Robert Boschgmbh

Robert Bosch Gbmh

Becker, Jan

Schwindt, Oliver

Kammel, Soeren

Oliver F Schwindt

Stephan Reuter

Thomas Benjamin Gussner

Theresa Veronika Kienle

Andreas Heyl

Joachim Carsten Mueller

Bhavana Chakraborty

Kevin Buckner

James Kim

Tobias Berling

Brad Alan Ignaczak

Jan Becker

Raphael Hellinger

Ruediger Jordan

Joerg Mueller

Thomas Voorheis

Maria Eugenia Garcia Bordes

Christos Zalidis

Andrei Vatavu

Oliver Pink

Karsten Muehlmann

Jason Scott Hardy

Soeren Kammel

Marcel Brueckner

Oliver Wackerl

Sameer J Parikh

Suresh Govindachar

Xavier Zhu

Jean-Francois Pelosse

Gunther Krehl

Shilpa Gulati

Andreas Mehs

Frank Von Zeppelin

Michael Maile

Michael Neumeyer

Moritz Dechant

Dominik Nuss

Thomas Vorheis

Systems and methods for controlling an autonomous vehicle. One system includes an electronic processor and a memory configured to store a multi-layer grid and a multi-instanceable sensor plugin. The multi-layer grid includes information associated with positions in the environment. A plurality of sensors are configured to output sensor information. The electronic processor is configured to, for each of the plurality of sensors, determine a modality of the sensor and generate an instance of the sensor plugin based on the modality. The electronic processor receives, processes, and determines a plausibility of the sensor information. In response to determining that the sensor information is plausible, the electronic processor integrates the sensor information to the multi-layer grid. The electronic processor controls vehicle movement based on the multi-layer grid.

Sensor plugin architecture for grid map

Systems and methods for controlling an autonomous vehicle. One system includes an electronic processor and a sensor configured to detect a dynamic object in an environment. The electronic processor is configured to generate a dynamic object list including dynamic object information associated with the detected dynamic object. The electronic processor is also configured to receive a static occupancy grid including a plurality of cells storing occupancy information and occupancy probabilities associated with a static object in the environment. The electronic processor is further configured to superimpose the dynamic object information on the static occupancy grid, determine an overlap of the detected dynamic object and the static object, determine a dynamic object existence probability associated with the detected dynamic object based on the overlap, and classify the detected dynamic object as a ghost object based on the dynamic object existence probability.

Using static grid map to cross-check dynamic objects

A method and device are disclosed for improved object detection in an area surrounding a robot. In the method, first and second sensing data are obtained, which can be assigned to a first or second sensing means of the robot, respectively, and which contain at least one portion of the area surrounding the robot. An objection detection of an object in the area surrounding the robot is carried out using a fusion of at least the first and the second sensing data. An item of redundancy information is generated, which is assigned to the object detection and at least indicates whether the detected object has been detected using only the first or only the second sensing data or whether the detected object or at least one or more sections of same has been detected redundantly using both the first and the second sensing data.

Method and device for operating a robot with improved object detection

Systems and methods for controlling an autonomous vehicle. One system includes an electronic processor and a memory configured to store an occupancy grid map. The occupancy grid map includes a set of cells associated with respective positions in an environment. A sensor is configured to output sensed occupancy information associated with positions in the environment. The electronic processor is configured to determine a position of the autonomous vehicle within the environment, and receive, from a server, a high density map. The high density map contains occupancy information. The electronic processor initializes the cells with occupancy information based on the high density map. The electronic processor confirms, based on the sensed occupancy information, a state of the set of the cells of the occupancy grid map, and controls vehicle movement based on the occupancy grid map.

Use of high density map in occupancy grid

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 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

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 for a vehicle, which drives in an at least in-part-automated manner is configured to determine a control signal for a control system. The system includes a sensor, a planning module, and a monitoring module. The sensor is configured to detect an object in a surrounding area of the vehicle and store a corresponding object representation. The planning module is configured to determine, based to the stored object representation, a first trajectory and a first probability of collision of the first trajectory for the vehicle. The monitoring module is configured to perform one of following actions when the first probability of collision exceeds a predefined probability of collision: determine, using the planning module and based on the stored object representation, a further trajectory having a further probability of collision and a maximum deceleration of the further trajectory; or assess the stored object representation of the object using the sensor.

Determination of a control signal for an in-part-autonomous vehicle

A method for managing tracklets in a particle filter estimation framework includes executing a tracklet prediction dependent on a list of previous tracklets, thereby determining persistent tracklets and new tracklets; sampling new measurements for initializing the new tracklets, thereby determining an amount of estimated new tracklets; and determining an amount of the persistent tracklets dependent on the list of previous tracklets. The method further includes determining an amount of the new tracklets and an amount of updated persistent tracklets to be sampled dependent on the amount of estimated new tracklets, the amount of the persistent tracklets, and a memory bound; sampling the updated persistent tracklets from a list of the persistent tracklets dependent on the determined amount of the updated persistent tracklets; and sampling the new tracklets from unassociated measurements dependent on the determined amount of the new tracklets.

Method for managing tracklets in a particle filter estimation framework

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

A system and method for estimating an orientation of a traffic participant. In one example, the system includes a sensor configured to detect a traffic participant and an electronic controller configured to receive a signal from the sensor, compare a location of the vehicle to a map of expected orientation of traffic participants to estimate an orientation of the traffic participant, perform a calculation based upon the signal from the sensor to estimate an orientation of the traffic participant if an expected orientation is not determined by the comparison of the location of the vehicle to the map of expected orientation of traffic participants, and generate a notification based upon the estimated orientation of the traffic participant.

Systems and methods for vehicle to improve an orientation estimation of a traffic participant

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.

Growing community of inventors

Sunnyvale, CA, United States of America

Oliver F Schwindt