Method for supporting the longitudinal control of a vehicle using metadata and method for making such metadata available

Abstract

Method for supporting the longitudinal control of a vehicle (Fn+1) using metadata, wherein - metadata (M) to support the longitudinal control before driving on a section of road are received (4), wherein the metadata has been generated by the data received from a plurality of other vehicles (F1, F n ) parameters recorded when driving through the same section of the route (P1, P n ) of the respective vehicle and / or the respective vehicle environment and / or time-related parameters (P1, P n ) have been recorded and statistically evaluated; - for the route section, additional sensor data of the vehicle's surroundings are recorded (1) using one or more sensors of the vehicle (Fn+1) and / or digital map data of a navigation system of the vehicle (Fn+1) are used; - plausibility is checked by comparing the metadata (M) with the recorded sensor data and / or the digital map data; and - depending on the result of the plausibility check, the longitudinal control is carried out based on the sensor data recorded by the vehicle (Fn+1) and / or the digital map data available in the vehicle (Fn+1) or the metadata is used for optimised longitudinal control (5).

Description

[0001] The present invention relates to a method for supporting the longitudinal control of a vehicle using metadata and a method for providing such metadata. The present invention further relates to a corresponding assistance system.

[0002] A variety of assistance systems are now used in motor vehicles. For this purpose, sensors such as one or more video cameras or ultrasound, radar or laser-based sensors are installed in the vehicle to monitor the vehicle's surroundings. For example, adaptive cruise control, often referred to as ACC (an abbreviation of the English term “Adaptive Cruise Control”), can provide adaptive cruise control. This system uses a sensor to determine the position and speed of a vehicle in front and automatically regulates the speed and distance of the following vehicle equipped with adaptive cruise control using engine and braking intervention. This automated longitudinal control relieves the driver and makes long motorway journeys, for example, more comfortable. It can also be used, if necessary, to...in conjunction with an emergency braking assistant, contribute to driving safety and enable a more energy-efficient driving style.

[0003] A cornering function can also be provided, in which the vehicle continuously "scans" the route ahead using digital map data stored in a satellite-based navigation device and checks whether a curve is imminent. Metadata such as the curve's curvature can also be stored, allowing the assistance system to determine the speed at which the curve can be comfortably negotiated. In this way, a vehicle's preset speed that is too high can be reduced before sharp bends, and the vehicle can then accelerate after the bend until the preset speed is reached again.

[0004] However, this requires that reliable metadata for the curves be stored in the digital map data. This is not the case for all curves in the public road network, even though the road network has now been almost completely mapped. For example, new roads are constantly being added to the road network or undergoing structural changes. In such a case, the new roads and altered road layouts must be manually scanned and mapped using appropriate scanning or digitized using drawings provided by the responsible road authorities. Only after this process, which can take several months, can the digital map data be updated.

[0005] Against this background, DE 10 2008 012 661 A1 describes a device for updating a digital map for a vehicle. The device has a multitude of sensors that measure the current traffic situation, the vehicle's movement, or even the road conditions. The measured values are transmitted to a central unit, which evaluates them and then sends corresponding update data to other vehicles to update the digital map.

[0006] Furthermore, DE 10 2016 007 567 A1 discloses that a vehicle system-side communication device receives a lane map from a computing device external to the vehicle, which describes movement paths determined by the computing device from actual past driving trajectories of other vehicles and related to an area surrounding the motor vehicle. A vehicle system-side control device then determines a trajectory to be traveled by the motor vehicle and / or performs a lateral guidance intervention based on the lane map.

[0007] DE 10 2015 000 856 A1 discloses a motor vehicle with a navigation device having stored route information describing a route section and a cruise control system that regulates the actual speed of the motor vehicle to a stored target speed determined based on the stored route information. If modified route information concerning a modified route section is supplied to the navigation device, the cruise control system regulates the actual speed depending on an evaluation parameter that qualifies an earlier passage through the modified route section, either to the stored target speed determined based on the stored route information or to a modified target speed determined based on the modified route information.

[0008] DE 10 2012 218 100 A1 describes a method for speed control in a vehicle based on driving speed profiles determined on a server, in which driving speed data of a plurality of vehicles are evaluated on the server to determine the driving speed profiles.

[0009] Finally, US 2011 / 0 301 802 A1 discloses a method for monitoring vehicle speed using historical speed data, in which a target speed is determined for a vehicle on a road section ahead of the vehicle. A driver warning is activated if the vehicle is expected to exceed the target speed based on a measured driving dynamic characteristic.

[0010] It is an object of the invention to provide an improved method for supporting the longitudinal control of a vehicle using metadata and an improved method for providing such metadata as well as a corresponding assistance system.

[0011] This object is achieved by a method having the features of claim 1 and by an assistance system according to claim 8. Preferred embodiments of the invention are the subject of the dependent claims.

[0012] In a method according to the invention for supporting the longitudinal control of a vehicle using metadata, wherein - metadata are received to support longitudinal control before driving on a section of road, the metadata having been generated by recording and statistically evaluating the parameters of the respective vehicle and / or the respective vehicle environment and / or time-related parameters recorded by a large number of other vehicles when driving on the same section of road; - for the route section, additional sensor data from the vehicle's surroundings are recorded using one or more sensors on the vehicle and / or digital map data from a navigation system on the vehicle are used; - plausibility is checked by comparing the metadata with the recorded sensor data and / or the digital map data; and - depending on the result of the plausibility check, the longitudinal control is carried out based on the sensor data recorded by the vehicle and / or digital map data available in the vehicle or the metadata is used for optimized longitudinal control.

[0013] The parameters recorded and statistically evaluated by the multitude of vehicles, hereinafter also referred to as the vehicle swarm and swarm data, thus provide metadata that enables the automated updating of digital map data, even at very short notice if the vehicle swarm is large enough. This data can then be made available to other vehicles immediately, ensuring that driver assistance systems that use this metadata are highly accurate and up-to-date. Navigation systems can also use this metadata to provide optimized route guidance, as they can also be supplied with very up-to-date and accurate maps and, depending on the speed of the vehicle swarm, plan an optimal route at any time of day or year. This is a major advantage for electric vehicles, for example, as it increases their sometimes still limited range.

[0014] According to one embodiment of the invention, the route section comprises a curve, wherein the curve, in particular the curve radius, and / or a recommended speed for the vehicle when driving through the curve is determined by means of the metadata.

[0015] In particular, it is advantageous if - a quality criterion for the metadata is determined for the route section; - the quality criterion is compared with a predefined limit value; and - the metadata is only used for automated longitudinal control if the quality criterion is below the predefined limit.

[0016] Preferably, the metadata from a driver assistance system of the vehicle is used for automated longitudinal control.

[0017] Preferably, one or more of the following parameters of the respective vehicle are recorded by sensors: - the geographical coordinates; - the yaw rate; - the speed; - the lateral acceleration; and / or - the steering angle.

[0018] Furthermore, one or more of the following parameters of the respective vehicle environment can advantageously be recorded: - the weather; and / or - the lighting conditions.

[0019] Likewise, one or more of the following time-related parameters are advantageously recorded: - the time; - the date; and / or - the day of the week.

[0020] The invention also relates to an assistance system for carrying out the method according to the invention.

[0021] Further features of the present invention will become apparent from the following description and claims in conjunction with the figures. Fig. 1 shows a flowchart of a method according to the invention; and Fig. Figure 2 shows a schematic overview of several swarm vehicles driving through the same curve section and reporting parameters to a server, as well as a vehicle receiving metadata generated based on this data for the longitudinal guidance of the vehicle when driving through the curve section.

[0022] To better understand the principles of the present invention, embodiments of the invention are explained in more detail below with reference to the figures. It is understood that the invention is not limited to these embodiments and that the described features can also be combined or modified without departing from the scope of the invention as defined in the claims.

[0023] Fig. Figure 1 shows a flowchart of a method according to the invention. In a first method step 1, various parameters are recorded from a plurality of vehicles while traveling along a section of road. First, the current position data is determined, for example, using the satellite-based navigation system GPS. Furthermore, the vehicle's surroundings can be recorded using sensors on the respective vehicles.

[0024] This can be done, for example, using one or more of the vehicle's cameras, but also other sensors, such as radar, ultrasonic, or lidar sensors. These can be used to precisely determine lane layouts, for example, by detecting lane markings, guardrails, or delineators, or to determine other parameters such as the current weather, road, or lighting conditions.

[0025] Likewise, parameters of the respective vehicle such as yaw rate, speed, lateral acceleration and / or steering angle, which are monitored, for example, by a driver assistance system for electronic stability control, or time-related parameters such as the current time, date and / or day of the week can be recorded.

[0026] The parameters recorded by the swarm vehicles are fed to a server, which in a process step 2 statistically evaluates the parameters using suitable algorithms and, based on this, generates metadata for the recorded route section, which can be used to support longitudinal control when a vehicle later travels along the route section.

[0027] In process step 3, the generated metadata is sent from the server to vehicles that may or may not be part of the vehicle swarm. The transmission can be performed simultaneously to multiple vehicles in the form of a push model as soon as new metadata for a route section is available. Alternatively, data can be transmitted in the form of a pull model only when the metadata is required by a vehicle and therefore requested from the server.

[0028] Accordingly, the metadata is then received by a vehicle in process step 4, either upon request immediately before traveling a specific route section or independently. In the latter case, the metadata is first stored in the vehicle's memory or the digital map data of a navigation system installed in the vehicle is updated.

[0029] The generated metadata can then be used in a process step 5 for automatic control of the longitudinal guidance, for example, in a semi-autonomous or autonomous driving mode to enable an assistance system to optimally adapt the current speed to the curve and, if necessary, external conditions such as the weather. In particular, the maximum safe driving speed can be determined using the curve radius known from the metadata and the vehicle type, such as a car, truck, or motorcycle. In the case of a car, for example, the car type, such as a sedan, sports car, or SUV, can also be taken into account, since different cornering speeds are possible, for example, due to the different configuration of the chassis.Likewise, a driving mode selected by the vehicle user, such as “sporty” or “consumption-optimized”, can also be taken into account in order to determine the speed and, if necessary, the trajectory for driving through the curve.

[0030] However, it can also be provided to provide the driver with a speed recommendation for an upcoming curve based on the generated metadata in manual driving mode, thus enabling a proactive driving style that adapts to the conditions in advance. For example, a warning that the current speed is too fast for the upcoming curve can be issued to the driver, prompting them to brake sufficiently and in a timely manner. This can be done, for example, via a corresponding visual display in the instrument cluster or head-up display, or via a voice output.

[0031] If, in addition to the metadata based on the swarm data, the vehicle has access to additional relevant information, such as navigation data or sensor data from a vehicle camera or radar sensor, these can be evaluated together. In this case, contradictions may arise between the swarm data and the additional information. The vehicle can then perform a plausibility check and, based on this, decide whether longitudinal control should be based solely on the data generated by the vehicle or available in the vehicle, or whether optimized control can be achieved with the help of the swarm data.

[0032] In Fig.Figure 2 schematically illustrates how a swarm of vehicles traveling along the same curve section collects and reports various parameters, and how a server generates metadata for longitudinal guidance when traveling along the curve section and sends it to a vehicle traveling along the same curve section later. A large number of swarm vehicles F1 to F n travel at different times t1 to t n the same curve section K.

[0033] The swarm vehicles F1 to F n record different parameters P at several, successive points in time when driving along the curve section 1,x to P n,x, which in particular also include their respective position, determined, for example, using a GPS navigation system. The determined parameters are sent to a central server S, which can be provided on the Internet as a backend server for the vehicle swarm and is part of an IT infrastructure not further described here. The transmission takes place via a wireless data radio connection between the respective vehicle and the server S, for example, using mobile radio units provided in the vehicles.

[0034] The server S in turn evaluates the received parameters P 1,x to P n,x statistically using suitable algorithms and, based on the result, generates metadata M for the longitudinal guidance when driving on the curve section, stores this in a database and transmits it to a vehicle F n+1This transmission can also be carried out, in particular, via a mobile network. During the statistical evaluation of the parameters, a plausibility check of the various parameters can also be performed in order to eliminate any outliers from the determined average values of the parameters, which may be due, for example, to a particular driving situation.

[0035] Such a plausibility check can also be performed on the vehicle side to determine whether the swarm data can actually be used for longitudinal control on the upcoming or currently traveled section of road, or whether longitudinal control should only be based on the sensor and navigation data generated by the vehicle. This can also involve initially comparing the swarm and sensor data using correlation methods. Furthermore, one or more quality criteria can be defined and limit values set for them to enable the vehicle to decide whether the metadata based on the swarm data should be used. The limit values can, in turn, be influenced by a variety of factors. List of reference symbols 1 - 5 process steps F1, F n Swarm vehicles F n+1 vehicle t1, t n , t n+1 Times of cornering K Curve section P1, P n parameter M Metadata S Server

Claims

[1] Method for supporting the longitudinal control of a vehicle (Fn+1) using metadata, wherein - metadata (M) to support the longitudinal control before driving on a section of road are received (4), wherein the metadata has been generated by the data received from a plurality of other vehicles (F1, F n ) parameters recorded when driving through the same section of the route (P1, P n ) of the respective vehicle and / or the respective vehicle environment and / or time-related parameters (P1, P n ) have been recorded and statistically evaluated; - for the route section, additional sensor data of the vehicle's surroundings are recorded (1) using one or more sensors of the vehicle (Fn+1) and / or digital map data of a navigation system of the vehicle (Fn+1) are used; - plausibility is checked by comparing the metadata (M) with the recorded sensor data and / or the digital map data; and - depending on the result of the plausibility check, the longitudinal control is carried out based on the sensor data recorded by the vehicle (Fn+1) and / or the digital map data available in the vehicle (Fn+1) or the metadata is used for optimised longitudinal control (5). [2] Method according to claim 1, wherein the route section comprises a curve (K) and the curve, in particular the curve radius, and / or a recommended speed for the vehicle when driving through the curve is determined by means of the metadata (M). [3] Method according to claim 1 or 2, wherein - a quality criterion for the metadata (M) is determined for the route section; - the quality criterion is compared with a predefined limit value; and - the metadata (M) are only used for automated longitudinal control (5) if the quality criterion is below the predefined limit. [4] Method according to one of the preceding claims, wherein the metadata is provided by a driver assistance system of the vehicle (F n+1 ) can be used for automated longitudinal control (5). [5] Method according to one of the preceding claims, wherein one or more of the following parameters (P1, P n ) of the respective vehicle is recorded by sensors: - the geographical coordinates; - the yaw rate; - the speed; - the lateral acceleration; and / or - the steering angle. [6] Method according to one of the preceding claims, wherein one or more of the following parameters (P1, P n ) of the respective vehicle environment are recorded: - the weather; and / or - the lighting conditions. [7] Method according to one of the preceding claims, wherein one or more of the following time-related parameters (P1, P n ) are recorded: - the time; - the date; and / or - the day of the week. [8] Assistance system for carrying out a method according to one of claims 1 to 7.

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