METHOD FOR TRAINING A DEEP-LEARNING-BASED MACHINE LEARNING ALGORITHM

Abstract

A method for training a deep-learning-based machine learning algorithm. The method includes: providing training data for training the deep-learning-based machine learning algorithm, wherein the training data comprise sensor data; training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data; and subsequently optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function.

Claims

1. A method for training a deep-learning-based machine learning algorithm, the method comprising the following steps: providing training data for training the deep-learning-based machine learning algorithm, the training data including sensor data; training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data; and subsequently, after the training, optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function.

2. The method according to claim 1, wherein the training of the deep-learning-based machine learning algorithm by the machine learning method includes training the deep-learning-based machine learning algorithm based on a differentiable cost function.

3. The method according to claim 1, wherein the optimizing of the at least one parameter of the trained deep-learning-based machine learning algorithm based on the non-differentiable cost function includes optimizing the trained deep-learning-based machine learning algorithm based on temperature scaling.

4. A method for controlling a controllable system, the method comprising the following steps: providing a deep-learning-based machine learning algorithm for controlling a controllable system, wherein the deep-learning-based machine learning algorithm has been trained by: providing training data for training the deep-learning-based machine learning algorithm, the training data including sensor data, training, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data, and subsequently, after the training, optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function; and controlling the controllable system based on the deep-learning-based machine learning algorithm.

5. The method according to claim 4, wherein the controllable system is an automatic distance control of an autonomously driving motor vehicle.

6. A control device for training a deep-learning-based machine learning algorithm, the control device comprising: a provisioning unit configured to provide training data for training the deep-learning-based machine learning algorithm, wherein the training data includes sensor data; a training unit configured to train, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data; and an optimization unit configured to subsequently, after the training, optimize at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function.

7. The control device according to claim 6, wherein the training unit is configured to train the deep-learning-based machine learning algorithm based on a differentiable cost function.

8. The control device according to claim 6, wherein the optimization unit is configured to optimize the trained deep-learning-based machine learning algorithm based on temperature scaling.

9. A control device for controlling a controllable system, the control device comprises: a provisioning unit configure to provide a deep-learning-based machine learning algorithm for controlling the controllable system, wherein the deep-learning-based machine learning algorithm has been trained by a control device for training a deep-learning-based machine learning algorithm including: a second provisioning unit configured to provide training data for training the deep-learning-based machine learning algorithm, wherein the training data includes sensor data, a training unit configured to train, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data, and an optimization unit configured to subsequently, after the training, optimize at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function, wherein the deep-learning-based machine learning algorithm is adapted to a particular use case; and a control unit configured to control the controllable system based on the deep-learning-based machine learning algorithm.

10. The control device according to claim 9, wherein the controllable system is an automatic distance control of an autonomously driving motor vehicle.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0044] FIG. 1 shows a flow chart of a method for training a deep-learning-based machine learning algorithm according to embodiments of the present invention.

[0045] FIG. 2 shows a schematic block diagram of a control device for training a deep-learning-based machine learning algorithm according to embodiments of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0046] In the figures, identical reference signs denote identical or functionally identical elements, parts or components, unless stated otherwise.

[0047] FIG. 1 shows a flow chart of a method for training a deep-learning-based machine learning algorithm 1 according to embodiments of the present invention.

[0048] Machine learning algorithms are based on statistical methods being used to train a data processing system in such a way that it can perform a particular task without it being originally programmed explicitly for this purpose. The goal of machine learning is to construct algorithms that can learn and make predictions from data. These algorithms create mathematical models with which data can be classified, for example.

[0049] Such machine learning algorithms are used, for example, in the area of driver assistance systems or in the control of autonomously driving motor vehicles. For example, when controlling autonomously driving motor vehicles, it is important to predict as precisely as possible which driving maneuvers further vehicles in the vicinity of the autonomously driving motor vehicle will perform before long, in order to be able to respond as adequately as possible.

[0050] Such predictions of future driving maneuvers of further vehicles in the vicinity of autonomously driving motor vehicles are usually based on hidden Markov models. A hidden Markov model is a stochastic model in which a system is modeled by a Markov chain with unobserved states. However, it proves disadvantageous in this respect that performance indicators or requirements with respect to a component or an application that further processes the predictions made by the hidden Markov model are frequently only available in practice in the form of non-continuous or non-differentiable functions. Hidden Markov models are also comparatively simple models, which do not cover all practical situations or dependencies on a regular basis.

[0051] FIG. 1 shows a method for training a deep-learning-based machine learning algorithm 1 adapted to a particular use case, wherein the method 1 comprises a step 2 of providing training data for training the algorithm of the deep-learning-based machine learning algorithm, wherein the training data comprises sensor data; a step 3 of training, by a machine learning method, the deep-learning-based machine learning algorithm based on training data; and a step 4 of subsequently optimizing at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function, in particular a non-differentiable cost function with respect to a particular application.

[0052] The use of deep-learning-based machine learning algorithms, i.e., of neural networks with several intermediate layers, has the advantage that they can cover significantly more complex situations or scenarios than, for example, hidden Markov models.

[0053] That at least one parameter of the deep-learning-based machine learning algorithm is also optimized based on a non-differentiable cost function with respect to the corresponding application also has the advantage that the deep-learning-based machine learning algorithm can also be adapted to performance indicators or requirements with respect to a component or an application, which further processes predictions that are made by the deep-learning-based machine learning algorithm and in practice are frequently available only in the form of non-differentiable functions.

[0054] Overall, an improved method 1 for predicting future states is thus specified, wherein the corresponding, improved prediction of future states proves to be advantageous, in particular in safety-critical systems, for example when controlling autonomously driving motor vehicles.

[0055] In particular, FIG. 1 thus shows a hybrid method for training a deep-learning-based machine learning algorithm 1.

[0056] According to the embodiments of FIG. 1, the deep-learning-based machine learning algorithm is in particular a deep-learning-based classifier.

[0057] According to the embodiments of FIG. 1, step 2 of training the deep-learning-based machine learning algorithm by a machine learning method furthermore comprises training the deep-learning-based machine learning algorithm based on a differentiable cost function, for example based on a gradient descent method.

[0058] According to the embodiments of FIG. 1, step 3 of optimizing the at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function furthermore comprises optimizing the trained deep-learning-based machine learning algorithm based on temperature scaling.

[0059] The optimization based on the temperature scaling can in particular take place based on a Bayesian optimization method. Bayesian optimization methods work best if the corresponding parameter space is small, wherein only one parameter, i.e., the temperature, is considered here. On the other hand, with a high number of parameters or a large parameter space, as occurs, for example, in neural networks with many layers, a Bayesian optimization can usually only be realized with difficulty. Furthermore, however, for example, a grid search method may also be used to optimize the deep-learning-based machine learning algorithm.

[0060] The training data furthermore comprise sensor data, wherein the corresponding sensor may in particular be an optical sensor, for example a RADAR sensor, a camera or a LiDAR sensor.

[0061] The deep-learning-based machine learning algorithm can subsequently be used in particular for controlling a controllable system, for example an automatic distance control of an autonomously driving motor vehicle.

[0062] For example, the deep-learning-based machine learning algorithm may be part of a system for predicting future driving maneuvers of motor vehicles in the vicinity of the autonomously driving motor vehicle, which is trained, for example, to predict, based on captured values with respect to a current speed of a motor vehicle, the current position of the motor vehicle, a relative distance between the motor vehicle and the autonomously driving motor vehicle and, where applicable, a current state of signal lights of the motor vehicle, a future driving maneuver of the motor vehicle, for example whether or not it is likely to change lanes before long. In so doing, the deep-learning-based machine learning algorithm may have been trained on, for example, historical data, or data collected during past trips, or information about relationships between the speed of a motor vehicle and/or the position of the motor vehicle and/or the relative distance of the motor vehicle to an autonomously driving motor vehicle and a subsequent driving maneuver of the motor vehicle.

[0063] The predictions made by the deep-learning-based machine learning algorithm may subsequently be used, for example, to determine that vehicle in the vicinity of an autonomously driving motor vehicle based on which an automatic distance control or an adaptive cruise control of the autonomously driving motor vehicle is to be controlled.

[0064] FIG. 2 shows a schematic block diagram of a control device for training a deep-learning-based machine learning algorithm 10 according to embodiments of the present invention.

[0065] As FIG. 2 shows, the control device 10 comprises a provisioning unit 11 designed to provide training data for training the deep-learning-based machine learning algorithm, wherein the training data comprise sensor data; a training unit 12 designed to train, by a machine learning method, the deep-learning-based machine learning algorithm based on the training data; and an optimization unit 13 designed to subsequently optimize at least one parameter of the trained deep-learning-based machine learning algorithm based on a non-differentiable cost function, in particular a non-differentiable cost function with respect to a particular application.

[0066] The provisioning unit may in particular be a receiver designed to receive corresponding data, in particular sensor data. The training unit and the optimization unit may, for example, respectively be realized based on code that is stored in a memory and can be executed by a processor.

[0067] According to the embodiments of FIG. 2, the training unit 12 is designed to train the deep-learning-based machine learning algorithm based on a differentiable cost function.

[0068] According to the embodiments of FIG. 2, the optimization unit 13 is also designed to optimize the trained deep-learning-based machine learning algorithm based on temperature scaling.