Patent Description:
<CIT> relates to an electronic apparatus using ground truth information about road wetness, input from one or more sources, such as on-board sensor signals and/or signals from other on-board modules and offboard signals to develop the deep learning model for road wetness classification. The vehicle can be operated in an autonomous driving mode, wherein the wrote condition deep learning model relates to a discrete classification or continuous regression/estimation of road wetness.

In vehicles a plurality of components can be adjusted manually or automatically, such as a seat, a ventilation system or an air-conditioning system. Some vehicles offer a memory function, to store settings for one or more components for a particular user (driver), such as a user profile containing preferred settings for a seat, steering wheel and mirrors. If the user is identified and/or authenticated, the component(s) can be adjusted according to the user profile.

However, the component settings are static, i.e., only user-related, such as a particular inclination of a backrest of a seat, longitudinal position of the seat within the vehicle, ventilation speed and/or preferred audio source. There is no context-related way of personalising the vehicle automatically, besides the user identification.

Therefore, it is an object of the present invention to provide an apparatus for improved vehicle component setting.

This object is solved by an electronic apparatus comprising the features of claim <NUM>, and a vehicle comprising the features of claim <NUM>.

According to a first aspect to better understand the present disclosure, an electronic apparatus for vehicle component setting comprises a sensor signal interface configured to connect to a plurality of vehicle sensors, a regressing module configured to receive a sensor signal output by each of the plurality of vehicle sensors and to predict a continuous component setting value based on the received sensor signals, a rounding operator configured to round the continuous component setting value predicted by the regressing module to a discrete component setting value and to output the discrete component setting value, and a component interface configured to connect to at least one vehicle component and further configured to transmit a setting signal corresponding to the discrete component setting value to the at least one vehicle component. Such apparatus is capable of outputting a discrete component setting value for at least one vehicle component based on signals from a plurality of vehicle sensors. While such sensors can form a context, i.e., a particular situation in which the vehicle and/or the driver is, the regression module is able to predict a component setting on the basis of this context. A continuous component setting value means any stepless value, such as a value having one or more decimals, whereas a discrete component setting value means a value having one of a definite amount of possible values, such as an integer number.

The electronic apparatus further comprises a controller configured to train the regressing module based on training data and the corresponding discrete component setting value output by the rounding operator. In other words, the regressing module is trained end-to-end, i.e., from the context-forming sensor signals to the discrete component setting value.

As an example, a regressing model (or simply referred-to as a regressor) usually models a target level continuously, for example, outputs a target level of <NUM>. However, the majority of vehicle components require discrete setting values, such as a wiper having the levels "off", "slow" or "interval", "medium" and "fast" or a ventilation system having the levels "off" and a distinct speed level between <NUM> and <NUM>, for example. Thus, the output target level of <NUM> would not match any of such discrete component setting levels.

Training the regressing model based on the output of the rounding operator, according to the present disclosure, significantly improves context-based vehicle component setting, since the trained regressing module outputs a continuous component setting value that will reliably and replicable rounded to the correct discrete component setting value by the rounding operator.

Although a classification module could be employed instead of the regressing model, since a classification module outputs discrete target levels, such classification does not consider ordering of levels. In other words, a classification usually assigns equal weights to each level, so that a misclassification (incorrect prediction) may have a significant influence on user satisfaction. As an example only, if a wiper is set to "fast" from an "off" state, this may be irritating, particularly if the user would expect a slow or interval wiper level. Another way of predicting a target level could be a probability calibration, such as a softmax activation. However, since such approach learns probability distributions rather than output levels, a high error can be caused as with the classification module. Thus, in both cases user satisfaction may not be met, since there is a high probability of false prediction.

Therefore, the present disclosure combines a regressor and a rounding operator, wherein the regressor is trained on the basis of the output of the rounding operator. Such regressor can handle an input of high dimensions, for example, the input from a plurality of sensors and/or other signal representing a context and can be trained efficiently.

A differentiable rounding operator can be employed, that reliably and replicable rounds the continuous value input from the regressor to a discrete target value.

Such combination of regressor and differentiable rounding operator allows training the regressor even with training data, that does not cover all possible target levels. For example, the training data may comprise only a subset of target levels for a wiper, such as "off", "slow" and "fast" (or in a numerical scale [<NUM>, <NUM>, <NUM>]), which can be effectively learned/trained by the regressor. The regressor alone could output a target level "medium" ([<NUM>]), although it was never trained, since the regressor (while being trained) interpolates and thus arrives at any possible level.

In an implementation variant the regressing module can comprise an artificial neural network (simply referred-to as a neural network) or a support vector machine (a supervised learning model). Both trainable models are differentiable by design, i.e. can be trained with respect to discrete target levels/values. Any regression loss can be used. For example, different costs for different error types can be implemented in an efficient way, such as a target = <NUM> and an actual value =<NUM> causes an error of <NUM> while a target = <NUM> and an actual value = <NUM> causes an error of <NUM>, which allows efficient training of the underlying model.

In another implementation variant the rounding operator can be configured to employ a sigmoid function, a sum of sigmoid functions, a rectified linear unit activation function, a Fourier transformation, a kernel density estimation, or a mapping function.

For instance, a sigmoid function can round a scalar in an interval of [-<NUM>, <NUM>], which once applied to the continuous component setting value predicted by the regressor allows outputting a discrete component setting value.

In another example, a sum of sigmoid functions may further be based on modified versions of sigmoid functions, such as <MAT> wherein T denotes the steepness, xi denotes an offset for level i, and alpha (α) denotes a scaling factor. Such sum of sigmoid functions allows excluding terms in the sum to exclude a level that is not covered by the training data.

For instance, <FIG> illustrates sigmoid rounding functions having a different steepness as well as an optimal rounding operator "round(x)". As is derivable from <FIG>, a target level of "f(x) = <NUM>" was not seen during training and can easily be excluded by the electronic apparatus of the present disclosure.

In yet further examples, variants of a rectified linear unit (ReLU) activation function (Exponential Linear Unit (ELU), LeakyReLU, Gaussian Error Linear Unit (GELU)) can be employed as the rounding operator. This avoids vanishing gradients. A Fourier transformation can describe rounding with multiple levels by step functions to obtain differentiable approximations, a kernel density estimation can describe rounding by a sum of kernel functions, such as a Gaussian kernel, which also leads to differentiable approximation. Furthermore, mapping functions, such as a floor function that maps a continuous regressor output to smaller discrete levels or a ceiling function that maps a continuous regressor output to a larger discrete level, may likewise be employed. Such mapping functions may be achieved by shifting a sigmoid function towards mappings to a lower level and larger level, respectively. As an example only, the setting of a heating of a seat or steering wheel could be rounded to a smaller value, in order to avoid uncomfortable settings to a level sensed as to warm.

The aim of the present disclosure is a differentiable (discrete) component setting value, and, where possible, additionally the avoidance of vanishing gradients, i.e. zero gradients.

For instance, ReLU defines a function f(x) = max (<NUM>, x), wherein max (a, b) outputs the maximum of a and b. Such function is sub-differentiable, but avoids a vanishing gradient for x ><NUM>. A Fourier transformation of an exact rounding function (as in <FIG>) can be calculated. By deleting/cutting certain "frequencies" a differentiable function can be achieved, comparable to one of the sigmoid functions shown in <FIG>. In case of the kernel density estimation, a rounding takes place by superimposing a plurality of differentiable kernel functions (form a sum). The sum itself is also differentiable.

According to a second aspect to better understand the present disclosure, a vehicle comprises an electronic apparatus according to the first aspect or one of its implementation variants.

In an implementation variant of the vehicle, the plurality of vehicle sensors can comprise at least one of an interior thermometer, an exterior thermometer, a light sensor, a humidity sensor, a rain sensor, a vehicle speed sensor, a vibration sensor, a gyroscope, a positioning sensor, a user identification sensor, and a clock. This list of sensors is not limiting, but any sensor available and implementable in a vehicle can be used and the respective sensor signal can be employed for predicting the setting of at least one vehicle component. As an example only, a certain exterior temperature and a certain interior temperature (with respect to a passenger compartment of the vehicle) may be compared, in order to adjust the level of ventilation and/or air-conditioning of the interior of the vehicle. A certain time of the day may influence the setting of the seat position adjustment or a massage function of a driver's seat, etc.. It is to be understood that any combination of sensor signals can be provided to the regressor, which leads to a more or less complete context-related setting of one or more vehicle components.

In another implementation variant, the vehicle can further comprise a driver assistance system configured to output at least one signal to the sensor signal interface. Thus, if the vehicle already comprises a driver assistance system, information already at hand can be used in form of signals with the electronic apparatus for predicting the vehicle component setting. As an example only, if the driver assistance system comprises a camera capturing the driver, a reaction in the driver's face (e.g., smaller pupils, narrowing eyebrows or the like) may tell that the driver is dazzled by a light. If a light sensor outputs a signal of an intense sunlight or of lights of a succeeding vehicle, the regressing module can be trained to output a component setting value for a sun shade or a rear mirror, respectively. It is to be understood that any combination of sensors signals and/or signals from an already present vehicle system can be used to train the regressing module.

In yet another implementation variant, the at least one vehicle component comprises at least one of a ventilation system, a heating system, an air-conditioning system, a wiper control, a seat adjustment system, a driving mode setting system, a driver assistance system, a light system, a mirror setting system, an entertainment system, a window lifter, and a sunroof lifter. This list is to be understood as not limiting the present disclosure. The regressing module can be trained to adjust settings for any of those vehicle components and/or any combination of those vehicle components. As an example only, if the air-conditioning system is switched on or adjusted to a higher cooling rate, a window and/or sunroof may be closed.

In a further implementation variant, the controller of the vehicle's apparatus may be a dedicated processor or may form part of a vehicle computing system, such as an engine control unit (ECU). The processor of the vehicle's apparatus may output a signal and/or data representing the discrete component setting value.

The present disclosure is not restricted to the aspects and variants in the described form and order. Specifically, the description of aspects and variants is not to be understood as a specific limiting grouping of features. It is to be understood that the present disclosure also covers combinations of these aspects and variants. Thus, each variant or optional feature can be combined with any other aspect, variant, optional feature or even combinations thereof.

Preferred embodiments of the invention are now explained in greater detail with reference to the enclosed schematic drawings, in which.

<FIG> schematically illustrates an electronic apparatus <NUM> for vehicle component setting. The apparatus <NUM> may be connected or linked to a plurality of sensors <NUM> on the one hand, and one or more vehicle components <NUM> on the other hand. A sensor signal interface <NUM> is provided that can be employed to connect the electronic apparatus <NUM> to the plurality of vehicle sensors <NUM>. This connection may be any electric line or data bus capable of transmitting sensor signals output by each of the plurality of vehicle sensors <NUM> to the sensor signal interface <NUM>.

Likewise, a component interface <NUM> can be employed to connect the electronic apparatus <NUM> to at least one vehicle component <NUM>. The component interface <NUM> allows transmitting a setting signal to each of the at least one vehicle component <NUM>. This connection may also be any electric line or data bus capable of transmitting a setting signal output by the electronic apparatus to any of the connected vehicle component(s) <NUM>.

A prediction module <NUM> in the electronic apparatus <NUM> is capable of processing the sensor signal(s) received at the sensor signal interface <NUM> and generate a component setting value that matches with a discrete setting of any one of the vehicle components <NUM>. For instance, the prediction module <NUM> can comprise a regressing module <NUM> configured to receive sensor signal output by each of the plurality of vehicle sensors <NUM> via the sensor signal interface <NUM> and to predict a continuous component setting value based on the received sensor signal(s). Such regressing module <NUM> is capable of handling a plurality of input signals, in order to predict on the basis of these signals one or more output values.

It is to be understood that more than one regressing module <NUM> can be employed, wherein each regressing module <NUM> is specialised for one vehicle component <NUM>. Thus, each regressing module <NUM> can output a vehicle component setting value for a particular vehicle component <NUM> based on any number of sensor signals received from the sensor signal interface <NUM>.

The prediction module <NUM> can further comprise a rounding operator <NUM> configured to round the continuous component setting value predicted by the regressing module <NUM>. The rounding operator <NUM> rounds the continuous value to a discrete component setting value, which corresponds to a discrete setting of one of the vehicle components <NUM>.

It is to be understood that the rounding operator <NUM> can round a plurality of continuous component setting values distinctly and simultaneously, one for each vehicle component <NUM>. In case that a plurality of regressing modules <NUM> are employed, the rounding operator <NUM> can be configured to round each of the values output by the plurality of regressing modules <NUM> simultaneously but separately, so that a plurality of discrete component setting values are output by the rounding operator <NUM>. Likewise, the prediction module <NUM> can comprise a plurality of rounding operators <NUM>, one for each regressing module <NUM>.

The electronic apparatus <NUM> further comprises a controller <NUM> configured to train the regressing module <NUM> based on training data and the corresponding discrete component setting value output by the rounding operator <NUM>. In more detail, the training data can comprise one or more sensor signals and one or more target component setting values. The sensor signals of the training data is fed to the regressing module <NUM>, which outputs a continuous component setting value. After rounding the continuous component setting value predicted by the regressing module <NUM>, the controller <NUM> trains the regressing module <NUM> based on the target component setting value(s).

It is to be understood that the training of the regressing module <NUM> can take place before the electronic apparatus <NUM> is implemented in a vehicle <NUM> (see <FIG>). Alternatively or additionally, the controller <NUM> can be configured to train the regressing module <NUM> continuously, i.e. during the actual use of the electronic apparatus <NUM>. As an example only, if a certain context, i.e., sensor signals, such as increasing exterior temperature, leads to the output of a discrete component setting value for the ventilation system (e.g., speeding up the ventilation), and shortly thereafter the user decreases the speed of the ventilation, the controller <NUM> can train the regressing module <NUM>, so that a discrete component setting value is output that corresponds to the lower ventilation speed set by the user.

The controller <NUM> trains the regressing module <NUM>, in any case, based on the output of the rounding operator <NUM>. This allows an improved vehicle component setting, since the continuous component setting values predicted by the regressing module <NUM> can be trained to lead to the correct discrete target component setting value.

<FIG> schematically illustrates a vehicle <NUM> comprising an electronic apparatus <NUM>, such as the apparatus <NUM> of <FIG>. The electronic apparatus <NUM> can be implemented as a separate entity within the vehicle <NUM>. Alternatively, the electronic apparatus <NUM> can be integrated into an already present electronic device, such as an ECU, an on-board computing device or the like.

The vehicle may be equipped with a plurality of sensors <NUM>. Such sensors can include a light and/or rain sensor 210a, which may be part of the vehicle <NUM> installed behind the windshield. Further sensors are schematically illustrated within the vehicle <NUM>, which can comprise an exterior thermometer 210b measuring a temperature of the air outside of and surrounding the vehicle <NUM>, an interior thermometer 210c measuring a temperature inside of a passenger compartment of the vehicle <NUM>, and a positioning sensor 210d measuring a position of the vehicle <NUM>, for example, in a global coordinate system. It is to be understood that the vehicle <NUM> can comprise further sensors, such as a humidity sensor (exterior as well as interior), a vehicle speed sensor, a vibration sensor, a gyroscope, a user identification sensor or a clock.

All of these sensors and particularly their respective sensor signals describe a context for the vehicle at a particular time. This context, particularly any change of sensor signal, can lead to a different output of the regressing module <NUM> and, hence, to a different output of the rounding operator <NUM>, so that one or more vehicle components <NUM> are set differently.

Such vehicle components <NUM> can comprise a wiper control 280a controlling the speed of a windshield wiper or another wiper, a light system 280b such as headlights of the vehicle, a seat adjustment system 280c (including a massage function or softness setting of the seat), a ventilation system 280d ventilating air into and/or within the passenger compartment of the vehicle <NUM>, and a sunroof lifter 280e capable of opening and closing a sunroof.

It is to be understood that further vehicle components <NUM> can be employed, which are not explicitly illustrated in <FIG>. For instance, a heating system, an air-conditioning system, driving mode setting system, a mirror setting system, and entertainment system or a window lifter can be part of the vehicle <NUM> and be controlled by the electronic apparatus <NUM>, to name a few.

Moreover, a driver assistance system <NUM> can be implemented in the vehicle <NUM>. This driver assistance system <NUM> can be contemplated as a vehicle component, too. As an example only, if the prediction module <NUM> outputs a setting value for the wiper control that increases the speed of the wiper, the driver assistance system <NUM> may be provided with a setting value corresponding to increased rain, so that the driver assistance system <NUM> may reduce the amount of time until remembering the driver to make a break or the like.

Likewise, the driver assistance system <NUM> can be contemplated, alternatively or additionally, as a (vehicle) sensor <NUM>. For instance, the driver assistance system <NUM> may derive information about the driver, the vehicle or a context of the vehicle, which can be employed by the electronic apparatus <NUM>, particularly by the prediction module <NUM>. As an example only, a state of the driver and/or a state of a driving mode (such as autonomous or semi-autonomous driving) and/or a state of the road the vehicle is travelling on can influence the adjustment of certain vehicle components, such as a seat adjustment system 280c, an engine control system, a gear-box mode control, an entertainment system or the like.

Claim 1:
An electronic apparatus (<NUM>) for vehicle component setting, the device comprising:
a sensor signal interface (<NUM>) configured to connect to a plurality of vehicle sensors (<NUM>);
a regressing module (<NUM>) configured to receive a sensor signal output by each of the plurality of vehicle sensors (<NUM>) and to predict a continuous component setting value based on the received sensor signals;
a rounding operator (<NUM>) configured to round the continuous component setting value predicted by the regressing module (<NUM>) to a discrete component setting value and to output the discrete component setting value;
a component interface (<NUM>) configured to connect to at least one vehicle component (<NUM>) and further configured to transmit a setting signal corresponding to the discrete component setting value to the at least one vehicle component (<NUM>); and
a controller (<NUM>) configured to train the regressing module (<NUM>) based on training data and the corresponding discrete component setting value output by the rounding operator (<NUM>).