made by express reference to the content of the description.

According to the invention, for a method for determining the position and / or movement of a vehicle, it is provided that the vehicle has an electronic processing device, a status detector, and at least one sensor designed to record driving dynamic measured values and to output sensor data, the method comprising the steps includes that the electronic processing device from the Sen sordaten on the basis of a vehicle dynamics model determines a position and / or movement of the vehicle, characterized in that

the condition detector detects a driving condition of the vehicle and

the processing device uses a first or a second vehicle dynamics model to determine the position and / or movement of the vehicle depending on the detected driving state.

Mentioned steps of the method according to the invention can be carried out in the order given. However, they can also be executed in a different order. The inventive method can be performed in one of its embodiments, for example, with a certain set of steps, in such a way that no further steps are performed. Without prejudice to the said steps of the method, however, further steps can be carried out in principle, even those which are not mentioned in this document.

The term sensor is preferably understood to mean a single sensor or a system of several sensors. The sensor data may preferably include one or more variables, such as position, speed, acceleration, direction, slip angle, ie the angle between the direction of movement of the vehicle in the center of gravity and the vehicle longitudinal axis when cornering, yaw rate and / or offset compensation Sen sorgrößen.

The vehicle dynamics model preferably designates one or more flowcharts and / or calculation instructions, such as specific input data, sensor data or sensor data processed data is processed into output data containing information about position and / or movement of the vehicle. The vehicle dynamics model preferably also depends on certain properties, such as dimensions, stiffnesses, masses of the vehicle or its components.

The specified method has the advantage that an adaptation of the position or movement determination to different driving or BEWE conditions is made possible by under different driving dynamics models, so that the positron or movement determination regardless of the current driving or Be wegungszustand precise and is reliable. In the following, for simplicity, only spoken of driving condition.

It is preferable that the state detector distinguishes between an accident-free driving state and a driving state during or after an accident when detecting the driving state, and the processing device uses the first driving dynamics model in an accident-free driving state and the second driving dynamics model in a driving state during or after an accident. By using a driving dynamics model tailored to the driving condition during or after an accident, it is possible that a high level of information quality is available even in this critical phase. This provides, for example, more precise position information for a satellite-based emergency call system (eCall), preferably permanently installed in the vehicle. In addition, systems that provide, for example, automatic or assisted braking or steering following an accident thus provide better movement information.

Hereinafter, various embodiments are mentioned with regard to the vehicle dynamics models used. It should be noted that these embodiments are arbitrarily combinable, so both alternative to each other and used in addition can. Accordingly, several retrievable variants can be available in parallel for the second vehicle dynamics model.

The second driving dynamics model preferably differs from the first driving dynamics model at least in that parts, in particular specific driving dynamic measured values, of the first driving dynamics model in the second driving dynamics model are not taken into account. According to an advantageous development, adaptive parts of the first driving dynamics model are no longer considered in the second in order to avoid mismatches.

According to an additional development, the second driving dynamics model is designed such that the determination of the position and / or the movement of the vehicle is suspended for a certain period of time after the change of the driving dynamics model, if appropriate under the condition that no plausible sensor data is available. Thus it can be prevented that the driving dynamics model is misaligned.

The condition detector preferably detects the differentiation of the driving conditions on the basis of the measured values of at least one accident sensor. An accident sensor should preferably be understood to be a single or a group of sensors designed to record measured values which in some way make it possible to draw conclusions about an undesired driving situation, for example an imminent, occurring or already occurring collision or a collision between a vehicle-external Object and the vehicle. In addition to the types of accident sensors mentioned below, other known from the prior art accident sensors may be appropriate, as they are often used as a basis for decision for the deployment of an airbag or belt tensioner.

The crash sensor preferably comprises a collision sensor configured to detect a contact with an object external to the vehicle or an acceleration induced thereby and / or a rollover sensor configured to detect a rotation about the vehicle longitudinal or transverse axis which is sufficiently high by a rollover allow the vehicle and / or

an acceleration sensor which is designed to detect accelerations above a, in particular accident-typical, threshold value and / or

an environment or proximity sensor configured to detect an imminent collision of the vehicle with an object. Such an environment or proximity sensor may be formed, for example, as an ultrasonic sensor. In addition or as an alternative to the aforementioned rollover sensor, the accident sensor may comprise one or the already existing GNSS receivers, wherein, based on the abruptly changing reception situation of the GNSS receiver, a rollover of the vehicle is recognized or the detection is confirmed by the overbuffer sensor.

Preferably, one of the vehicle dynamics models, in particular the second vehicle dynamics model, is modeled on the basis of vehicle data, in particular center distance, axle width, rolling resistance of the wheels, which are dependent on a deformation of the vehicle derived from the measured values of the accident sensor. Thus, a corruption in the determination of the position and / or the movement of the vehicle by vehicle deformation counteracts acts.

An advantageous embodiment of the method according to the invention comprises that one of the driving dynamics models, in particular the second driving dynamics model, is based on kinematic variables which are derived from the measured values of the accident sensor. For example, a suitably designed collision sensor can provide acceleration values.

It is preferred that one of the vehicle dynamics models, in particular the second vehicle dynamics model, comprises equations of motion in which the vehicle is mathematically modeled as point mass. The point mass describes a strong idealization of a real body, in this case the vehicle. The point mass is preferably understood as a physical model that represents the body solely in terms of its location and mass, and serves to facilitate the description of the movement of the body. External properties such as volume and shape are neglected. The body is considered as a mathematical point, which thus has no extension, but a finite mass. In particular, a point mass preferably does not possess rotational degrees of freedom. The fact that rotational movement and distribution of the mass in the body volume of the vehicle are disregarded with the idealization as a point mass, sizes that are in any given driving condition, such as after an accident, anyway uncertain or error-prone, not in the positi ons / motion detection with a.

It is preferred that one of the driving dynamics models, in particular special second driving dynamics model, based solely on driving dynamics variables that reflect the movement of the vehicle regardless of a road contact, in particular while wheel speeds disregard. In a driving condition in which it is uncertain whether the wheels touch the ground at all, thus an uncertain size is disregarded in the calculation.

It is preferred that the second vehicle dynamics model differs from the first vehicle dynamics model in that one or more several threshold values for error detection, in particular threshold values for error detection in acceleration measurement data, are not taken into account from the first vehicle dynamics model in the second vehicle dynamics model.

A preferred development of the method according to the invention provides that the second vehicle dynamics model differs from the first vehicle dynamics model in that GNSS data as an input variable of the first vehicle dynamics model in the second driving dynamics model are not taken into account as an input variable. This is particularly advantageous if it is expected that the GNSS receiver is damaged or the normal assumptions for the propagation of GNSS signals no longer apply.

It is preferred that the second vehicle dynamics model differs from the first vehicle dynamics model in that data of an inertial measurement unit is not taken into account as an input variable of the first vehicle model in the second vehicle dynamics model as an input quantity. This is particularly advantageous when it is expected that the inertial measuring unit is operated outside its normal working range. Preferably, the term inertial measuring unit is understood to mean an acceleration or yaw rate sensor, particularly preferably a plurality thereof, in particular in the form of a sensory measuring unit of an inertial navigation system.

It is preferred that during the use of the first or second vehicle dynamics model for determining the position and / or the movement of the vehicle, the processing device carries out calculations according to the respective other vehicle dynamics model and makes use of these when changing the vehicle dynamics model. In other words, one or more different from the first vehicle dynamics model driving dynamics models already counted during normal driving. Their output data are available thus at any time available and switching the driving dynamics used by the processing device be merely indicates that the output of another Fahrdyna mikmodells is used. Thus, the vehicle dynamics model can be changed quickly, without settling times for the newly used vehicle dynamics model are necessary.

According to a preferred embodiment of the method according to the invention stores the processing means used in the use of the first or second vehicle dynamics model for determining the position and / or movement of the vehicle sensor data in a data storage between and used at a change of the vehicle dynamics model, the inter mediate stored sensor data for a certain Period as input. Thus, the driving dynamics model can be changed quickly.

According to a further aspect of the invention, the object is achieved by an electronic control device which is configured to carry out a method according to one of the preceding claims, wherein the control device preferably has a sensor designed to detect driving dynamic measured values and to output sensor data and a state detector For detecting a driving state of the vehicle includes or is preferably adapted to sen sordaten from the sensor and a driving state of the state detector to receive.

Further features and advantages of the invention, the skilled artisan take the embodiment described below with reference to the accompanying drawings.

Fig. 1 shows schematically a flow chart for carrying out the method according to the invention according to an embodiment. In this case, it is assumed that a vehicle has an electronic processing device 20, a state detector 10, as well as at least one sensor designed to detect driving-dynamic measured values and to output sensor data. In the present embodiment, there are several sensors. On the one hand an inertial measuring unit, which is able to detect accelerations and turning rates of the vehicle in all spatial directions. On the other hand, an odometry sensor and a GNSS receiver for receiving satellite signals of a satellite navigation system. The various sensor data originating from these sensors are fused or filtered in the processor 20 so that the respective advantages of the various sensors come into play for the determination of the position or movement of the vehicle.

The processing device 20 in the present embodiment receives, via the GNSS receiver, location data of the vehicle including an absolute position of the vehicle on a road surface. In addition to the absolute position, the location data from the GNSS receiver also includes a speed of the vehicle. The location data from the GNSS receiver is derived in the present embodiment in a manner known to those skilled in the art from a GNSS signal in the GNSS receiver which is received via a GNSS antenna and hence referred to below as GNSS location data. For details, refer to the relevant literature.

By merging with the further sensor data, the processing device 20 is able to increase the information content of the GNSS position data derived from the GNSS signal. This is useful because the GNSS signal is not always available. The further sensor data in the present case include vehicle dynamics data from an inertial measurement unit in the form of a longitudinal acceleration, a lateral acceleration and a Vertical acceleration and a roll rate, a pitch rate and a yaw rate of the vehicle.

To further increase the information content of the GNSS position data, in the present embodiment, an odomimetry sensor system is used. This includes wheel speed sensors, which detect the wheel speeds of the individual wheels of the vehicle. It may also include a steering angle signal to further increase the information content of the GNSS location data.

On the one hand, situation data of the vehicle are generated via a strapdown algorithm from the vehicle dynamics data of the inertial measurement unit. The fusion of the sensor data also uses comparative data for the same quantities that are derived from the GNSS position data or the data from the odometry sensors. How the position data or comparison data are calculated may depend on the vehicle dynamics model used.

The filter used for the merger calculates an error budget for the location data and a fault budget for the comparison data based on the location data and the comparison data. The fault households are then supplied according to the strapdown algorithm and the vehicle dynamics model used to correct the position data or the comparison data. This means that the location data and the comparison data are iteratively adjusted for their errors.

The used vehicle dynamics model can be changed depending on a driving state 1 he grasps. For this purpose, a state detector 10 is provided, which is designed to detect a change in the driving state 1 via an accident sensor. The driving state 1 is forwarded by the state detector 10 via a corresponding detector signal 2 to the processing device 20, wherein the detector signal 2 at least a distinction allows between a driving state 1, which corresponds to a normal driving and a driving state 1, which corresponds to a driving state 1 during or after an accident.

The processing device 20 determines the position and / or movement of the vehicle based on the used Fahrdy namikmodells. If it receives a detector signal 2 which corresponds to a driving state 1 during or after an accident, it uses a second driving dynamics model 22 instead of the previously used first driving dynamics model 21 in order to generate therefrom a position / movement signal 3 which is generated by the vehicle second driving dynamics model 22, the specifics of the changed driving condition 1 taken into account.

For the second driving dynamics model 22 different variants are conceivable. Correspondingly, the detector signal 2 in conjunction with the accident sensor or the accident sensors can also permit finer distinctions, so that the respectively suitable variant of the second vehicle dynamics model 22 can be selected.

Claims

claims

1. A method for determining the position and / or movement of a vehicle, wherein the vehicle has an electronic processing device (20), a state detector (10), and at least one sensor configured for detecting driving dynamic measured values and for outputting sensor data

the electronic processing device (20) determines from the sensor data on the basis of a vehicle dynamics model a position and / or movement of the vehicle, characterized in that

the condition detector (10) detects a driving condition (1) of the vehicle and

the processing device (20) uses a first or a second vehicle dynamics model (21, 22) to determine the position and / or movement of the vehicle, depending on the driving state (1).

2. The method according to claim 1, characterized in that the state detector (10) upon detection of the driving state (1) between an accident-free driving state (1) and a driving state (1) at or after an accident differs and the processing device (20) at an accident-free Fahrzu stood (1) the first driving dynamics model (21) and in a driving condition (1) during or after an accident, the second driving dynamics model (22) used.

3. The method according to claim 2, characterized in that the second driving dynamics model (22) differs from the first driving dynamics model (21) at least in that parts, in particular specific driving dynamic measured values, of the first driving dynamics model (21) in the second driving dynamics model (22). not considered.

4. The method according to any one of the preceding claims, characterized in that the state detector (10) detects the differentiation of the driving conditions based on the measured values of at least one accident sensor.

5. The method according to claim 4, characterized in that the accident sensor

a collision sensor, which is designed to detect a contact with a vehicle-foreign object and / or

a rollover sensor configured to detect a rotation about the vehicle longitudinal or lateral axis that is sufficiently high to allow rollover of the vehicle and / or

comprises an acceleration sensor which is designed to detect accelerations above a, in particular accident-typical, threshold value and / or

an environment or proximity sensor configured to detect an imminent collision of the vehicle with an object.

6. The method according to any one of claims 4 or 5, characterized in that one of the driving dynamics models, in particular the second driving dynamics model (22), on the basis of vehicle data, in particular center distance, axle width, rolling resistance of the wheels is modeled, the one of the Measured values of the inadvertent sensor derived deformation of the vehicle are dependent.

7. The method according to any one of claims 4 to 6, characterized in that one of the driving dynamics models, in particular the second driving dynamics model (22), on kinematic variables which are derived from the measurements of the accident sensor.

8. The method according to any one of the preceding claims, characterized in that one of the driving dynamics models, in particular the second driving dynamics model (22), equations of motion to summarizes, in which the vehicle is mathematically modeled as a point mass mo.

9. The method according to any one of the preceding claims, characterized in that one of the driving dynamics models, in particular the second driving dynamics model (22), based exclusively on driving dynamic variables that reflect the movement of the vehicle regardless of a road contact, in particular wheel speeds out of consideration.

10. The method according to any one of claims 2 to 9, characterized in that the second driving dynamics model (22) of the first driving dynamics model (21) differs in that one or more thresholds for error detection from the first vehicle dynamics model (21) in the second vehicle dynamics model (22) are not taken into account.

11. The method according to any one of claims 2 to 10, characterized in that the second driving dynamics model (22) differs from the first driving dynamics model (21) in that GNSS data as input of the first driving dynamics model (21) in the second driving dynamics model (22 ) are not considered as an input variable.

12. The method according to any one of claims 2 to 11, characterized in that the second driving dynamics model (22) differs from the first driving dynamics model (21) in that data of an inertial measuring unit as input of the first Vehicle dynamics model (21) in the second vehicle dynamics model (22) are not taken into account as an input variable.

13. The method according to any one of the preceding claims, characterized in that the processing device (20) during the use of the first or second driving dynamics model (21, 22) for determining the position and / or the movement of the vehicle performs calculations according to the other driving dynamics model and resorting to this when changing the driving dynamics model.

14. The method according to any one of the preceding claims, characterized in that the processing means (20) caches the sensor data used in the use of the first or second driving dynamics model (21, 22) for determining the position and / or movement of the vehicle in a data memory and When changing the vehicle dynamics model, the cached sensor data is used as an input variable for a certain period of time.

15. An electronic control device configured to perform a method according to any one of the preceding claims.