Abstract: A computer-implemented method is provided. The method calculates an importance score for each agent of the at least one other agent based on the acquired sensor data and the acquired position and motion data, generates a list comprising the at least one other agent based on the calculated importance scores of the at least one other agent, partitions the generated list in at least one partition based on the importance score of the at least one other agent, and generates a prediction result by predicting a behavior of the at least one other agent included in the at least one partition based on the sensor data and the position and motion data using a selected behavior prediction model. The selected behavior prediction model is selected from a plurality of behavior prediction models based on the importance score of the at least one agent included in the at least one partition.

Abstract: The present invention provides an advanced driver assistance system for assisting a driver of a vehicle. The system comprises a sensor unit, a processing unit and a display unit. The sensor unit is configured to sense an environment of the vehicle. The processing unit is configured to determine, based on a sensing output, at least one feature of the environment. The processing unit is configured to determine, for a current time, a risk zone of the feature, by estimating, based on at least one parameter of the vehicle at the current time, at each virtual position of two or more virtual positions of the vehicle a respective risk with regard to the feature; and by forming the risk zone based on the two or more risks. The display unit is configured to display the environment of the vehicle with the feature and the risk zone of the feature.

Abstract: A method for improving prediction results of advanced driver assistance systems of a vehicle includes obtaining map data including information about a road geometry in a proximity of the vehicle. A sensor means is assigned, sensing a surrounding of the vehicle. A virtual sensing means output is generated, corresponding to an output of the sensing means if the sensing means. The generation is based on a mathematical model of the sensing means. The surrounding of the vehicle is sensed, and a sensing means output is generated. The sensing means output is compared to the virtual sensing means output. Parameters of the mathematical model are modified, and the virtual sensing means output are generated and compared to the sensing means output. Map data is combined with information derived from the sensing means output to generate combined information, which is output for further processing.

Abstract: The invention relates to a method for analyzing localization errors, wherein the method includes the steps of obtaining relative positions and absolute positions of an object moving within a mapped environment, which are generated sequentially over time, calculating differences between the relative positions and absolute positions to determine sequential localization errors, and decomposing the localization errors into a systematic error that is dependent on the orientation of the object and into a systematic error that is independent on the orientation of the object by analyzing the intrinsic dynamics of the localization errors.

Abstract: The present invention relates to a method for use in a driver assistance system of an ego-vehicle. The method supports driving of the ego-vehicle and comprises the steps of retrieving a priority relationship between the ego-vehicle and at least one traffic participant involved in a traffic situation; selecting a prediction model for the at least one traffic participant depending on the priority relationship; predicting at last one hypothetical future trajectory for the ego-vehicle and, based on the selected prediction model, at last one hypothetical future trajectory for the at least one traffic participant; and calculating a behavior relevant score for ego-vehicle based on the calculated hypothetical future trajectories.

Abstract: The invention relates to a method for analyzing localization errors, wherein the method includes the steps of obtaining relative positions and absolute positions of an object moving within a mapped environment, which are generated sequentially over time, calculating differences between the relative positions and absolute positions to determine sequential localization errors, and decomposing the localization errors into a systematic error that is dependent on the orientation of the object and into a systematic error that is independent on the orientation of the object by analyzing the intrinsic dynamics of the localization errors.

Abstract: The method for assisting a person in operating in a dynamic environment may form part of a mobility assistance system. The method comprises a step of acquiring sensor data comprising a time sequence of at least two consecutive images of the dynamic environment from at least one sensor, for example a camera. Optical flows are calculated based on the at least two consecutive images. Feature scores associated to spatial positions for selected regions in an image space are determined in order to generate a feature score field. An output signal including directional stimulus information is generated based on the generated feature score field, wherein the directional stimulus information comprises information on relative spatial relations between the selected regions. The generated output signal is provided to at least one actuator, which signals the directional stimulus information to the person.

Abstract: The present invention relates to a method for use in a driver assistance system of an ego-vehicle. The method supports driving of the ego-vehicle and comprises the steps of retrieving a priority relationship between the ego-vehicle and at least one traffic participant involved in a traffic situation; selecting a prediction model for the at least one traffic participant depending on the priority relationship; predicting at last one hypothetical future trajectory for the ego-vehicle and, based on the selected prediction model, at last one hypothetical future trajectory for the at least one traffic participant; and calculating a behavior relevant score for ego-vehicle based on the calculated hypothetical future trajectories.

Abstract: An automotive driver assistance for a vehicle with an improved capability to handle obstructed sensor coverage includes steps of acquiring sensor data on an environment of the vehicle from at least one sensor, of generating an environment representation based on the acquired sensor data and of predicting at least one behavior of the vehicle. The method determines at least one area in the environment of the vehicle wherein for the at least one area either a confidence for the sensor data is below a threshold or no sensor data is available and generates at least one virtual traffic entity in the at least one determined area, wherein the virtual traffic entity is adapted to interact with the at least one predicted behavior of the vehicle. A risk measure for each combination of the at least one virtual traffic entity and the predicted behavior of the vehicle is estimated, the calculated risk measure is evaluated and a controlling action for the vehicle is executed based on the evaluated risk measure.

Abstract: The method for assisting a person in operating in a dynamic environment may form part of a mobility assistance system. The method comprises a step of acquiring sensor data comprising a time sequence of at least two consecutive images of the dynamic environment from at least one sensor, for example a camera. Optical flows are calculated based on the at least two consecutive images. Feature scores associated to spatial positions for selected regions in an image space are determined in order to generate a feature score field. An output signal including directional stimulus information is generated based on the generated feature score field, wherein the directional stimulus information comprises information on relative spatial relations between the selected regions. The generated output signal is provided to at least one actuator, which signals the directional stimulus information to the person.

Abstract: An advanced driver assistant system and a method for assisting a driver of a vehicle in driving the vehicle. The system includes sensing means for sensing an environment of the vehicle, a storing unit for storing a database for storing prototypical situations associated with risk information, a processing unit and an output unit to output an assistance signal on the basis of which driver information can be output and/or vehicle operators can be actuated. The processing unit is configured to determine parameters of the vehicle and/or objects in the environment of the vehicle based upon the sensing means output, to classify a traffic situation on the basis of the determined parameters by comparing the determined parameters with parameters of the prototypical traffic situations, to extract risk information from the database and to calculate an expected risk for the encountered traffic situation on the basis of the extracted risk information.

Abstract: In one aspect, an image processing method for processing images is provided, comprising the steps of: obtaining, from an optical sensor, at least two images, determining an image warping function at least partially compensating the distortion, applying the determined image warping function to the image including the distortion, and calculating by a processing unit, and outputting, a depth and/or disparity image from the at least two images.

Abstract: An automotive driver assistance for a vehicle with an improved capability to handle obstructed sensor coverage includes steps of acquiring sensor data on an environment of the vehicle from at least one sensor, of generating an environment representation based on the acquired sensor data and of predicting at least one behavior of the vehicle. The method determines at least one area in the environment of the vehicle wherein for the at least one area either a confidence for the sensor data is below a threshold or no sensor data is available and generates at least one virtual traffic entity in the at least one determined area, wherein the virtual traffic entity is adapted to interact with the at least one predicted behavior of the vehicle. A risk measure for each combination of the at least one virtual traffic entity and the predicted behavior of the vehicle is estimated, the calculated risk measure is evaluated and a controlling action for the vehicle is executed based on the evaluated risk measure.

Abstract: An advanced driver assistant system and a method for assisting a driver of a vehicle in driving the vehicle. The system includes sensing means for sensing an environment of the vehicle, a storing unit for storing a database for storing prototypical situations associated with risk information, a processing unit and an output unit to output an assistance signal on the basis of which driver information can be output and/or vehicle operators can be actuated. The processing unit is configured to determine parameters of the vehicle and/or objects in the environment of the vehicle based upon the sensing means output, to classify a traffic situation on the basis of the determined parameters by comparing the determined parameters with parameters of the prototypical traffic situations, to extract risk information from the database and to calculate an expected risk for the encountered traffic situation on the basis of the extracted risk information.

Abstract: The present invention presents a sensing system 1 and a corresponding method for detecting moving objects 11 in the surroundings of a vehicle 10. The sensing system 1 comprises an imaging unit 2 for obtaining an image stream 2a, a computing unit 3 for analyzing the image stream 2a, and a control unit 4 for controlling the vehicle 10 based on the analysis result of the computing unit 3. The sensing system 1 particularly employs a background-model-free estimation. The sensing system 1 is configured to perform a local analysis of two neighboring motion vectors 6, which are computed from points 7a in images 5a, 5b of the image stream 2a, and to determine, whether the points 7a corresponding to these motion vectors 6 belong to a particularly moving object.

Abstract: A method supports driving an ego-vehicle including a driver assistance system. A traffic participant or infrastructure element involved in the traffic situation is selected and taken into consideration for traffic scene analysis. A hypothetical future trajectory for the ego-vehicle is predicted by predicting the current state of the ego-vehicle and varied to generate a plurality of ego-trajectory alternatives including the calculated hypothetical future ego-trajectory. A hypothetical future trajectory from another traffic participant gained by predicting the current state of the traffic participant or calculating a hypothetical future state sequence of the infrastructure element is determined. Based upon at least one pair of ego-trajectory plus one other trajectory risk functions over time or along the calculated hypothetical future ego-trajectory alternatives are calculated. One risk function corresponds to one ego-trajectory alternative. The risk functions are combined into a risk map.

Abstract: A method for improving prediction results of advanced driver assistance systems of a vehicle includes obtaining map data including information about a road geometry in a proximity of the vehicle. A sensor means is assigned, sensing a surrounding of the vehicle. A virtual sensing means output is generated, corresponding to an output of the sensing means if the sensing means. The generation is based on a mathematical model of the sensing means. The surrounding of the vehicle is sensed, and a sensing means output is generated. The sensing means output is compared to the virtual sensing means output. Parameters of the mathematical model are modified, and the virtual sensing means output are generated and compared to the sensing means output. Map data is combined with information derived from the sensing means output to generate combined information, which is output for further processing.

Abstract: The invention provides an image processing method for processing a sequence of images, comprising the steps of: obtaining, from a visual sensor, at least two images of the sequence of images, detecting whether the images include a distortion, determining an image warping function at least partially compensating the distortion, applying the determined warping function to the image(s) including the distortion, and calculating by a processing unit, and outputting, an optical flow as a displacement vector field form the images.

Abstract: The invention presents a method for comparing the similarity between image patches comprising the steps of receiving form at least two sources at least two image patches, wherein each source supplies an image patch, comparing the received image patches by extracting a number of corresponding subpart pairs from each image patch, calculating a normalized local similarity score between all corresponding subpart pairs, calculating a total matching score by integrating the local similarity scores of all corresponding subpart pairs, and using the total matching score as an indicator for an image patch similarity, determining corresponding similar image patches based on the total matching score.

Abstract: A method supports driving an ego-vehicle including a driver assistance system. A traffic participant or infrastructure element involved in the traffic situation is selected and taken into consideration for traffic scene analysis. A hypothetical future trajectory for the ego-vehicle is predicted by predicting the current state of the ego-vehicle and varied to generate a plurality of ego-trajectory alternatives including the calculated hypothetical future ego-trajectory. A hypothetical future trajectory from another traffic participant gained by predicting the current state of the traffic participant or calculating a hypothetical future state sequence of the infrastructure element is determined. Based upon at least one pair of ego-trajectory plus one other trajectory risk functions over time or along the calculated hypothetical future ego-trajectory alternatives are calculated. One risk function corresponds to one ego-trajectory alternative. The risk functions are combined into a risk map.