Patents by Inventor Maximilian Schaefer

Maximilian Schaefer has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240166204
    Abstract: A computer-implemented method for collision threat assessment of a vehicle includes obtaining context information for the surrounding of the vehicle, including information about at least one road user. The method includes determining ego occupancy information for multiple possible future locations of the vehicle at multiple future points in time based on the context information. The method includes determining road user occupancy information for multiple possible future locations of the at least one road user at multiple future points in time based on the context information. The method includes fusing the ego occupancy information and the road user occupancy information to obtain fused occupancy information at each future point in time. The method includes determining a collision threat value based on the fused occupancy information.
    Type: Application
    Filed: November 17, 2023
    Publication date: May 23, 2024
    Inventors: Maximilian Schaefer, Kun Zhao, Markus Buehren
  • Publication number: 20240144696
    Abstract: A method of determining information related to a road user in an environment of a vehicle includes receiving, from vehicle sensors, a digital image and a Lidar point cloud. The digital image and the Lidar point cloud represent a scene in the environment of the vehicle. The method includes detecting a road user in the scene based on the received digital image and Lidar point cloud. The method includes generating a combined digital representation of the detected road user by combining corresponding image data and Lidar data associated with the detected road user. The method includes determining information related to the detected road user by processing the combined digital representation of the detected road user.
    Type: Application
    Filed: October 30, 2023
    Publication date: May 2, 2024
    Inventors: Lukas Hahn, André Paus, Maximilian Schäfer
  • Publication number: 20240092430
    Abstract: A rear structure for a motor vehicle includes two longitudinal members, a front cross member and a rear cross member. The front cross member and the rear cross member connect the longitudinal members to one another. The rear structure is a single-piece die-cast component. A motor vehicle includes such a rear structure.
    Type: Application
    Filed: March 15, 2022
    Publication date: March 21, 2024
    Inventors: Manuel ANASENZL, Moritz FRENZEL, Christian HASLAUER, Daniel HEIM, Maximilian JUENGLING, Manuel RIEDL, Andre SCHAEFER, Marcel STERZENBACH
  • Publication number: 20230242159
    Abstract: Computer implemented method for target selection in the vicinity of a vehicle, comprising obtaining vehicle state information, the vehicle state information comprising dynamic information regarding the vehicle, predicting a first trajectory of the vehicle based on the vehicle state information for a first prediction time horizon, detecting road users in the vicinity of the vehicle, determining state information from the detected road users, the state information comprising dynamic information regarding the road users, predicting a second trajectory of the vehicle based on the vehicle state information and the road users state information for the first prediction time horizon and performing a first similarity comparison of the first predicted trajectory and the second predicted trajectory of the vehicle to determine whether the detected road users are a potential target of the vehicle for the first prediction time horizon.
    Type: Application
    Filed: January 17, 2023
    Publication date: August 3, 2023
    Applicant: Aptiv Technologies Limited
    Inventors: Kun Zhao, Maximilian Schaefer, Markus Buehren
  • Publication number: 20230048926
    Abstract: A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and/or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and/or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.
    Type: Application
    Filed: August 16, 2022
    Publication date: February 16, 2023
    Inventors: Thomas Kurbiel, Maximilian Schaefer, Kun Zhao, Markus Bühren
  • Publication number: 20220242453
    Abstract: The prediction system for predicting an information related to a pedestrian has a tracking module that detects and tracks in real-time a pedestrian in an operating area, from sensor data; a machine-learning prediction module that performs a prediction of information at future times related to the tracked pedestrian using a machine-learning algorithm from input data including data of the tracked pedestrian transmitted by the tracking module and map data of the operating area; a pedestrian behavior assessment module that determines additional data of the tracked pedestrian representative of a real time behavior of the pedestrian, and said additional data of the tracked pedestrian is used by the machine-learning prediction module as another input data to perform the prediction.
    Type: Application
    Filed: February 1, 2022
    Publication date: August 4, 2022
    Inventors: Lukas Hahn, Maximilian Schaefer, Kun Zhao, Frederik Lenard Hasecke, Yvonne Schnickmann, Andre Paus
  • Patent number: 8566264
    Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method is used to efficiently reduce a high-dimensional state space describing the technical system to a smaller dimension. The reduction of the state space is performed using an artificial recurrent neuronal network. In addition, the reduction of the state space enables conventional learning methods, which are only designed for small dimensions of state spaces, to be applied to complex technical systems with an initially large state space, wherein the conventional learning methods are performed in the reduced state space. The method can be used with any technical system, especially gas turbines.
    Type: Grant
    Filed: December 19, 2007
    Date of Patent: October 22, 2013
    Assignee: Siemens Aktiengesellschaft
    Inventors: Anton Maximilian Schäfer, Steffen Udluft
  • Patent number: 8554707
    Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method includes two steps, namely modeling the dynamic behavior of the technical system with a recurrent neural network using training data, the recurrent neural network includes states and actions determined using a simulation model at different times and learning an action selection rule by the recurrent neural network to a further neural network. The method can be used with any technical system in order to control the system in an optimum computer-assisted manner. For example, the method can be used in the control of a gas turbine.
    Type: Grant
    Filed: December 19, 2007
    Date of Patent: October 8, 2013
    Assignee: Siemens Aktiengesellschaft
    Inventors: Anton Maximilian Schäfer, Steffen Udluft, Hans-Georg Zimmermann
  • Patent number: 8494980
    Abstract: A method for the computer-assisted exploration of states of a technical system is provided. The states of the technical system are run by carrying out an action in a respective state of the technical system, the action leading to a new state. A safety function and a feedback rule are used to ensure that a large volume of data of states and actions is run during exploration and that at the same time no inadmissible actions occur which could lead directly or indirectly to the technical system being damaged or to a defective operating state. The method allows a large number of states and actions relating to the technical system to be collected and may be used for any technical system, especially the exploration of states in a gas turbine. The method may be used both in the real operation and during simulation of the operation of a technical system.
    Type: Grant
    Filed: September 29, 2008
    Date of Patent: July 23, 2013
    Assignee: Siemens Aktiengesellschaft
    Inventors: Alexander Hans, Daniel Schneegaβ, Anton Maximilian Schäfer, Volkmar Sterzing, Steffen Udluft
  • Patent number: 8160978
    Abstract: A method for computer-aided control of any technical system is provided. The method includes two steps, the learning of the dynamic with historical data based on a recurrent neural network and a subsequent learning of an optimal regulation by coupling the recurrent neural network to a further neural network. The recurrent neural network has a hidden layer comprising a first and a second hidden state at a respective time point. The first hidden state is coupled to the second hidden state using a matrix to be learned. This allows a bottleneck structure to be created, in that the dimension of the first hidden state is smaller than the dimension of the second hidden state or vice versa. The autonomous dynamic is taken into account during the learning of the network, thereby improving the approximation capacity of the network. The technical system includes a gas turbine.
    Type: Grant
    Filed: April 21, 2009
    Date of Patent: April 17, 2012
    Assignee: Siemens Aktiengesellschaft
    Inventors: Anton Maximilian Schäfer, Volkmar Sterzing, Steffen Udluft
  • Publication number: 20100241243
    Abstract: A method for the computer-assisted exploration of states of a technical system is provided. The states of the technical system are run by carrying out an action in a respective state of the technical system, the action leading to a new state. A safety function and a feedback rule are used to ensure that a large volume of data of states and actions is run during exploration and that at the same time no inadmissible actions occur which could lead directly or indirectly to the technical system being damaged or to a defective operating state. The method allows a large number of states and actions relating to the technical system to be collected and may be used for any technical system, especially the exploration of states in a gas turbine. The method may be used both in the real operation and during simulation of the operation of a technical system.
    Type: Application
    Filed: September 29, 2008
    Publication date: September 23, 2010
    Inventors: Alexander Hans, Daniel Schneegass, Anton Maximilian Schäfer, Volkmar Sterzing, Steffen Udluft
  • Publication number: 20100094788
    Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method includes two steps, namely learning the dynamics of a technical system using historical data based on a recurrent neuronal network, and the subsequent learning of an optimum regulation by coupling the recurrent neuronal network to another neuronal network. The method can be used with any technical system in order to control the system in an optimum computer-assisted manner. For example, the method can be used in the control of a gas turbine.
    Type: Application
    Filed: December 19, 2007
    Publication date: April 15, 2010
    Inventors: Anton Maximilian Schäfer, Steffen Udluft, Hans-Georg Zimmerman
  • Publication number: 20100049339
    Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method is used to efficiently reduce a high-dimensional state space describing the technical system to a smaller dimension. The reduction of the state space is performed using an artificial recurrent neuronal network. In addition, the reduction of the state space enables conventional learning methods, which are only designed for small dimensions of state spaces, to be applied to complex technical systems with an initially large state space, wherein the conventional learning methods are performed in the reduced state space. The method can be used with any technical system, especially gas turbines.
    Type: Application
    Filed: December 19, 2007
    Publication date: February 25, 2010
    Inventors: Anton Maximilian Schäfer, Steffen Udluft