Robert Boschgmbh

Thomas Benjamin Gussner

Oliver F Schwindt

Andreas Heyl

Stephan Reuter

Theresa Veronika Kienle

Joachim Carsten Mueller

Lutz Buerkle

Thomas Michalke

Folko Flehmig

Stefan Lang

Thomas Maurer

Claudius Glaeser

Claus Marberger

Dariu M Gavrila

Yuefeng Ma

Thomas Schlender

A method for evaluating locating measurements of a surroundings sensor for a motor vehicle. The method includes: associating locating measurements with an object described by an estimated object state, for the locating measurements in each case an association probability being determined for the association of the locating measurement with the object; estimating instantaneous state parameters of the object, including an adaptation of the state parameters to the locating measurements associated with the object, weightings of the locating measurements associated with the object being taken into consideration during the adaptation, for the locating measurements in each case the weighting being dependent on the determined association probability for the association of the particular locating measurement with the object; and transferring the estimated instantaneous state parameters of the object to a state estimator for updating the estimated state of the object. A sensor system is also described.

Evaluating locating measurements of a surroundings sensor for a motor vehicle

Methods and systems for multi-hypothesis object tracking for automated driving systems. One system includes an electronic processor configured to receive environment information and generate pseudo-measurement data associated with an object within an environment of the vehicle. The electronic processor is also configured to determine, based on the environment information and the pseudo-measurement data, a set of association hypotheses regarding the object. The electronic processor is also configured to determine, based on the set of association hypotheses, an object state of the object. The electronic processor is also configured to control the vehicle based on the determined object state.

Multi-hypothesis object tracking for automated driving systems

A method for identifying and classifying static radar targets with the aid of a radar sensor of a motor vehicle. The method includes: identifying an object as a static radar target based on the received radar signals reflected by the object, generating an occupancy pattern in an occupancy grid based on the received radar signals reflected by the object, storing an assignment, which assigns the generated occupancy pattern to the static radar target, classifying the static radar target as belonging to one or multiple groups of static radar targets based on characteristic features of radar signatures of the received radar signals reflected by the corresponding object. A radar sensor is also described.

Method for identifying static radar targets using a radar sensor for motor vehicles

A system for a reliability of objects for a driver assistance or automated driving of a vehicle includes a plurality of sensors that include one or more sensor modalities for providing sensor data for the objects. An electronic tracking unit is configured to receive the sensor data to determine a detection probability (p_D) for each of the plurality of sensors for each of the objects, to determine an existence probability (p_ex) for each of the plurality of sensors for each of the objects, and to provide vectors for each of the objects based on the existence probability (p_ex) for each contributing one of the plurality of sensors for the specific object. The vectors are provided by the electronic tracking unit for display as an object interface on a display device. The vectors are independent from the sensor data from the plurality of sensors.

Redundancy information for object interface for highly and fully automated driving

A method for monitoring a vehicle system configured to detect an environment of a vehicle, wherein the vehicle system has a sensor system with at least two sensor units configured to capture the environment of the vehicle, and an evaluation unit configured to detect objects in the environment of the vehicle by merging sensor data of the at least two sensor units. The method includes, for each of the detected objects, using sensor data to determine separately for each of the at least two sensor units a probability of existence indicating a probability of the detected object representing a real object, and a probability of detection indicating a probability with which the detected object can be captured by the sensors. The method includes determining whether the vehicle system is in a robust state based on the probability of existence and the probability of detection.

Method for monitoring a vehicle system for detecting an environment of a vehicle

Redundant environment perception tracking for automated driving systems. One example embodiment provides a surveillance system, the system including a plurality of sensors, a memory, and an electronic processor. The electronic processor is configured to receive, from the plurality of sensors, environmental information of a common field of view, generate, based on the environmental information, a plurality of hypotheses regarding an object within the common field of view, the plurality of hypotheses including at least one set of hypotheses excluding the environmental information from at least one sensor of a first sensor type, determine, based on a subset of the plurality of hypotheses, an object state of the object, wherein the subset includes the at least one set of hypotheses excluding the environmental information from the at least one sensor of the first sensor type, and track the object based on the object state that is determined.

Redundant environment perception tracking for automated driving systems

Methods and systems for controlling an autonomous vehicle. The method includes receiving sensor data from a plurality of sensors, determining, a plurality of probability hypotheses based upon the sensor data, and receiving metadata from at least one sensor of the plurality of sensors. An integrity level of at least one of the plurality of probability hypotheses is determined based upon the received metadata and at least one action is determined based upon the determined integrity level and at least one probability hypothesis of the plurality of probability hypotheses. The at least one action is then initiated by an electronic controller for the vehicle.

Safety architecture for control of autonomous vehicle

A system and method for determining if a field calibration of a subject sensor associated with a vehicle is warranted, and calibrating the subject sensor using a sensor associated with another vehicle when calibration is warranted. Reference vehicles may perform predetermined maneuvers in order to provide additional measurements for use during calibration.

Vehicle sensor field calibration utilizing other vehicles

Systems and methods for controlling a vehicle. The system includes one or more sensors positioned on the vehicle and configured to sense an environment surrounding the vehicle, a collision mitigation subsystem configured to control a braking system of the vehicle, and an autonomous driving subsystem communicatively coupled to the one or more sensors and the collision mitigation subsystem. The autonomous driving subsystem is configured to receive sensor information from the one or more sensors and generate, based on the sensor information, a model of the environment surrounding the vehicle. The autonomous driving subsystem is configured to determine, based on the model of the environment surrounding the vehicle, a plurality of possible trajectories for the vehicle and to select, from the plurality of possible trajectories, a travel path for the vehicle. The autonomous driving subsystem is configured to transmit the travel path to the collision mitigation subsystem.

Use of driver assistance collision mitigation systems with autonomous driving systems

A system and method of sensor validation utilizing a sensor-fusion network. The sensor-fusion network may comprise a number of sensors associated with one or more vehicles having autonomous or partially autonomous driving functions.

Inter-vehicle sensor validation using senso-fusion network

Redundant environment perception tracking for automated driving systems. One example embodiment provides an automated driving system for a vehicle, the system including a plurality of sensors, a memory, and an electronic processor. The electronic processor is configured to receive, from the plurality of sensors, environmental information of a common field of view, generate, based on the environmental information, a plurality of hypotheses regarding an object within the common field of view, the plurality of hypotheses including at least one set of hypotheses excluding the environmental information from at least one sensor of a first sensor type, determine, based on a subset of the plurality of hypotheses, an object state of the object, wherein the subset includes the at least one set of hypotheses excluding the environmental information from the at least one sensor, and perform a vehicular maneuver based on the object state that is determined.

A method is described for avoiding or mitigating a collision of a vehicle with an obstacle in a traffic lane of the vehicle, if the obstacle is at least partially blocking a planned travel envelope of the vehicle, the method including a step of detecting and a step of adapting. In the step of detecting, a position and/or trajectory of the obstacle is detected. In the step of adapting, the travel envelope is adapted with the aid of the position and/or the trajectory, in order to avoid or mitigate the collision.

Method and device for avoiding or mitigating a collision of a vehicle with an obstacle

A method for guiding a vehicle in the surrounding environment of an object. The method includes reading in a multiplicity of view ray endpoints in a three-dimensional image, produced by a stereo camera of the vehicle, of a surrounding environment, containing the object, of the vehicle, at least one of the view ray endpoints representing an outer surface of the object, connecting the multiplicity of view ray endpoints to form a polygon that represents a free surface to be traveled by the vehicle, and generating a driving corridor, provided for the vehicle, for driving around the object, based on the free surface.

Method and apparatus for guiding a vehicle in the surroundings of an object

A method for classifying a behavior, of a pedestrian when crossing a roadway of a vehicle, includes reading in a sensor signal to detect the pedestrian and at least one piece of surroundings information regarding surroundings of the pedestrian. The sensor signal represents here a signal of at least one sensor of the vehicle. The method further includes ascertaining at least one physical variable of a correlation between the pedestrian and the at least one piece of surroundings information. Finally, the method includes classifying the behavior of the pedestrian using the at least one physical variable.

Method and device for classifying a behavior of a pedestrian when crossing a roadway of a vehicle as well as passenger protection system of a vehicle

In a method for ascertaining the stress level of the driver, the setpoint trajectory of the vehicle is ascertained and compared to the actual trajectory. A higher stress level is inferred from an increasing frequency and/or magnitude of the deviation of the actual trajectory from the setpoint trajectory.

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Ludwigsburg, Germany

Thomas Benjamin Gussner