Robert Boschgmbh

Karsten Behrendt

Jason Scott Hardy

Jonas Witt

Mithun Jacob

Jan Kleindieck

A method for assessing possible trajectories of road users in a traffic environment includes capturing the traffic environment with static and dynamic features, identifying at least one traffic user, determining at least one possible trajectory for at least one road user in the traffic environment, and assessing the at least one determined possible trajectory for the at least one road user with an adapted/trained recommendation service and the captured traffic environment.

Method for the assessment of possible trajectories

A system and method for localizing an entity includes a processing system with at least one processing device. The processing system is configured to obtain sensor data from a sensor system that includes at least a first set of sensors and a second set of sensors. The processing system is configured to produce a map image with a map region that is selected and aligned based on a localization estimate. The localization estimate is based on sensor data from the first set of sensors. The processing system is configured to extract sets of localization features from the sensor data of the second set of sensors. The processing system is configured to generate visualization images in which each visualization image includes a respective set of localization features. The processing system is configured to generate localization output data for the vehicle in real-time by optimizing an image registration of the map image relative to the visualization images.

Localization with neural network based image registration of sensor data and map data

Systems and methods of detecting and tracking one or more vehicles in a field of view of an imaging system using neural network processing. An electronic controller receives an input image from a camera mounted on the host vehicle. The electronic controller applies a neural network configured to output a definition of a three-dimensional bounding box based at least in part on the input image. The three-dimensional bounding box indicates a size and a position of a detected vehicle in a field of view of the input image. The three-dimensional bounding box includes a first quadrilateral shape outlining a rear or front of the detected vehicle and a second quadrilateral shape outline a side of the detected vehicle.

Direct vehicle detection as 3D bounding boxes using neural network image processing

A method and system for navigating a vehicle using automatically labeled images. The system includes a camera configured to capture an image of a roadway and an electronic processor communicatively connected to the camera. The electronic processor is configured to load a map that includes a first plurality of features and receive the image. The image includes a second plurality of features. The electronic processor is further configured to project the map onto the image; detect the second plurality of features within the image; and align the map with the image by aligning the first plurality of features with the second plurality of features. The electronic processor is further configured to copy a label describing one of the first plurality of features onto a corresponding one of the second plurality of features to create a labelled image and to use the labelled image to assist in navigation of the vehicle.

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Sunnyvale, CA, United States of America

Karsten Behrendt