Methods and devices for using a relationship between activities of different traffic signals in a network to improve traffic signal state estimation are disclosed. An example method includes determining that a vehicle is approaching an upcoming traffic signal. The method may further include determining a state of one or more traffic signals other than the upcoming traffic signal. Additionally, the method may also include determining an estimate of a state of the upcoming traffic signal based on a relationship between the state of the one or more traffic signals other than the upcoming traffic signal and the state of the upcoming traffic signal.

An evaluating apparatus is provided with: a first acquirer configured to obtain a feature value indicating driving behavior of a driver; a classifier configured to classify a plurality of feature values obtained from a plurality of drivers, into a plurality of groups; a second acquirer configured to obtain the feature value that is representative in each of the plurality of groups, as a representative feature value; a ranking device configured to give a rank corresponding to a driving carefulness degree, to each of the plurality of groups, on the basis of the representative feature value; and a determinator configured to determine a driver type corresponding to the driving carefulness degree of the driver, on the basis of a rank of a group into which the feature value of the driver is classified.

An evaluating apparatus is provided with: a first acquirer configured to obtain a feature value indicating driving behavior of a driver; a classifier configured to classify a plurality of feature values obtained from a plurality of drivers, into a plurality of groups; a second acquirer configured to obtain the feature value that is representative in each of the plurality of groups, as a representative feature value; a ranking device configured to give a rank corresponding to a driving carefulness degree, to each of the plurality of groups, on the basis of the representative feature value; and a determinator configured to determine a driver type corresponding to the driving carefulness degree of the driver, on the basis of a rank of a group into which the feature value of the driver is classified.

A method for predicting a failure of a power control unit of a vehicle is provided. The method includes obtaining data from a plurality of sensors of the power control unit of a vehicle subject to simulated multi-load conditions, implementing a machine learning algorithm on the data to obtain machine learning data, obtaining new data from the plurality of sensors of power control unit of the vehicle subject to real multi-load conditions, implementing the machine learning algorithm on the new data to obtain test data, predicting a failure of the power control unit based on a comparison between the test data and the machine learning data.

A method for evaluating an angular position of an object recognized on the basis of radar data, the radar data being ascertained by a radar device. The method includes: ascertaining of an intrinsic speed of the radar device; ascertaining a relative speed of the recognized object in relation to the radar device, using the ascertained radar data; ascertaining at least one angular test region using the ascertained intrinsic speed and the ascertained relative speed, the at least one angular test region corresponding to possible stationary objects that have a relative speed that substantially corresponds to the ascertained relative speed; and ascertaining whether an azimuth angle of the recognized object lies in the ascertained angular test region.

A method for evaluating an angular position of an object recognized on the basis of radar data, the radar data being ascertained by a radar device. The method includes: ascertaining of an intrinsic speed of the radar device; ascertaining a relative speed of the recognized object in relation to the radar device, using the ascertained radar data; ascertaining at least one angular test region using the ascertained intrinsic speed and the ascertained relative speed, the at least one angular test region corresponding to possible stationary objects that have a relative speed that substantially corresponds to the ascertained relative speed; and ascertaining whether an azimuth angle of the recognized object lies in the ascertained angular test region.

The present invention relates to a method for providing a route stipulation for a route system of a vehicle, comprising the following steps: providing a plurality of detected trajectories of further vehicles in a route section to be used, ascertaining a trajectory stipulation from the detected trajectories, ascertaining a deviation zone from the detected trajectories, wherein the deviation zone is determined on the basis of a deviation of at least individual detected trajectories from the trajectory stipulation, determining the route stipulation at least on the basis of the trajectory stipulation and the deviation zone.

Systems, methods and computer-readable media for path planning for an autonomous vehicle, comprising receiving data defining an upper bound of a spatio-temporal convex corridor and lower bound of the spatio-temporal convex corridor; computing a plurality of control point times within a corridor start time and a corridor end time, the control point times including at least a start control point time, an end control point time and an intermediate control point time between the start control point time and the end control point time; determining, for each of the control point times, respective control point maximum values and control point minimum values; computing, based on the respective control point maximum values and the respective control point minimum values, respective control point values for each of the control point times, the control point values defining a curve segment that is within the upper bound and the lower bound of the spatio-temporal convex corridor.

Provided is a processor-implemented method and a processor in a vehicle for estimating the value of a quantity for which a physical sensor is not available for measurement. The method includes: receiving a plurality of measured signals representing values of measurable variables; computing, in real-time, time derivatives of the measured signals; and applying a trained feedforward neural network, in real-time, to estimate values for a plurality of unmeasurable variables, the unmeasurable variables being variables that are unmeasurable in real-time, the feedforward neural network having been trained using test data containing time derivatives of values for the measurable variables and values for the unmeasurable variables; wherein the vehicle uses the estimated values for the unmeasurable variables for vehicle operation.

The invention relates to a device (20) for assisting a driver of a vehicle to perform physical exercises, comprising:—means (21) for acquiring data representative of a driving context,—means (22) for determining a level of risk, from the acquired data,—means (23) for selecting at least one physical exercise compatible with the level of risk,—means (24) for transmitting a signal indicating to the driver the at least one selected exercise; characterised in that it further comprises—means (21′) for acquiring data representative of a history of exercises performed by said driver, said data coming from a remote storage,—and in that the means (23) for selecting at least one physical exercise also use said data representative of a history in order to select said at least one exercise.