METHOD AND APPARATUS FOR OBJECT DETECTION USING CONVOLUTIONAL NEURAL NETWORK SYSTEMS

1 . A radar system in an autonomous vehicle for object detection and classification, comprising:

an antenna module having a dynamically controllable metastructure antenna; and

a perception module, comprising:

a machine learning module trained on a first set of data and retrained on a second set of data to generate a set of perceived object locations and classifications; and

a classifier to use velocity information combined with the set of object locations and classifications to output a set of classified data.

2 . The radar system of claim 1 , wherein the dynamically controllable metastructure antenna is controlled by the perception module.

3 . The radar system of claim 1 , wherein the first set of data comprises acquired lidar data.

4 . The radar system of claim 1 , wherein the second set of data comprises radar data acquired by the radar system.

5 . The radar system of claim 1 , wherein the machine learning module comprises a convolutional neural network.

6 . The radar system of claim 1 , wherein the machine learning module is adjusted during training on the first set of data by comparing an output set to a first set of labeled data.

7 . The radar system of claim 1 , wherein the machine learning module is adjusted during training on the second set of data by comparing the set of perceived object locations and classifications to a second set of labeled data.

8 . An object detection and classification method, comprising:

configuring a first set of training data with corresponding labeled data;

training a machine learning module on the first set of training data to generate a first set of perceived object locations and classifications;

acquiring a second set of training data from a sensor;

configuring the second set of training data with corresponding labeled data;

modifying a format of the second set of training data to the format of the first set of training data by extracting a set of parameters from the second set of training data;

retraining the machine learning module on the second set of training data to generate a second set of perceived object locations and classifications;

combining the set of extracted parameters with the second set of perceived object locations and classifications to generate a combined data set; and

applying the combined data set to a classifier to output a set of classified data.

9 . The object detection and classification method of claim 8 , wherein the first set of training data comprises lidar data.

10 . The object detection and classification method of claim 8 , wherein the sensor comprises a radar and the second set of training data comprises radar data.

11 . The object detection and classification method of claim 8 , wherein the set of parameters comprises a set of velocity information.

12 . The object detection and classification method of claim 8 , wherein the machine learning module comprises a convolutional neural network.

13 . The object detection and classification method of claim 8 , wherein the format of the first set of training data comprises a range, an azimuthal angle, an elevation angle and an intensity.

14 . The object detection and classification method of claim 8 , wherein the format of the second set of training data comprises a range, an azimuthal angle, an elevation angle, a velocity and an intensity.

15 . An object detection and classification method, comprising:

acquiring radar data from a radar in an autonomous vehicle;

filtering velocity data from the radar data to generate a micro-doppler set and a reduced data set;

applying the reduced data set to a machine learning module to generate a set of perceived object locations and classifications;

combining the set of perceived object locations and classifications with the micro-doppler set to generate a combined data set; and

applying the combined data set to a classifier to generate a set of object locations and classifications.

16 . The object detection and classification method of claim 15 , wherein the micro-doppler set comprises a set of velocities.

17 . The object detection and classification method of claim 15 , wherein the reduced data set comprises a range, an azimuthal angle, an elevation angle and an intensity.

18 . The object detection and classification method of claim 15 , further comprising distinguishing stationary and moving objects in the set of object locations and classifications.

19 . The object detection and classification method of claim 18 , further comprising determining whether to perform an action in the autonomous vehicle based on the distinguishing stationary and moving objects in the set of object locations and classifications.

20 . The object detection and classification method of claim 19 , further comprising sending the set of object locations and classifications to a sensor fusion module in the autonomous vehicle.