Gm Cruise Holdings LLC

Airbus Operations Gmbh

Airbus Defence and Space Gmbh

Michael Meyer

Georg Kuschk

Marc Unzueta Canals

Bernd Trahmer

Sven Möller

Jan Lesemann

Marc Unzueta

Stefan Storm

Wolfgang Machunze

Matthias Lengers

Matthias Bauer

Ralph Sturm

Peter Langenbacher

Thomas Canton

Filippo Grazioli

Sylvia Pomorin

Kathrin Meyer-Rusitschka

Daniel Redondo Garcia

Autonomous vehicles (AVs) utilize perception and understanding of objects on the road to predict behaviors of the objects, and to plan a trajectory for the vehicle. In some scenarios, an AV may benefit from having information about an object that is not within a perceivable area of the sensors of the AV. Another AV in the area that has the object within a perceivable area of the sensors of the other AV may share information with the AV. Rich information about the object determined by the other AV can be compressed using an encoder and transferred efficiently to the AV for inclusion in a temporal map that combines locally determined object information and transferred object information determined by other AV(s). The temporal map may be used in one or more parts of the software stack of the AV.

Online occlusion prediction for autonomous vehicles

Disclosed are embodiments for generating long-range objects dataset using proximal time-synced data for object detection models. In some aspects, a method includes receiving a set of sensor data for a first autonomous vehicle (AV) corresponding to first scene data that is part of an intersecting field of view (FoV) with second scene data of a second AV; inputting the set of sensor data to a simulated trained object detection model of the first AV to generate a first set of object detections for the first scene data; comparing the first set with a second set of object detections generated for the second scene data; supplementing the first set of object detections with information from the second set of object detections to generate a supplemental set of object detections; and generating, using the supplemental set of object detections, a training data set for an object detection model of the first AV.

Generating long-range objects dataset using proximal time-synced data for object detection models

Technologies for detecting, identifying, and tracking objects in a scene based upon images generated by a camera are described. A computing system obtains a perspective image from the camera and generates a feature image based upon the perspective image. The feature image has X×Y pixels, and each pixel in the X×Y pixels has a feature vector that includes values assigned thereto. The feature image is provided as input to a machine learning model, where the machine learning model generates a tensor based upon the feature image, where the tensor includes several bins that include values that are representative of likelihoods that an object exists in the scene at distances from the camera that correspond to the bins. The machine learning model is trained using a Gumbel-softmax algorithm. The computing system generates the image in the overhead view based upon the tensor output by the machine learning model.

Detecting, identifying, and tracking objects based upon a tensor with sparsely computed non-zero values

A method and apparatus for implementing a method are disclosed. The method includes providing point cloud data to a machine learning algorithm, the point cloud data detected in the vicinity of an autonomous vehicle. The method further includes differentiating, via the machine learning algorithm, in the point cloud, data directly representing a location of a first object and data indirectly representing a location of a second object. The method includes transforming the data indirectly representing the location of the second object into data directly representing the location of the second object and generating corrected point cloud data based on the data directly representing the location of the first object and the data directly representing the location of the second object. The method includes outputting the corrected point cloud data to the autonomous vehicle.

Radar point cloud multipath reflection compensation

An aircraft section, between a cockpit and a cabin, comprises an aisle to access to the cockpit from the cabin, and a monument extending in a longitudinal direction and a transverse direction. The monument includes a first lavatory, a second lavatory adjacent thereto in the transverse direction and comprising the aisle, a wall separating the first lavatory from the second lavatory, a boundary wall delimiting the first lavatory and a portion of a rear wall of the cockpit, and a second boundary wall delimiting the second lavatory and a second portion of the rear wall of the cockpit. The boundary walls are parallel or continuous. The monument further includes an outer door movable between an open position, where the cockpit aisle is accessible from the cabin area, and a closed position, where the cockpit aisle is blocked with respect to the cabin area.

Aircraft section and aircraft having a monument with two lavatories

Technologies described herein relate to learning parameter values of a preprocessing algorithm that is executed in a radar system. The preprocessing algorithm is configured to receive raw radar data as input and is further configured to generate three-dimensional point clouds as output, where the preprocessing algorithm generates the three-dimensional point clouds based upon the raw radar data and the parameter values that are assigned to the preprocessing algorithm. To learn the parameter values, the preprocessing algorithm is converted to auto-differentiated form and is added as an input network layer to a deep neural network (DNN) that is configured to identify objects represented in three-dimensional point clouds. The parameter values are learned jointly with weight matrices of the DNN.

Setting radar parameters for generating point clouds based upon raw radar data

A radar sensor system receives or generates radar data indicative of radar returns received at an antenna array. An encoder neural network encodes the radar data into first embeddings defined in a first latent space. A transformer neural network receives the first embeddings and transforms the first embeddings to second embeddings defined in a second latent space. A decoder neural network receives the second embeddings and decodes the second embeddings to generate beamformed radar data.

Radar beamforming by way of latent space encoding

The technologies described herein relate to a computing architecture in an autonomous vehicle (AV). The AV includes a radar system that includes a local data processing device. The AV also includes a centralized data processing device. The local data processing device is configured to perform processing on raw sensor data generated by the radar system to form a feature vector. The local data processing device transmits the feature vector to the centralized data processing device, which performs further processing to identify an object in an environment of the AV.

Computing architecture of an autonomous vehicle

A method of preventing separation of a fluid flow flowing over a flow surface is described. The method includes generating longitudinal vortices for suppressing or delaying separation of the flow, and enhancing the longitudinal vortices. A flow body system having a flow body and a flow control arrangement is further described.

Method of preventing separation of a fluid flow and flow body system

An airfoil portion includes an outer skin member and an inner stiffening member. Either the outer skin member or the inner stiffening member or both are arranged to delimit a chamber. The chamber is configured to contain an inner pressure, wherein the inner pressure differs from a pressure external to the chamber. In an embodiment, an aircraft includes such an airfoil portion. A method for providing such an airfoil portion with a chamber is also described.

Airfoil portion with a chamber

Fuselage

According to an exemplary embodiment of the invention a fuselage segment of an aircraft fuselage is provided, which aircraft fuselage comprises four different radii of curvature, wherein two radii of curvature comprise center points that are not situated on a vertical symmetry axis of the fuselage segment.

Segment of a fuselage of an aircraft

A passenger stairway, which may be integrated with an aircraft, contains at least one first stairway segment having a first plurality of steps and a receptacle device, and at least one second stairway segment having a second plurality of steps, which is insertable into the receptacle device, so that the first and second stairway segments come to rest lying one on top of another.

Passenger stairway for an aircraft and method for pulling in and out the passenger stairway

A climatic chamber for rapidly reaching and maintaining a predetermined air humidity and/or a predetermined temperature. The air in a sample space is conveyable by at least one fan from an outlet of sample space at least partially via a bypass branch and/or at least partially via a drying branch back to an inlet of sample space. The air is circulated continuously by the fans in a substantially self-contained circuit. The air is guided by guide elements through the bypass branch and/or drying branch.

Growing community of inventors

Munich, Germany

Michael Meyer