DE102019126400A1 - Method for operating a motor vehicle with a LIDAR sensor - Google Patents

Abstract

The invention relates to a method for operating a motor vehicle (2) with a LIDAR sensor (4), with the following steps: (S200) in response to a request signal (AS) determining a search area (28a, 28b), (S400) evaluating LIDAR Data (LD) in the search area (28a, 28b) to detect an obstacle (26a, 26b) in the search area (28a, 28b), and (S500) generating an output signal (AU) when in the search area (28a , 28b) an obstacle (26a, 26b) has been detected.

Description

The invention relates to a method for operating a motor vehicle with a LIDAR sensor. The invention also relates to a computer program product and such a motor vehicle.

Motor vehicles can be designed for so-called autonomous driving. An autonomously driving motor vehicle is a self-driving motor vehicle that can drive, control and park without the influence of a human driver (highly automated driving or autonomous driving). In the event that no manual control is required on the part of the driver, the term robot car is also used. Then the driver's seat can remain empty; possibly the steering wheel, brake and accelerator pedals are not available.

Autonomous vehicles of this type can use various sensors to detect their surroundings and use the information obtained to determine their own position and that of other road users, navigate to a destination in cooperation with the navigation software and avoid collisions on the way there.

Such a sensor can be a so-called LIDAR sensor. LIDAR (abbreviation for light detection and ranging), describes a radar-related method for optical distance and speed measurement as well as for remote measurement. Instead of radio waves as in RADAR, laser beams are used.

LIDAR sensors offer the advantage that they can scan a 360-degree area around the motor vehicle with high resolution and speed. Typically, a LIDAR sensor uses an array of laser-based sensors (e.g. 64) that rotate at high speed (several 100 n / min). The LIDAR sensor is then able to detect obstacles hit by a laser beam. It is thus possible to determine the coordinates of every hit or of every obstacle in the vicinity of the motor vehicle. By evaluating this LIDAR data, it is also possible to obtain information about the topology of the terrain in the area around the motor vehicle. Such methods are, for example, from US 9 052 721 B1 or the US 9 097 800 B1 known.

However, the evaluation of the LIDAR data, e.g. for trajectory planning and obstacle detection in real time, especially due to the amount of data to be processed and the complexity of the planning algorithms, is very computationally intensive.

There is therefore a need to show ways in which the need for computing power can be reduced here.

• auf ein Anforderungssignal hin Bestimmen eines Suchbereichs,
• Auswerten von LIDAR-Daten in dem Suchbereich, um ein Hindernis in dem Suchbereich zu erfassen., und
• Erzeugen eines Ausgangssignals, wenn in dem Suchbereich ein Hindernis erfasst wurde.

The object of the invention is achieved by a method for operating a motor vehicle, with the steps:

• determining a search area in response to a request signal,
• Evaluating lidar data in the search area in order to detect an obstacle in the search area., And
• Generating an output signal when an obstacle has been detected in the search area.

A heuristic search is thus implemented in which only LIDAR data that relate to a predetermined area of the overall surroundings of the motor vehicle are evaluated. In this way, a reduction in the memory / CPU load and an increase in computing power can be achieved. Furthermore, it can be provided that an evaluation of LIDAR data relating to the search area is terminated if an obstacle has been detected in the search area. In other words, if it has been determined that an obstacle is blocking a parking lot, for example, no computing power is used for a further evaluation of the LIDAR data, since it is irrelevant whether, for example, a truck or car is blocking the parking lot or how big they are. In this way, the required computing power can be reduced again.

According to one embodiment, the request signal is provided to a further step by a driver detection module and / or an operation detection module and / or a situation detection module. The driver detection module is designed to detect a driving situation. To do this, the driver registration module reads values from internal vehicle sensors and other signals (e.g. via a CAN bus) and evaluates them. The operation detection module is designed to determine which specific operating modes of the activated driver assistance systems are currently active. The situation acquisition module acquires data relating to an environmental condition, such as traffic or weather conditions, and evaluates them. In the event of a wide variety of situations, the LIDAR data provided by the LIDAR sensor can be used and evaluated in a way that conserves computer resources.

According to a further embodiment, shape parameters for the search area are determined in a further step. The shape parameters can define, for example, whether the search area should be two- or three-dimensional, what geometric shape the search area should have, a size and / or shape and / or a curvature and / or an inclination of the Search area and / or a position of the search area in relation to a position of the motor vehicle. This enables the search areas to be adapted quickly and flexibly without using up computer capacities.

The invention also includes a computer program product and such a motor vehicle.

• 1 in schematischer Darstellung eine Systemübersicht.
• 2 in schematischer Darstellung ein Verkehrsszenario mit dem Kraftfahrzeug.
• 3 in schematischer Darstellung einen Verfahrensablauf.

The invention will now be explained with reference to a drawing. Show it:

• 1 a schematic representation of a system overview.
• 2 a schematic representation of a traffic scenario with the motor vehicle.
• 3 a schematic representation of a process sequence.

It is first applied to the 1 Referenced.

A motor vehicle is shown 2 as well as other components of the motor vehicle 2 .

The components shown are a LIDAR sensor 4th , a driver registration module 6th , an operation registration module 8th , a situation detection module 10 , a requirements module 12th , a search area definition module 14th , an evaluation module 16 , an obstacle detection module 18th , a driver assistance system 20th and a control module 22nd .

The car 2 and the other components mentioned can each have hardware and / or software components for the tasks and / or functions described below.

The car 2 is designed as a car in the present embodiment. The motor vehicle is also 2 in the present exemplary embodiment for autonomous driving, ie designed as a self-driving motor vehicle. In a departure from the present exemplary embodiment, the motor vehicle 2 also be designed according to one of the SAE autonomy levels 1 to 5.

The level of autonomy 0 does not represent any automation, the level of autonomy 1 represents driver assistance, the level of autonomy 2 represents a partial automation, the level of autonomy 3 represents a conditional automation, the level of autonomy 4th represents a high level of automation, and autonomy level 5 represents full automation.

The LIDAR sensor 4th is trained to handle LIDAR data LD (please refer 3 ) representative of the surroundings of the motor vehicle 2 provide.

The driver registration module 6th is designed to detect a driving situation. To do this, the driver registration module reads 6th Values from internal vehicle sensors and other signals (e.g. CAN bus) and evaluates them.

The driving situation provides information about the vehicle status and condition. If, for example, the drive is switched on and the accelerator pedal is pressed and the speed is, for example, 30 km / h, then the motor vehicle moves and accelerates 2 . The detection of the driving situation can be based on an analysis of currently available values, on the analysis of past values, or on a prediction based on a value analysis. The one from the driver registration module 6th recorded and determined data are assigned to the requirements module 12th made available.

The operational registration module 8th is designed to determine which specific operating modes of the activated driver assistance systems are currently active. The modes can be detected by internal vehicle signals. For example, a scanning mode of a parking assistance system is active when a suitable parking space is sought for parking. The from the operation registration module 8th recorded and determined data are assigned to the requirements module 12th made available.

The situation detection module 10 records data relating to an environmental condition, such as traffic or weather conditions, and evaluates them. To do this, the situation detection module reads 10 Data from vehicle sensors, connection data, or GPS data and evaluates them. The from the situation detection module 10 recorded and determined data are assigned to the requirements module 12th made available.

The requirements module 12th can through a request signal AS (please refer 3 ) to be activated. The request signal AS can in the present exemplary embodiment from the driver detection module 6th , the operation registration module 8th , or the situation detection module 10 be generated. In contrast to the present exemplary embodiment, the request signal AS also from an external source outside the motor vehicle 2 be provided, for example when the motor vehicle 2 for parking one outside of the motor vehicle 2 located driver is steered or controlled with the help of a handheld, such as a smartphone.

The search scope definition module 14th is designed to have a shape of a search area 28a , 28b (please refer 2 ) by determining shape parameters GP (please refer 3 ) to be determined. In other words, the search area definition module 14th determines a virtual geometric shape of the search area 28a , 28b .

The design parameters GP depending on the instance that the request signal AS provided can be set differently.

The shape parameters GP can, for example, specify whether the search area 28a , 28b should be two- or three-dimensional. For example, a two-dimensional search area can be used for a parking maneuver 28a , 28b suffice, while a cruise control a three-dimensional search area 28a , 28b may require.

Furthermore, the shape parameters GP eg a geometric shape of the search area 28a , 28b establish. The geometric shape can be any 2D / 3D polygon or a combination of polygons. The choice of shape can be made depending on the instance issuing the request AS provided can be set differently. For example, if a parking assistance system is being scanned, the geometric shape can be a simple rectangle.

The shape parameters GP can change the size and / or shape of the search area 28a , 28b establish. Typically, the size depends on a driving situation (for example a change in length of a rectangular parallelepiped in relation to a vehicle speed), on an operating mode (for example standard size while scanning a parking assistance system) and / or on an external situation.

Furthermore, the shape parameters GP a curvature of the search area 28a , 28b establish. So it is possible to apply a certain curvature to the geometric shape of the search area 28a , 28b to apply, that is, to transform them. For example, one side of a cubic geometric shape can be curved with respect to a road curvature and / or a steering angle.

The shape parameters GP can also include a slope of the search area 28a , 28b set in relation to a road incline.

Finally, the shape parameters GP a position of the search area 28a , 28b in relation to a position of the motor vehicle 2 establish. For example, a virtual area, which is represented by a cuboid and is positioned in front of the motor vehicle, can be arranged in order to recognize a front obstacle. Furthermore, a cuboid can be positioned on one side of the vehicle in order to detect a lateral obstacle.

The evaluation module 16 is trained to pre-select the LIDAR data LD perform, to the effect that the data are selected that make up the search area 28a , 28b are relevant for obstacle detection. The evaluation module 16 enables the memory / CPU load to be reduced and the computing power to be increased. The evaluation module is used for this 16 including those from the search area definition module 14th provided shape parameters GP .

The obstacle detection module 18th is trained to determine if there is an obstacle 26a , 26b (please refer 2 ) in the search area 28a , 28b is located or not. This is done by checking whether one of the LIDAR hit coordinates is within the search area 28a , 28b is located. In order to reduce false alarms and / or signal noise, it can be provided that at least a predetermined minimum number of hits in the search area 28a , 28b must lie. Additionally or alternatively, it can be provided that a predetermined minimum number of hits must lie within a predetermined period of time.

The driver assistance system 20th can be a parking assistance system or another assistance system.

The control module 22nd controls the named components and controls the data exchange between them.

It is now additionally on 2 Referenced.

A traffic scenario is shown in which the motor vehicle 2 with the LIDAR sensor 4th and the driver assistance system designed as a parking assistance system in the present exemplary embodiment 20th drives down a street in search of a parking space. The motor vehicle is moving 2 from the position I. to the position II with activated parking assistance system and passes through the parking spaces 24a , 24b , 24c both of which the parking lot 24a as well as the parking lot 24c each from an obstacle 26a , 26c in the form of parked vehicles are occupied while the parking lot 24b is still free.

In advance was made by the driver of the motor vehicle 2 this activates the parking assistance system, which then sends the request signal AS has generated.

To the request signal AS the search area definition module then has it 14th by determining shape parameters GP the shape of the search areas 28a , 28b set. In the present exemplary embodiment, the search areas 28a , 28b a three-dimensional, cuboid basic shape.

The evaluation module 16 then evaluates those at the positions I. and II captured lidar data LD and determines the data that is required for the respective search areas 28a , 28b are relevant.

The obstacle detection module 18th then evaluates the respective from the evaluation module 16 processed data to determine if there is an obstacle 26a , 26b in the search area 28a , 28b is located or not.

In the in 2 The scenario shown is the parking lot 24a due to the obstacle 26a occupied. However, this does not apply to the parking lot 24b .

In the present embodiment it is provided that the evaluation of LIDAR data LD regarding the search area 28a canceled when in the search area 28a the obstacle 26a was recorded. In other words, when an obstacle has been found 26a the parking lot 24a No more computing power is blocked for further evaluation of the LIDAR data LD is used because it is irrelevant whether, for example, a truck or a car is blocking the parking lot or how large their dimensions are. In this way, the required computing power can be reduced again.

It will now be made with additional reference to FIG 3 a procedure explained.

In a first step S100 becomes the request signal AS from the driver registration module 6th and / or the operation registration module 8th and / or the situation detection module 10 provided

In a further step S200 determined on the request signal AS for example the requirements module 12th the search areas 28a , 28b .

In a further step S300 sets the search area definition module 14th the shape of the search areas 28a , 28b by determining the shape parameters GP firmly.

In a further step S400 determines the obstacle detection module 18th whether there is an obstacle 26a , 26b in the search area 28a , 28b is located or not, where previously, for example, the evaluation module 16 a preselection of the lidar data LD performed to select the data that includes the search area 28a , 28b are relevant for obstacle detection.

In a further step S500 becomes an output signal AU generated when in the search area 28a , 28b an obstacle 26a , 26b was recorded. The output signal AU in the present exemplary embodiment is a logical variable which is based on the detection of an obstacle 28a , 28b towards the value logical one is assigned. Otherwise the logical variable has the value logical zero. The presence of the output signal AU the evaluation of the LIDAR data LD regarding the search area 28a , 28b canceled.

In a departure from the present exemplary embodiment, the sequence of the steps can also be different. Furthermore, several steps can also be carried out at the same time or simultaneously. Furthermore, different from the present exemplary embodiment, individual steps can also be skipped or omitted.

In this way, a reduction in the memory / CPU load and an increase in computing power can be achieved.

List of reference symbols

2

Motor vehicle

4th

LIDAR sensor

6th

Driver registration module

8th

Operation registration module

10

Situation detection module

12th

Requirements module

14th

Search area definition module

16

Evaluation module

18th

Obstacle detection module

20th

Driver assistance system

22nd

Control module

24a

parking spot

24b

parking spot

24c

parking spot

26a

obstacle

26b

obstacle

28a

Search area

28b

Search area

I.

position

II

position

AS

Request signal

AU

Output signal

GP

Shape parameters

LD

LIDAR data

S100

step

S200

step

S300

step

S400

step

S500

step

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• US 9052721 B1 [0005]
• US 9097800 B1 [0005]

Claims (7)

Method for operating a motor vehicle (2) with a LIDAR sensor (4), comprising the steps: (S200) determining a search area (28a, 28b) in response to a request signal (AS), (S400) evaluating LIDAR data (LD) in the search area (28a, 28b) in order to detect an obstacle (26a, 26b) in the search area (28a, 28b), and (S500) Generating an output signal (AU) when an obstacle (26a, 26b) has been detected in the search area (28a, 28b). Procedure according to Claim 1 , wherein in a further step (S100) the request signal (AS) is provided by a driver detection module (6) and / or an operation detection module (8) and / or a situation detection module (10). Procedure according to Claim 1 or 2 , wherein in a further step (S300) shape parameters (GP) for the search area (28a, 28b) are determined. Computer program product designed to carry out a method according to one of the Claims 1 to 3 . Motor vehicle (2) with a LIDAR sensor (4), the motor vehicle (2) being designed to determine a search area (28a, 28b) in response to a request signal (AS) and to determine LIDAR data (LD) in the search area (28a, 28b) in order to detect an obstacle (26a, 26b) in the search area (28a, 28b), and for generating an output signal (AU) when an obstacle (26a, 26b ) was recorded. Motor vehicle (2) according to Claim 5 , wherein a driver detection module (6) and / or an operation detection module (8) and / or a situation detection module (10) of the motor vehicle (2) provide the request signal (AS). Motor vehicle (2) according to Claim 5 or 6th , wherein the motor vehicle (2) is designed to determine shape parameters (GP) for the search area (28a, 28b).

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