Aptiv Technologies Ag

Moritz Luszek

Jan Siegemund

Dominic Spata

Mirko Meuter

Stephanie Lessmann

Yu Su

Weimeng Zhu

Adrian Becker

Markus Schoeler

Arne Grumpe

Florian Kästner

Alessandro Cennamo

Jittu Kurian

Simon Roesler

Zhiheng Niu

Sven Labusch

Saeid Khalili Dehkordi

Marco Braun

Kevin Kollek

The present disclosure relates to methods and systems for object tracking, for example for object detection and grid segmentation using recurrent neural networks. A computer implemented method for object tracking comprises the following steps carried out by computer hardware components: providing random values as a hidden state of a trained neural network for an initial time step, wherein the hidden state represents an encoding of sensor data acquired over consecutive time steps in a grid structure, wherein the hidden state further represents an offset indicating a movement of the object between the consecutive time steps; iteratively determining an updated hidden state by processing a present hidden state and present sensor data using the trained neural network; and determining object tracking information based on the updated hidden state.

Methods and systems for object tracking

A computer implemented method for detection of objects in a vicinity of a vehicle comprises the following steps carried out by computer hardware components: acquiring radar data from a radar sensor; determining a plurality of features based on the radar data; providing the plurality of features to a single detection head; and determining a plurality of properties of an object based on an output of the single detection head.

Methods and systems for detection of objects in a vicinity of a vehicle

A method is provided for semantic segmentation of an environment of a vehicle. Via a processing device, a grid of cells is defined dividing the environment of the vehicle. A radar point cloud is received from a plurality of radar sensors, and at least one feature of the radar point cloud is assigned to each grid cell. By using a neural network including deterministic weights, high-level features are extracted for each grid cell. Several classes are defined for the grid cells. For layers of a Bayesian neural network, various sets of weights are determined probabilistically. Via the Bayesian neural network, confidence values are determined for each class and for each grid cell based on the high-level features and based on the various sets of weights in order to determine a predicted class and an extent of uncertainty for the predicted class for each grid cell.

Method for determining a semantic segmentation of an environment of a vehicle

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Detmold, Germany

Moritz Luszek