DE102018220020A1 - Method for the satellite-based determination of a vehicle's own position, a method for providing a hybrid, three-dimensional environment model for the satellite-based localization of a vehicle, a computer program, a machine-readable storage medium and a vehicle - Google Patents

Abstract

The invention relates to a method for satellite - based determination of a vehicle 's own position (1), comprising at least the following steps: a) receiving signals (6) from navigation satellites (2), b) detecting at least one item of information about at least one mobile reception obstacle (3) in the Environment of the vehicle (1), c) determining the own position of the vehicle (1) using the signals received in step a) and the information recorded in step b).

Description

State of the art

In the future there will be a multitude of highly or even fully automated driving functions for vehicles. With these functions, the car drives automatically, i.e. a driver no longer has to control all or all of the driving operations. It is important for highly / fully automated driving that the localization is as accurate as possible, preferably in a detailed map, which is done at least in a first step via signals from global navigation satellite systems (GNSS). Here, the global (own) position of the receiver and thus of the vehicle is calculated over the duration of radio signals from navigation satellites to the receiver in or on the vehicle.

In addition to atmospheric disturbances such as refraction in the ionosphere, so-called multipath effects (i.e. effects from multipath propagation) are one of the main sources of error in GNSS-based localization systems. The effect of GNSS-based localization in house canyons, in which direct signal paths are only possible in a small radial field, has long been known, whereby most satellite signals can only be received after at least one reflection on a building facade or other obstacles. In order to be able to predict the errors resulting from multipath effects (also referred to here as multipath propagation effects) and, if necessary, to correct them subsequently, the most accurate possible estimation and / or modeling of the environment responsible for the multipath effects is sought.

Disclosure of the invention

1. a) Empfangen von Signalen von Navigationssatelliten,
2. b) Erfassen mindestens einer Information über mindestens ein mobiles Empfangshindernis im Umfeld des Fahrzeugs,
3. c) Ermitteln der Eigenposition des Fahrzeugs unter Verwendung der in Schritt a) empfangenen Signale und der in Schritt b) erfassten Information.

Here, according to claim 1, a method is proposed for satellite-based determination of a vehicle's own position, comprising at least the following steps:

1. a) receiving signals from navigation satellites,
2. b) capturing at least one piece of information about at least one mobile obstacle to reception in the vicinity of the vehicle,
3. c) determining the vehicle's own position using the signals received in step a) and the information acquired in step b).

The specified sequence of steps a), b) and c) can occur in a regular operating procedure. In addition, at least steps a) and b) can also be carried out at least partially in parallel or even simultaneously.

The method allows in a particularly advantageous manner that the detected information about at least one mobile reception obstacle in the vicinity of the vehicle can be used to recognize and possibly take into account multipath propagation effects caused by the mobile reception obstacle. It has been found that, in addition to static obstacles, such as buildings, in particular outside urban environments, dynamic obstacles (also referred to here as mobile reception obstacles), such as other road users, are often responsible for multipath effects (also referred to here as multipath propagation effects). This applies not only, but in particular to multi-lane roads, expressways and / or motorways, where other road users with low relative speed can cover part of the reception field of the vehicle's GNSS antenna over a long period of time.

In other words, the method is used in particular to determine a GNSS-based vehicle position of one's own vehicle. The (own) vehicle is preferably an autonomous vehicle, in particular an autonomously operating automobile. GNSS stands for global navigation satellite system. GNSS is a system for position determination and / or navigation on earth and / or in the air by receiving the signals from navigation satellites, here referred to as satellite data. GNSS is a collective term for the use of existing and future global satellite systems such as GPS (NAVSTRAR GPS), GLONASS, Beidou and Galileo.

The method can be carried out at least partially by or in a GNSS sensor of the vehicle. The GNSS sensor is usually a sensor system that is suitable for receiving and processing, for example evaluating, navigation satellite data. The GNSS sensor is preferably able to determine a highly precise vehicle position with the aid of navigation satellite data (GPS, GLONASS, Beidou, Galileo). GNSS data is, in particular, data that is received by a navigation satellite, GNSS data can also be referred to as “navigation satellite data”.

In step a), signals from navigation satellites are received. In other words, this means in particular that GNSS data are received in step a). A GNSS sensor of one's own vehicle preferably receives the GNSS satellite data from at least one GNSS receiver unit (of the vehicle), for example a GNSS antenna in particular on the vehicle, which in turn communicates (directly) with the navigation satellites or receives the satellite signals (directly).

In step b), at least one piece of information about at least one mobile reception obstacle is recorded in the area surrounding the vehicle. The at least one mobile obstacle to reception can be, for example, another road user, such as another vehicle. In particular, the mobile obstacle to reception can be a truck (or truck for short). Mobile reception obstacles in the surroundings of the vehicle are understood here to mean, in particular, moving, in particular moving objects, on which signals from navigation satellites could have been reflected before they reach the vehicle. Furthermore, in step b), information about several mobile reception obstacles in the area of the vehicle can also be recorded.

In step c) the own position of the vehicle is determined using the signals received in step a) and the information acquired in step b). The determination can take place, for example, by means of a GNSS sensor of the vehicle. In particular, in step c) a vehicle position of the own vehicle is calculated with the signals (GNSS data) received in step a). For this purpose, runtime measurements of the signals are usually carried out. Furthermore, the information recorded in step b) can in particular be used in step c) in such a way that multipath effects caused by the mobile obstacle to reception may be recognized and possibly taken into account, preferably compensated.

The information recorded in step b) can, for example, be taken into account directly in the runtime measurements. For example, lukewarm measurements of signals where strong multipath effects can be expected could be stopped or not started at all. This can be implemented, for example, in such a way that signals that come from a certain direction or certain directions are not taken into account or are only weighted slightly.

Alternatively or cumulatively, the information recorded in step b) can be used, for example, to compensate for multipath propagation effects or to correct position results falsified by multipath propagation effects. For example, falsified position results from signal propagation times that are too long due to signal reflections at the mobile reception obstacle can be corrected (subsequently).

In addition, in step b), at least one item of information about at least one static obstacle to reception can also be recorded in the surroundings of the vehicle. The at least one static obstacle to reception can be, for example, a building, such as a high-rise building. Static reception obstacles in the surroundings of the vehicle are understood here to mean, in particular, immobile, in particular stationary objects, on which signals from navigation satellites could have been reflected before they reach the vehicle. Furthermore, in step b), information about a number of static reception obstacles in the surroundings of the vehicle can also be recorded.

According to an advantageous embodiment, it is proposed that the information describes at least one property relevant for multipath propagation effects. In other words, this can also be expressed in such a way that the information is characteristic of multipath propagation effects.

Properties relevant for multipath propagation effects can be, for example, the (relative) position (relative to one's own vehicle), the (spatial) extent and / or the material of an obstacle to reception. The information preferably describes at least two properties relevant for multipath propagation effects, such as, for example, at least the position and the extent of the reception obstacle.

According to a further advantageous embodiment, it is proposed that the detection in step b) takes place using at least one environmental sensor of the vehicle. The environment sensor can be, for example, a camera, an ultrasound sensor, a LIDAR sensor and / or a RADAR sensor. Corresponding environment sensors can generally help to determine a distance between your own vehicle and an obstacle, an expansion of the obstacle and / or the material of the obstacle.

According to a further advantageous embodiment, it is proposed that the detection in step b) be carried out using at least one vehicle-to-vehicle or vehicle-to-infrastructure communication.

Vehicle-to-vehicle communication (Car2Car or C2C) means the exchange of information and data between (motor) vehicles. The aim of this data exchange is to report critical and dangerous situations to the driver at an early stage. The vehicles in question collect data, such as ABS intervention, steering angle, position, direction and speed, and send this data to the other road users via radio (WLAN, UMTS, etc.). The driver's “visibility” should be extended electronically. Under car-to-infrastructure communication (or: C2I) the exchange of data between a vehicle and the surrounding infrastructure (eg light signal systems) understood. The technologies mentioned are based on the interaction of sensors from different transport partners and use the latest methods of communication technology to exchange this information. Vehicle-to-X is a generic term for the various communication links, such as vehicle-to-vehicle and vehicle-to-infrastructure.

• i) Empfangen von Informationen über statische Empfangshindernisse im Umfeld des Fahrzeugs,
• ii) Erfassen mindestens einer Information über mindestens ein mobiles Empfangshindernis im Umfeld des Fahrzeugs,
• iii) Fusionieren der in Schritt i) empfangenen Informationen und der in Schritt ii) erfassten Information zu dem hybriden, dreidimensionalen Umfeldmodell.

According to a further advantageous embodiment, it is proposed that (in step c)) at least one multipath propagation effect is compensated for using the information recorded in step b). In particular, a reflection of a signal emitted by a navigation satellite before reaching the vehicle (which leads to an extended signal delay and therefore to a falsified position result) can be corrected. According to a further aspect, a method for providing a hybrid, three-dimensional environment model for the satellite-based localization of a vehicle is also proposed, comprising at least the following steps:

• i) receiving information about static reception obstacles in the vicinity of the vehicle,
• ii) capturing at least one piece of information about at least one mobile reception obstacle in the vicinity of the vehicle,
• iii) Fusion of the information received in step i) and the information acquired in step ii) into the hybrid, three-dimensional environment model.

The specified sequence of steps i), ii) and iii) can occur in a normal operating procedure. In addition, at least steps i) and ii) can also be carried out at least partially in parallel or even simultaneously.

The environment model is in particular formed in such a way that it provides data for the detection, modeling and / or compensation of multi-web propagation effects (multipath effects). This data can be provided, for example, by a navigation system and / or a control device for automatic driving and can be used there if necessary.

Basically, the environment model (in particular the proportion of static reception obstacles) can be based on 3D models from highly precise maps. Static obstacles, which are stored in a high-precision map as a 3D body, can be projected into a virtual environment relative to the estimated vehicle position. Localization relative to the map can be done either with the help of a rough localization before the multipath effects occur, by odometry extrapolation or by a suitable other localization method. With the help of the satellite positions and the virtual 3D environment, disruptive multipath effects can be predicted and consequently compensated for.

The solution proposed here advantageously allows mobile obstacles, such as other road users, to be projected into a virtual environment relative to the estimated vehicle position. The method makes this possible in particular by capturing at least one item of information about at least one mobile reception obstacle in the area surrounding the vehicle.

In step i) information about static reception obstacles in the environment of the vehicle is received. The static obstacles to reception can be, for example, buildings such as high-rise buildings forming a canyon. Static reception obstacles in the surroundings of the vehicle are understood here to mean, in particular, immobile, in particular stationary objects, on which signals from navigation satellites could have been reflected before they reach the vehicle.

In step ii), at least one piece of information about at least one mobile reception obstacle is recorded in the area surrounding the vehicle. The at least one mobile obstacle to reception can be, for example, another road user, such as another vehicle. In particular, the mobile obstacle to reception can be a truck (or truck for short). Mobile reception obstacles in the surroundings of the vehicle are understood here to mean, in particular, moving, in particular moving objects, on which signals from navigation satellites could have been reflected before they reach the vehicle. Furthermore, in step ii), information about several mobile reception obstacles in the vehicle's surroundings can also be recorded.

In step iii) the information received in step i) is merged with the information acquired in step ii) to form the hybrid, three-dimensional environment model. The information received in step i) and / or (artificial or digital) replicas of the static reception obstacles determined on the basis of this information can be relative to a (previously) estimated vehicle position in a virtual environment (around the vehicle) can be projected. Furthermore, the at least one item of information recorded in step ii) and / or an (artificial or digital) replication of the at least one mobile reception obstacle determined on the basis of this information can be projected into a virtual environment (around the vehicle) relative to a (previously) estimated vehicle position will.

The solution proposed here advantageously makes it possible to provide a hybrid 3D environment model which, in addition to previously used static 3D environment data, also contains dynamic obstacles (mobile reception obstacles). Dynamic obstacles or mobile obstacles to reception as well as their properties relevant for multipath effects (position, extent, material, ...) can originate, for example, from the perception of the environment used in particular for driver assistance functions, but also via vehicle-to-vehicle or vehicle-to-vehicle - Infrastructure communication to be transmitted.

In contrast to static obstacles, dynamic obstacles (mobile reception obstacles), if these originate from the perception of the surroundings, can be modeled in a global coordinate system instead of - with localization errors - with localization errors. In addition, dynamic objects and the perception of the surroundings of static objects can be used to improve the localization in the static 3D environment map. In the hybrid environment model, multipath effects can thus be modeled, predicted and / or (finally) compensated for, taking dynamic obstacles into account.

If there is greater uncertainty about the nature of the dynamic obstacles (e.g. uncertainty with regard to the material composition), it may happen that modeling to compensate for the multipath effects is not possible with sufficient accuracy. In this case, however, it may be possible to detect signals with multipath effects and to exclude them from the localization.

According to an advantageous embodiment, it is proposed that the information in each case describe at least one property relevant for multipath propagation effects. In other words, this can also be expressed in such a way that the information (in each case) is characteristic of multipath propagation effects.

Properties relevant for multipath propagation effects can be, for example, the (relative) position (relative to one's own vehicle), the (spatial) extent and / or the material of an obstacle to reception. The information preferably describes at least two properties relevant for multipath propagation effects, such as, for example, at least the position and the extent of the reception obstacle.

According to a further aspect, a computer program is also proposed which is set up to carry out a method presented here. In other words, this relates in particular to a computer program (product) comprising instructions which, when the program is executed by a computer, cause the computer to carry out a method described here.

According to a further aspect, a machine-readable storage medium on which the computer program is stored is also proposed. The machine-readable storage medium is usually a computer-readable data carrier.

According to a further aspect, a vehicle is also proposed with a system which is set up to carry out a method presented here. The vehicle is preferably a vehicle that is set up for automatic or autonomous operation. In particular, the vehicle is an autonomous automobile. The system can be, for example, a navigation system and / or a control unit of the vehicle, in particular a control unit for the automatic or autonomous driving of the vehicle. The system can include, for example, a storage medium proposed here and a processor that can access the storage medium.

The details, features and advantageous configurations discussed in connection with the method for satellite-based determination of a vehicle's own position can accordingly also be used in the method presented here for providing a hybrid, three-dimensional environment model for the satellite-based localization of a vehicle, the computer program, the storage medium and / or occur in the vehicle and vice versa. In this respect, full reference is made to the explanations given there for the more detailed characterization of the features.

• 1: einen beispielhaften Ablauf eines hier vorgestellten Verfahrens zum satellitengestützten Ermitteln einer Eigenposition eines Fahrzeugs,
• 2: eine beispielhafte Veranschaulichung eines hier vorgestellten Verfahrens zum satellitengestützten Ermitteln einer Eigenposition eines Fahrzeugs,
• 3: einen beispielhaften Ablauf eines hier vorgestellten Verfahrens zum Bereitstellen eines hybriden, dreidimensionalen Umfeldmodells für die satellitengestützte Eigenlokalisierung eines Fahrzeugs, und
• 4: eine beispielhafte Darstellung eines hier vorgeschlagenen Fahrzeugs.

The solution presented here and its technical environment are explained in more detail below with reference to the figures. It should be pointed out that the invention is not intended to be limited by the exemplary embodiments shown. In particular, unless explicitly stated otherwise, it is also possible to extract partial aspects of the facts explained in the figures and to combine them with other components and / or knowledge from other figures and / or the present description. They show schematically:

• 1 an exemplary sequence of a method presented here for satellite-based determination of a vehicle's own position,
• 2nd : an exemplary illustration of a method presented here for satellite-based determination of an own position of a vehicle,
• 3rd : an exemplary sequence of a method presented here for providing a hybrid, three-dimensional environment model for the satellite-based localization of a vehicle, and
• 4th : an exemplary representation of a vehicle proposed here.

1 shows schematically an exemplary sequence of a method presented here for the satellite-based determination of an own position of a vehicle 1 (not shown here, cf. 2nd ). The sequence of process steps a), b) and c) shown with the blocks 110 , 120 and 130 is only an example and results in the order shown usually in a regular operating sequence.

In block 110 signals are received 6 (not shown here, cf. 2nd ) of navigation satellites 2nd (not shown here, cf. 2nd ). In block 120 at least one piece of information about at least one mobile reception obstacle is recorded 3rd (not shown here, cf. 2nd ) around the vehicle 1 . In block 130 the vehicle's own position is determined 1 using the in step a) or in block 110 received signals and those in step b) or in block 120 captured information.

2nd schematically shows an exemplary illustration of a method presented here for satellite-based determination of an own position of a vehicle. The reference symbols are used uniformly, so that reference is made to the preceding explanations 1 full reference can be made.

The in step b) or block 120 in 1 recorded at least one piece of information about the at least one mobile obstacle to reception 3rd describes here, by way of example, a property of the mobile reception obstacle that is relevant for multipath propagation effects 3rd . Such a property can be, for example, a position, an extent and / or a material of the mobile reception obstacle 3rd be.

In addition, the detection in step b) is carried out here, for example, using at least one environmental sensor 4th of the vehicle 1 . With the environment sensor 4th For example, it can be a camera that detects the (relative) position, the extent and / or the material of the mobile reception obstacle 3rd can record, in particular measure and / or recognize. In addition, the detection in step b) can, for example, alternatively or cumulatively also take place using at least one vehicle-to-vehicle or vehicle-to-infrastructure communication.

Furthermore, in step c) or block 130 using the in step b) or block 120 recorded information, a compensation of at least one multipath propagation effect takes place. In 2nd can be seen that the mobile reception obstacle 3rd a reflection of the signal 6 occurs, which leads to a multipath propagation effect. This can be based on the knowledge of the at least one piece of information about the at least one mobile obstacle to reception 3rd be compensated.

In other words, this can in particular also be described as follows: The vehicle to be located 1 (Car) receives signals 6 of the GNSS satellite 2nd not only directly, but also indirectly through reflection on a dynamic obstacle 3rd (e.g. the side wall of a truck). The dynamic obstacle 3rd (also referred to here as a mobile reception obstacle) is the vehicle to be located 2nd but through the perception of the environment 4th known, so that occurring multipath effects can be modeled, predicted and / or compensated.

3rd schematically shows an exemplary sequence of a method presented here for providing a hybrid, three-dimensional environment model for the satellite-based localization of a vehicle 1 (not shown here, cf. 2nd ). The sequence of process steps i), ii) and iii) shown with the blocks 210 , 220 and 230 is only an example and results in the order shown usually in a regular operating sequence.

In block 210 information about static reception obstacles is received 5 (not shown here, cf. 2nd ) around the vehicle 1 . In block 220 at least one piece of information about at least one mobile reception obstacle is recorded 3rd (not shown here, cf. 2nd ) around the vehicle 1 . In block 230 there is a fusion of the step i) or in block 210 received information and in step ii) or in block 220 captured information on the hybrid, three-dimensional environment model.

In the described method for providing the hybrid, three-dimensional environment model, the information generally describes at least one property relevant for multipath propagation effects.

4th schematically shows an exemplary representation of a vehicle proposed here 1 . The vehicle 1 assigns a system 7 which is set up to carry out a method presented here.

• • Nutzung von dynamischen Hindernissen zur Modellierung, Prädiktion und/oder Kompensation von Multipath-Effekten bei der GNSS-Lokalisierung.
• • Erweiterung eines statischen Umfeldmodells auf ein hybrides Umfeldmodell, z.B. durch Umfeldwahrnehmung oder Car2X-Kommunikation.

The solution proposed here allows in particular one or more of the following advantages:

• • Use of dynamic obstacles for modeling, prediction and / or compensation of multipath effects in GNSS localization.
• • Extension of a static environment model to a hybrid environment model, for example through environment awareness or Car2X communication.

Claims (10)

Method for satellite-based determination of a vehicle's own position (1), comprising at least the following steps: a) receiving signals (6) from navigation satellites (2), b) capturing at least one piece of information about at least one mobile reception obstacle (3) in the vicinity of the vehicle (1), c) determining the own position of the vehicle (1) using the signals received in step a) and the information acquired in step b). Procedure according to Claim 1 , the information describing at least one property relevant for multipath propagation effects. Procedure according to Claim 1 or 2nd , wherein the detection in step b) takes place using at least one environment sensor (4) of the vehicle (1). Method according to one of the preceding claims, wherein the detection in step b) is carried out using at least one vehicle-to-vehicle or vehicle-to-infrastructure communication. Method according to one of the preceding claims, wherein [in step c)] using the information acquired in step b) a compensation of at least one multipath propagation effect takes place. Method for providing a hybrid, three-dimensional environment model for the satellite-based localization of a vehicle (1), comprising at least the following steps: i) receiving information about static reception obstacles (5) in the vicinity of the vehicle (1), ii) capturing at least one piece of information about at least one mobile reception obstacle (3) in the vicinity of the vehicle (1), iii) Fusion of the information received in step i) and the information acquired in step ii) into the hybrid, three-dimensional environment model. Procedure according to Claim 6 , the information each describing at least one property relevant for multipath propagation effects. Computer program which is set up to carry out a method according to one of the preceding claims. Machine-readable storage medium on which the computer program is based Claim 8 is saved. Vehicle (1) with a system (7) which is used to carry out a method according to one of the Claims 1 to 7 is set up.

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