Granted Patent B2

US 12,352,849 · App. 17/384,493 · Granted Jul 8, 2025

Inventors: Mirko Meuter (Erkrath, DE); Jittu Kurian (Wuppertal, DE); Yu Su (Wuppertal, DE); Jan Siegemund (Cologne, DE); Zhiheng Niu (Wuppertal, DE); Stephanie Lessmann (Erkrath, DE); Saeid Khalili Dehkordi (Berlin, DE); Florian Kästner (Bochum, DE); Igor Kossaczky (Wuppertal, DE); Sven Labusch (Cologne, DE); Arne Grumpe (Essen, DE); Markus Schoeler (Wuppertal, DE); Moritz Luszek (Detmold, DE); Weimeng Zhu (Wuppertal, DE); Adrian Becker (Leverkusen, DE); Alessandro Cennamo (Wuppertal, DE); Kevin Kollek (Wuppertal, DE); Marco Braun (Koblenz, DE); Dominic Spata (Witten, DE); Simon Roesler (Neuss, DE)

Assignee: Aptiv Technologies AG

G01S13/931B60W60/001G01S7/41G01S13/865G01S13/867G06F18/253G06N3/04G06V20/58B60W2420/403B60W2420/408B60W2554/404

Abstract

Claims (63)

1. A method, comprising:

detecting, using computer hardware components of a vehicle, objects in a vicinity of a vehicle by:

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 that comprises a plurality of sequentially arranged layers, the single detection head being free from layers arranged in parallel;

determining a plurality of properties of each object based on an output of the single detection head;

carrying out, with an ego-motion compensation module, a nearest neighbor interpolation to determine a new position of each object in a current time step and avoiding drift due to an accumulation of positional errors in positions of the objects over time by recording a residual part indicating a positional error of a movement of each object from the current time step;

determining the new position of each object based on a transformation grid from the current time step and a previous residual part for each object from a previous time step;

determining a subsequent position of each object in a subsequent time step based on the recorded residual part for each object; and

detecting the objects based on a regression subnet comprising the ego-motion compensation module; and

controlling, by an autonomous driving system of the vehicle, autonomous driving of the vehicle based on the plurality of properties of each object.

2. The method of claim 1 ,

wherein each of the plurality of features is connected to an input of the single detection head.

3. The method of claim 1 ,

wherein the plurality of features are determined using an artificial neural network.

4. The method of claim 1 ,

wherein the single detection head is trained for the plurality of properties simultaneously.

5. The method of claim 1 ,

wherein the plurality of properties comprises at least two of a class of the object, a size of the object, or a yaw angle of the object.

6. The method of claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:

determining a radar data cube based on the radar data;

providing the radar data cube to a plurality of layers of a neural network;

resampling the output of the plurality of layers into a vehicle coordinate system; and

determining the plurality of features based on the resampled output.

7. The method of claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:

fusing data from a plurality of radar sensors including the radar sensor; and

determining the plurality of features further based on the fused data.

8. The method of claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:

acquiring camera data from a camera;

wherein the plurality of features are determined further based on the camera data.

9. The method of claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises:

acquiring lidar data from a lidar sensor; and

determining the plurality of features further based on the lidar data.

10. The method of claim 1 , wherein detecting the objects in the vicinity of the vehicle further comprises determining an angle of arrival based on the radar data.

11. The method of claim 10 , wherein the angle of arrival is determined using an artificial neural network with a plurality of layers.

12. The method of claim 11 , wherein the artificial neural network further comprises a dropout layer.

13. The method of claim 1 , wherein the regression subnet further comprises at least one of a u-shaped network, or a LSTM.

14. The method of claim 1 , wherein the ego-motion compensation module is configured to carry out ego-motion compensation of an output of a recurrent network of the previous time step, and input the result of the ego-motion compensation into a recurrent network of the current time step.

15. A system comprising a plurality of computer hardware components configured to:

detect objects in a vicinity of a vehicle by:

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 that comprises a plurality of sequentially arranged layers, the single detection head being free from layers arranged in parallel;

determining a plurality of properties of each object based on an output of the single detection head;

carrying out, with an ego-motion compensation module, a nearest neighbor interpolation to determine a new position of each object in a current time step and avoiding drift due to an accumulation of positional errors in positions of the objects over time by recording a residual part indicating a positional error of a movement of each object from the current time step;

determining the new position of each object based on a transformation grid from the current time step and a previous residual part for each object from a previous time step;

determining a subsequent position of each object in a subsequent time step based on the recorded residual part for each object; and

detecting the objects based on a regression subnet comprising the ego-motion compensation module; and

control autonomous driving of the vehicle based on the plurality of properties of each object.

16. A vehicle comprising the system of claim 15 .

17. A non-transitory computer readable medium comprising instructions that, when executed, configure a plurality of computer hardware components to:

detect objects in a vicinity of a vehicle by:

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 that comprises a plurality of sequentially arranged layers, the single detection head being free from layers arranged in parallel;

determining a plurality of properties of each object based on an output of the single detection head;

carrying out, with an ego-motion compensation module, a nearest neighbor interpolation to determine a new position of each object in a current time step and avoiding drift due to an accumulation of positional errors in positions of the objects over time by recording a residual part indicating a positional error of a movement of each object from the current time step;

determining the new position of each object based on a transformation grid from the current time step and a previous residual part for each object from a previous time step;

determining a subsequent position of each object in a subsequent time step based on the recorded residual part for each object; and

detecting the objects based on a regression subnet comprising the ego-motion compensation module; and

control autonomous driving of the vehicle based on the plurality of properties of each object.

18. The system of claim 15 , wherein each of the features is connected to an input of the single detection head.

19. The system of claim 15 , wherein the single detection head is trained for the plurality of properties simultaneously.

Assignments (4)

Priority Claims (2)

Continuity (1)

References Cited (41)