Aptiv Technologies Ag

Simon Roesler

Adrian Becker

Jan Konried Schiffmann

Mirko Meuter

Stephanie Lessmann

Yu Su

Weimeng Zhu

Jan Siegemund

Markus Schoeler

Arne Grumpe

Florian Kästner

Alessandro Cennamo

Dominic Spata

Moritz Luszek

Jittu Kurian

Zhiheng Niu

Saeid Khalili Dehkordi

Sven Labusch

Marco Braun

Kevin Kollek

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

This document describes techniques and systems for a partially-learned model for speed estimates in radar tracking. A radar system is described that determines radial-velocity maps of potential detections in an environment of a vehicle. The model uses a data cube to determine predicted boxes for the potential detections. Using the predicted boxes, the radar system determines Doppler measurements associated with the potential detections that correspond to the predicted boxes. The Doppler measurements are used to determine speed estimates for the predicted boxes based on the corresponding potential detections. These speed estimates may be more accurate than a speed estimate derived from the data cube and the model. Driving decisions supported by the speed estimates may result in safer and more comfortable vehicle behavior.

Partially-learned model for speed estimates in radar tracking

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Neuss, Germany

Simon Roesler