CROSS REFERENCE

The present application claims the benefit under 35 U.S.C. § 119 of German Patent Application No. DE 102019200828.3 filed on Jan. 24, 2019, which is expressly incorporated herein by reference in its entirety.

FIELD

The present invention relates to a method for selecting a main object for an assistance function or automated driving function of a driver assistance system or driving system of a motor vehicle, the system including at least one sensor for sensing the traffic environment of the motor vehicle. The present invention further relates to a driver assistance system or driving system for a motor vehicle.

BACKGROUND INFORMATION

Some conventional semi-automated driver assistance systems adjust a vehicle speed in such a way that a preceding vehicle may be followed at a safe distance. Such vehicle-speed controllers are also referred to as ACC (Adaptive Cruise Control) systems. From various radar objects detected by a radar sensor, one main object is determined, the so-called target, which is to be followed. As a rule, the target is located in the traffic lane of the ego vehicle and is driving in front of the ego vehicle.

The target is determined based on rules, by an algorithm suitably generated and implemented in the driver assistance system.

SUMMARY

An object of the present invention is to provide a method as well as a corresponding driver assistance system and driving system, which makes it possible to select a target for an assistance function or automated driving function more efficiently and at the same time reliably.

This object may be achieved according to example embodiments of the present invention. Advantageous further developments and refinements of the present invention are described herein.

For instance, the automated driving function may include a function for driver assistance or a function for semi-automated, conditionally automated, highly automated or fully automated driving. In particular, the automated driving function may include a function for autonomous or semi-autonomous driving.

Correspondingly, the driver assistance system or driving system may include a system for driver assistance or for automated driving, for instance, especially for semi-automated, conditionally automated, highly automated or fully automated driving. In particular, the driving system may be an automated driving system or autonomous driving system.

The method makes it possible to use the second object selection branch in the normal case, and to select the main object for the function through the artificial neural network. This permits the use of a machine learning process (ML, Machine Learning). In comparison to a conventional, rule-based approach, the utilization of a neural network is able to markedly improve the performance of an object selection. In addition, it is advantageous that by aggregating sensor data, the size of the neural network may be kept relatively small. Thus, an efficient calculation is possible when using the neural network, and a small storage-space requirement results for the weights of the neural network. It is especially advantageous that by evaluating the novelty of the traffic situation characterized by the aggregated object data records in relation to the training data of the neural network, the reliability of the process may be increased considerably. It was determined that the performance of machine learning processes in the application case depends strongly on whether a data record to be processed or to be classified is sufficiently comparable to a data record utilized during the training of the learning process. In the extreme case, if a neural network is used exclusively, a systematically false evaluation of the input data records may take place. Because according to an example embodiment of the present invention, the novelty of the traffic situation is evaluated, in the event a novelty exceeds the threshold value, it is possible to switch over to the use of the rule-based object selection branch for selecting the main object for the assistance function or driving function. In this way, the advantages of a rule-based object selection may be combined with the advantages of an object selection with the aid of an artificial neural network, so that a method may be attained which is particularly reliable, and at the same time, as efficient as possible. In particular, it is made possible to combine the performance of a neural network with the demonstrable robustness of a classic, rule-based approach to object selection.

Preferably, in the second object selection branch, the artificial neural network is set up to use a plurality of object data records simultaneously as input variables for selecting the main object for the assistance function or driving function. Compared to an evaluation of specific individual object data records by a neural network and a selection based on the results of the evaluations, the simultaneous use of a plurality of object data records as input variables of the neural network makes it possible to use relationships between the objects characterized by the object data records as an additional information source, as well. Thus, for example, even in the case of missing or poor lane markings on the roadway, it may be inferred from relative positions between the objects, which of the objects is traveling in the traffic lane of the ego vehicle (also referred to as ego traffic lane), and is therefore possible, for instance, as main object for a driver assistance function.

For example, the at least one sensor may include a camera and/or a radar sensor and/or a lidar sensor.

The object data records for which the sensor data is aggregated may correspond to moving objects and/or stationary objects. For example, stationary objects may include a guardrail. Object data records of stationary objects may contain information about a road boundary, for instance. Thus, an object in the form of a guardrail may be used, for instance, to estimate a road boundary. For example, the rule-based object selection branch may be set up, using the position of the ego vehicle relative to the estimated road boundary and relative to detected dynamic objects, and possibly based on their position relative to the estimated road boundary, to ascertain which object is driving in front of the ego vehicle in the same traffic lane. If the sensor includes a camera, then lane markings detectable in the video image may also be used to select the main object.

For instance, the aggregated sensor data in the form of an object data record may correspond to one object like, e.g., a vehicle or a pedestrian. Thus, multiple object data records may correspond to a list of objects. Each object may be described in the object data record by a point in a state space, with the point being able to have one or more of the following elements, for example, including: a lateral position, a longitudinal position, a velocity, an acceleration, an object orientation, an extension such as length or width of the object in Cartesian coordinates, or variants of the variables indicated. In particular, the aggregating of sensor data may markedly reduce the scope of information as against the raw data (sensor data), especially in comparison to a video image or in comparison to a large number of radar detections which stem from the same object.

In the second object selection branch, the artificial neural network preferably has at least one folding layer adjacent to an input layer of the artificial neural network. A folding layer, also known as convolutional layer, is a layer in which the activity of each neuron is calculated via a discrete convolution corresponding to a convolution matrix or a filter kernel. The convolution of the input data (object data records) makes it possible to evaluate correlations between objects in the traffic environment in an especially efficient manner. The artificial neural network is thus preferably a convolutional artificial neural network. In that context, the convolutional layer corresponds to a number M of one-dimensional filters, which are able to highlight and combine individual object properties. For instance, such an object property may be the property of an object, that it is located to the left of the ego traffic lane and is increasing an acceleration (of the acceleration) in the direction of the ego lane. The filters, that is, the structure of the convolutional layer, are generated in automated fashion during training of the neural network with suitable training data records.

The evaluation of the novelty of a traffic situation characterized by aggregated object data records in relation to training data of the artificial neural network preferably includes an evaluation of a distance of a data tuple including the object data records, from a multidimensional probability distribution of such data tuples calculated from the training data.

For example, in the modeling of the multidimensional probability distribution, for all training data per training data record which corresponds to a traffic situation, one input data tuple equivalent to one or more aggregated object data records and corresponding to the traffic situation may be used as an input value (input data duple (tuple)) for the probability distribution to be calculated from the input values. The probability distribution therefore constitutes a representation of the training data.

In that context, a probability distribution may be determined for each input data tuple (tuple) corresponding to each traffic situation of the training data, the individual probability distributions being summed up for a probability distribution to be used for an evaluation of the novelty. The modeling of the training data with the aid of the multidimensional probability distribution has the advantage that the novelty of a traffic scene may be evaluated extensively, especially, e.g., in terms of all attributes of an object, how they correspond to an object data record, and/or in terms of the constellation of multiple objects in the traffic situation.

In one exemplary embodiment, the evaluation of a novelty of a traffic situation characterized by the aggregated object data records in relation to training data of the artificial neural network includes evaluation of a novelty of the traffic situation characterized by the aggregated object data records, in conjunction with additional data characterizing an environment scenario. For example, the additional data characterizing an environment scenario may include information about the weather (e.g., sun/rain/snow/fog), information about the presence of a tunnel (e.g., yes/no), and/or information about the presence of a road construction site (yes/no). Such data may be gathered from road traffic network information (e.g., tunnel), for instance, or be obtained through information services (construction sites) or additional sensors (weather). The consideration of additional data characterizing the environment scenario makes it possible in situations when, on the basis of the data characterizing the environment scenario, a relevant deviation results with respect to the training data, to switch over to a rule-based object selection branch, even if the object data records to be used by the neural network to select the main object match well with the training data. Consequently, the potential otherness of the traffic situation characterized by the additional data may be taken into account.

In addition, the subject matter of the present invention is a driver assistance system or driving system for a motor vehicle, having at least one sensor for sensing the traffic environment of the motor vehicle, and having an object selection unit for selecting a main object for an assistance function or automated driving function of the driver assistance system, one of the methods described above being implemented in the system.

In the following, an exemplary embodiment is explained in greater detail based on the figures.

