Method for operating a vehicle designed to be driven in an automated, particularly highly automated or autonomous manner

Abstract

The invention relates to a method for operating a vehicle (F) designed to be driven in an automated, in particular highly automated or autonomous manner. Ego ), wherein the driving control is adapted to the climate conditions (W) of the vehicle environment (U). It is provided that, when the climate conditions (W) differ from a predetermined standard, the driving control is adjusted according to a reference control parameter (R), wherein, in order to generate the reference control parameter (R), a reference to the vehicle environment (U) is determined. Ego ) driving behaviors of a plurality of adjacent vehicles (F1-F8), and determining a speed average value, a distance average value (d M ), average acceleration (a M ) and the average value of deceleration (‑a M ) and is taken into account when generating the reference control parameter (R).

Description

Technical Field

[0001] The invention relates to a method for operating a vehicle designed to be driven in an automated, in particular highly automated or autonomous manner, according to the preamble of claim 1 . Background Art

[0002] DE 10 2016 012 774 A1 discloses a method for operating an autonomously drivable vehicle, wherein a plurality of different driving strategies are output to a vehicle user for selection, wherein a corresponding vehicle driving mode is determined as a function of at least one driving strategy selected by the vehicle user, and the vehicle is operated as a function of the determined driving mode. The determined driving mode is wirelessly transmitted to traffic participants who are themselves located in the vehicle's surroundings. Summary of the invention

[0003] The object of the present invention is to specify a method for operating a vehicle which is designed to be automated, in particular highly automated or autonomously driven, which is improved compared to the prior art.

[0004] This object is achieved according to the invention by a method having the features of claim 1 .

[0005] Advantageous embodiments of the invention are the subject matter of the dependent claims.

[0006] In a method for operating a vehicle designed for automated, in particular highly automated or autonomous driving, driving control is adapted to climate conditions in the vehicle's surroundings.

[0007] The present invention provides that, when the climate conditions differ from a prescribed standard, the driving control is adjusted with the aid of reference control parameters, wherein, in order to generate the reference control parameters, the driving behavior of a plurality of vehicles adjacent to the vehicle in the vehicle environment is determined, and wherein, based on the determined driving behavior, a speed average value, a distance average value, an acceleration average value and a deceleration average value are determined and taken into account when generating the reference control parameters.

[0008] The method is used to adapt the driving behavior of the vehicle to the driving behavior of other vehicles. In particular, the adaptation is performed when unfavorable weather conditions are detected and when it is also determined that other vehicles detected by the highly automated vehicle are generally driving in a more defensive manner than in normal weather conditions. Since the behavior of the vehicle is generally adapted to that of other vehicles, the risk of accidents can be significantly reduced. In addition, the method can be used to take into account regional or cultural characteristics that are relevant to driving behavior.

[0009] In one embodiment, vehicles are taken into account when determining the driving behavior, which are located in the vehicle environment near the vehicle at different times. In this case, not only vehicles that are currently in the vehicle environment are detected, but also vehicles that were located in the vehicle environment within a predefined past period. In other words, vehicles that were detected a short time ago but are currently outside the detection range of the host vehicle are also taken into account because the host vehicle has, for example, overtaken other vehicles. This increases the efficiency of the method, so that the driving behavior is adjusted in an improved manner.

[0010] In another embodiment, the distance threshold, the acceleration threshold and the deceleration threshold are determined based on the determined speed average value. For example, the distance threshold increases as the speed average value increases, while the acceleration threshold or the deceleration threshold decreases as the speed average value increases. The distance threshold is especially a safety distance related to the speed average value, which is set as the minimum distance between two vehicles to avoid accidents, especially collisions. For example, the safety distance between two vehicles is calculated according to the empirical formula "safety distance = half of the speedometer value in meters", wherein the speed average value is used as the speedometer value.

[0011] For example, when the distance mean value differs from a distance threshold, the acceleration mean value differs from an acceleration threshold and / or the deceleration mean value differs from a deceleration threshold, the threshold value forms a reliable criterion for adjusting the driving control of the vehicle when the driving control is adjusted with the aid of the reference control parameters.

[0012] In addition, one embodiment provides that a correction factor is applied to the speed average value, the distance average value, the acceleration average value and / or the deceleration average value. The correction factor includes, for example, regional factors and / or cultural factors, so that the requirements for safety and comfort can be considered according to the region. For example, traffic participants in northern latitudes and / or mountainous areas are accustomed to snow-covered roads and therefore have safer driving behavior in winter climate conditions than traffic participants in southern latitudes and / or plains. Traffic participants with safer driving behavior therefore drive with less defense than other, especially unskilled, traffic participants. The driving behavior of traffic participants therefore varies from region to region, wherein the method takes the differences into account.

[0013] Another embodiment provides that the classification of adjacent vehicles is carried out according to the vehicle type. It is therefore possible to only take into account vehicles that have the same vehicle type as the vehicle when determining and analyzing the driving behavior of other vehicles. If the vehicle is, for example, a car, only vehicles that are also cars are taken into account. All other vehicles, such as trucks, buses, special vehicles, cyclists and other traffic participants, such as pedestrians, are not taken into account when determining and analyzing the driving behavior. This allows the driving behavior of the vehicle to be adapted to vehicles of the same model. This prevents the acceleration behavior of a vehicle designed, for example, as a car from being adjusted to the acceleration behavior of a truck or bus, because this could be seen as interference by other cars.

[0014] Furthermore, one embodiment provides that only vehicles whose model corresponds to the model of the host vehicle, which are located in the same lane as the host vehicle, and which are located in an adjacent lane extending parallel to the lane of the host vehicle at a specified distance are taken into account when determining the driving behavior. In the latter case, it is checked, for example, whether the adjacent lane branches off or is blocked within a distance of, for example, 2 kilometers in front of the vehicle. If this is the case and this does not apply to the host lane, the vehicle in the adjacent lane is not taken into account when determining and analyzing the driving behavior.

[0015] The course and state of the adjacent lanes can be determined, for example, based on a map stored in the vehicle or in an off-board data processing unit, information from a traffic information center and / or optical detection of the vehicle surroundings. The off-board data processing unit is, for example, a backend server that determines the course and / or state of the adjacent lanes by means of vehicle-to-X communication.

[0016] In order to determine the weather conditions, various measured, calculated and / or stored parameters can be taken into account. In particular, the weather conditions can be determined based on a determined friction value of the road surface, a determined precipitation intensity, a measured ambient temperature, an image of the environment captured by means of at least one camera, a radar sensor and / or a lidar sensor and / or information from an off-board climate service or a traffic control center. The friction value is determined and provided, for example, by an electronic stability program of the vehicle. The precipitation intensity can be measured by a rain sensor or determined based on a signal measured by a rain sensor. The ambient temperature can be detected by a temperature sensor. Based on the image of the environment captured by the camera, for example, a snow-covered road can be identified, for example, because the contrast between the road surface and the lane markings is very low or the lane markings are not recognizable. Similarly, the environmental data collected by means of radar sensors and / or lidar sensors can be evaluated accordingly, because the reflective properties of unsnow-covered roads and snow-covered roads are completely different. It is therefore possible to determine with the help of this parameter whether normal weather conditions exist that exclude slippery roads and / or impaired visibility caused by, for example, precipitation, or whether unfavorable weather conditions exist that include slippery roads and / or impaired visibility caused by, for example, precipitation.

[0017] Furthermore, one embodiment provides that the correspondence of the reference control parameters to the climate conditions is continuously checked. In this case, it is continuously checked whether the climate conditions have changed and whether the driving control therefore still needs to be adjusted with the aid of the reference control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The embodiments of the present invention are described in detail below with reference to the drawings, wherein:

[0019] Figure 1 A vehicle environment with a vehicle and a plurality of adjacent vehicles is schematically shown from a bird's eye perspective,

[0020] Figure 2 A process diagram showing a method for operating an autonomously driven vehicle.

[0021] Parts that correspond to one another are provided with the same reference symbols in all the figures. DETAILED DESCRIPTION

[0022] Figure 1 The vehicle surroundings U are schematically shown from a top-down perspective.

[0023] The vehicle environment U includes a road FB having three lanes FS1-FS3, wherein a plurality of vehicles F Ego , F1-F8, which move along the road FB in the driving direction x. There are two vehicles F2, F6 and the main vehicle F in the middle lane FS2. Ego , which is hereinafter referred to as vehicle F Ego . This car F Ego At this time, it is located between vehicles F2 and F6. There are three other vehicles F1, F3-F5, F7, and F8 on other lanes FS1 and FS3 respectively. Ego Designed for automated, in particular highly automated or autonomous driving. Adjustable parameters such as maximum speed, maximum acceleration and / or maximum deceleration and minimum distance are usually designed such that the vehicle F Ego In normal weather conditions W, for example, non-slip roads FB and the absence of precipitation that significantly impairs visibility, the traffic situation can be handled reliably. In adverse weather conditions W, for example, when the road is slippery and / or visibility is impaired due to precipitation, the driver usually adapts his driving behavior to the weather conditions W that occur during manual driving. For example, he reduces the driving speed, maintains a greater distance from the traffic participants ahead, and generally reduces driving dynamics. In other words, the driver's driving behavior in adverse weather conditions W is more defensive than in normal or good weather conditions W.

[0024] If the vehicle F EgoIn such adverse weather conditions W, the vehicle F may be adjusted in highly automated driving according to the parameters designed for normal weather conditions W. Ego The passengers and / or other traffic participants consider it as improper driving behavior. Therefore, a method is proposed, whereby the vehicle F is controlled under adverse weather conditions W. Ego The control of the highly automated driving is adapted to the driving behavior of the other vehicles F1-F8.

[0025] In the method described in detail below, the local climate conditions W (see Figure 2 ). Various measured, calculated and / or stored parameters may be taken into account for determining the weather condition W. In particular, the weather condition W may be determined based on a determined road surface friction value, a determined precipitation intensity, a measured ambient temperature, at least one parameter provided on the vehicle F Ego The friction value is determined by cameras in and / or on the vehicle, images of the environment captured by radar sensors and / or lidar sensors, and / or information from an external climate service or traffic control center. Ego The intensity of precipitation can be determined and provided by the electronic stability program set in the vehicle F Ego The ambient temperature can be determined by the rain sensor on the vehicle or by the signal measured by the rain sensor. Ego The temperature sensor of the vehicle environment U can be used to detect the temperature of the road surface. Based on the environment image captured by the camera, it can be detected, for example, whether the road FB is covered with snow. This can be detected, for example, when evaluating the environment image based on the low contrast between the road surface and the lane markings or the lane markings that are not recognizable. Similarly, the vehicle environment U can be detected by means of the vehicle F Ego When evaluating the environmental data provided at this time, snow covering can be inferred based on the reflective properties of the road FB, because the reflective properties of the road FB in the snow-covered situation are completely different from those in the unsnowed situation.

[0026] Therefore, it can be determined based on the above parameters whether a normal weather condition W that excludes slippery roads and / or impaired visibility caused by, for example, precipitation occurs, or whether an unfavorable weather condition W that includes slippery roads and / or impaired visibility caused by, for example, precipitation occurs. Ego The highly automated driving of the vehicle F1-F8 is adjusted and / or controlled without being affected by the driving behavior of other vehicles F1-F8.

[0027] Under unfavorable weather conditions W, the vehicle F Ego The control of highly automated driving is adapted to the driving behavior of other vehicles F1-F8. The control of highly automated driving particularly involves the adjustment of driving speed, driving dynamics, vehicle acceleration and / or deceleration and safety distance.

[0028] As shown in this embodiment, the vehicle F Ego Adjacent vehicles F1-F8, which are composed of vehicle F Ego The detection of the vehicles F1-F8 can be performed by means of a sensor system (not shown) provided on the vehicle F Ego The detection is performed by using cameras, lidar sensors and / or radar sensors in and / or at the vehicle environment U. Vehicles detected in a predetermined past period are also taken into account (not shown). In other words, not only the vehicles F1-F8 currently in the vehicle environment U are detected, but also the vehicles that were located in the vehicle environment U in a predetermined past period are detected. Ego Vehicles outside the detection range of the vehicle are also taken into account, because the vehicle F Ego For example, you have overtaken another vehicle.

[0029] In order to determine and analyze the driving behavior of the detected vehicles F1-F8, the vehicle F Ego , the distances d18, d20, d34, d87, d06, d45 between vehicles F1-F8. In addition, the speed, acceleration and deceleration of vehicles F1-F8 are determined respectively. In a fourth step, the speed average is calculated from the determined speeds. The acceleration average a is calculated from the determined accelerations M (See Figure 2 ) Calculate the deceleration average value - a from the determined deceleration M (See Figure 2 ) Calculate the distance average d from the determined distances d18, d20, d34, d87, d06, d45 M (See Figure 2 ), vehicle F Ego The distance d20 to the preceding vehicle F2 is not taken into account. In particular, the mean value is formed here. Alternatively, an arithmetic mean value can also be formed. The mean value can be determined by means of a calculation unit (not shown), such as a vehicle control unit, and is compared with the vehicle F Ego The above sensor system is connected.

[0030] According to the determined speed average value, the distance threshold d is determined S , acceleration threshold a S and deceleration threshold-a S (See Figure 2 ). For example, the distance threshold d S As the average speed increases, the acceleration threshold a S Or deceleration threshold - a S As the average speed increases, it decreases. SIn particular, the safety distance is dependent on the speed average value and is set as the minimum distance between two road users to avoid accidents and in particular collisions. For example, the safety distance is determined according to the empirical formula "safety distance = half the speedometer value in meters", wherein the speed average value is used as the speedometer value.

[0031] In addition, it can be provided that the average speed and the average distance d M , average acceleration a M and / or deceleration average value-a M A correction factor is applied. The correction factor includes, for example, regional and / or cultural factors, so that the requirements for safety and comfort can be considered according to the region. For example, traffic participants in northern latitudes and / or mountainous areas are accustomed to snow-covered roads FB and therefore have safer driving behavior under winter climate conditions W than traffic participants in southern latitudes and / or plain areas. Traffic participants with safer driving behavior therefore drive with less defensiveness than other, especially unskilled, traffic participants. The driving behavior of traffic participants therefore varies from region to region, wherein the method described herein takes the differences into account.

[0032] Here, the threshold value is used to adjust the vehicle F Ego The reliability standard of highly automated driving control is M Different from the distance threshold d S , average acceleration a M Different from the acceleration threshold a S and / or deceleration average value-a M Different from deceleration threshold-a S , it can be inferred that the other vehicles F1-F8 are driving in a more defensive manner on average than in normal weather conditions W. In other words, the drivers of vehicles F1-F8 adapt their driving behavior to weather conditions W. If this is the case, then the driver of the host vehicle F1-F8 is also Ego The control of the highly automated driving is adapted to the climate conditions W. If this is not the case, no adaptation takes place.

[0033] With the help of reference control parameter R, the vehicle F Ego The control of highly automated driving is adapted to the climate conditions W (see Figure 2 ). In particular, this makes the vehicle F Ego The control of highly automated driving of the vehicle F is adapted to the climate conditions W, that is, Ego The driving behavior of a reference vehicle is simulated. The reference vehicle is a fictitious vehicle that travels at a speed corresponding to the determined speed average value, and that complies with the distance average value d M The distance to the vehicle ahead, and when the speed changes, at most according to the average acceleration aM Or the average value of deceleration - a M The reference control parameter R therefore includes the speed average, the distance average d M , average acceleration a M and / or deceleration average value-a M In this case, the adjustment is carried out according to the reference control variable R, for example, in the form of a slow technical adjustment process.

[0034] In order to determine and analyze the driving behavior of other vehicles F1-F8, the vehicles F1-F8 can optionally be classified according to the type of vehicle. It is thus possible to consider only the vehicles F1-F8 that have a similar driving behavior to the vehicle F1-F8. Ego The same model. Ego For example, if it is a car, only the car type is taken into account. All other types of vehicles, such as trucks, buses, special vehicles, cyclists and other traffic participants such as pedestrians are not taken into account when determining and analyzing driving behavior. Ego The acceleration behavior of is adapted to that of a truck or bus, since it may be seen as a disturbance by other cars.

[0035] In addition, only the following vehicles are considered when determining the driving behavior, which are located in the same direction as the vehicle F Ego The vehicle FS2 is located on the same lane FS2, and on adjacent lanes FS1, FS3 which extend parallel to the vehicle FS2 at a predetermined distance, in particular ahead. In this case, for example, the adjacent lanes FS1, FS3 are checked to see whether they are within the vehicle FS2. Ego The distance ahead is, for example, within 2 km of the fork or congestion. If this is the case and this does not apply to the own lane FS2, the vehicles F1, F3-F5, F7, F8 located in the adjacent lanes FS1, FS3 are not taken into account in determining and analyzing the driving behavior. In this case, for example, in urban areas, the distance to the residential area can be predefined as the distance ahead, provided that it is not the only turning lane.

[0036] The direction and state of the adjacent lanes FS1 and FS3 can be based on the data stored in the vehicle F Ego Or it can be determined from a map in an off-board data processing unit, information from a traffic information center and / or optical detection of the vehicle environment U. The off-board data processing unit is, for example, a backend server which detects the course and / or state of the adjacent lanes FS1 , FS3 by means of vehicle-to-X communication.

[0037] Figure 2 An exemplary process diagram of the aforementioned method is shown.

[0038] In the first step S1, according to Figure 1 Description to determine the climate condition W.

[0039] In a second step S2, it is checked whether the climatic conditions W are favorable. If so, the method returns to the first step S1. If the climatic conditions W are unfavorable, a third step S3 follows in which the climatic conditions W are determined according to the first step S2. Figure 1 The driving behavior of the detected vehicles F1-F8 is determined by the description of the vehicle F1-F8. This includes: determining the distance d18, d20, d34, d87, d06, d45, speed, acceleration and / or deceleration of the vehicles F1-F8; forming an average value a M , -a M d M and threshold a S , -a S d S .

[0040] In a fourth step S4, the average value a is checked. M , -a M d M Is it different from the threshold a S , -a S d S When the distance from the mean value d M Different from the distance threshold d S , average acceleration a M Different from the acceleration threshold a S and / or deceleration average value-a M Different from deceleration threshold-a S When the fifth step S5 follows. If the average value a M , -a M d M With threshold a S , -a S d S If yes, the method returns to the third step S3 or optionally returns to the first step S1.

[0041] In the fifth step S5, the vehicle F is adjusted by means of the reference control parameter R. Ego Furthermore, the correspondence of the reference control parameter R to the climate conditions W is continuously checked. In particular, it is continuously checked whether the climate conditions W have changed and whether an adjustment of the driving control with the aid of the reference control parameter R is therefore still necessary.

Claims

1. A method for operating a vehicle designed to be driven in an automated manner (F Ego ), wherein Driving control is adjusted according to the weather conditions (W) of the vehicle environment (U), It is characterized in that when the weather condition (W) differs from a prescribed standard, the driving control is adjusted by means of a reference control parameter (R), wherein: - In order to generate the reference control parameter (R), determine the vehicle (F Ego ) the driving behaviors of multiple adjacent vehicles (F1-F8), and - Determine the speed average value and the distance average value (d M ), average acceleration (a M ) and the average value of deceleration (-a M ), and is taken into account when generating the reference control parameter (R), -Distance threshold (d S ), acceleration threshold (a S ) and deceleration threshold (-a S ) is determined according to the determined speed average value.

2. The method according to claim 1, characterized in that When determining the driving behavior, the vehicle (F Ego ) nearby vehicles (F1-F8).

3. The method according to claim 1, characterized in that: When the average distance (d M ) is different from the distance threshold (d S ), the average value of the acceleration (a M ) is different from the acceleration threshold (a S ) and / or the average value of the deceleration (-a M ) is different from the deceleration threshold (-a S ), the driving control is adjusted with the help of the reference control parameter (R).

4. The method according to claim 1 or 2, characterized in that: The average speed and the average distance (d M ), the average value of the acceleration (a M ) and / or the average value of the deceleration (-a M ) is subject to a correction factor.

5. The method according to claim 1 or 2, characterized in that: Adjacent vehicles (F1-F8) are classified according to their vehicle types.

6. The method according to claim 5, characterized in that Only the following vehicles (F1-F8) are considered when determining the driving behavior: - Its model meets the requirements of this vehicle (F Ego ) model, - It is located at the same position as the vehicle (F Ego ) on the common lane (FS2), and - It is located at a specified distance and parallel to the vehicle (F Ego ) lane (FS2) is extended onto the adjacent lane (FS1, FS3).

7. The method according to claim 6, characterized in that: The direction and state of the adjacent lanes (FS1, FS3) are based on the data stored in the vehicle (F Ego ) or a digital map in an off-board data processing unit, information from a traffic information center and / or determined according to optical detection of the vehicle environment (U).

8. The method according to claim 1 or 2, characterized in that: The climate condition (W) is determined according to the following conditions: - the determined road friction value, - the determined precipitation intensity, - the measured ambient temperature, - environmental data collected by means of at least one camera and / or radar sensor, and / or - Information from the off-board climate service or traffic control centre.

9. The method according to claim 1, characterized in that: The weather condition (W) is determined according to the environmental data collected by the lidar sensor.

10. The method according to claim 1 or 2, characterized in that: The compliance of the reference control parameter (R) with the climate conditions (W) is continuously checked.

11. The method according to claim 1, characterized in that: The vehicle (F Ego ) are designed to drive in a highly automated manner.

12. The method according to claim 1, characterized in that: The vehicle (F Ego ) are designed to be driven in an autonomous manner.

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