Method for Classifying a Tracked Object

A system comprising:

• a computer system for a vehicle including a plurality of radar sensors and a processor, the processor configured to execute a neural network to classify a tracked object in an environment of the vehicle by:
• capturing local radar detections from an object in the environment of the vehicle via the radar sensors;
• determining point features based on the local radar detections;
• determining tracker features based on the local radar detections;
• encoding the point features via point encoding layers of the neural network;
• encoding the tracker features via track encoding layers of the neural network;
• concatenating the encoded point features and the encoded tracker features to form a common tensor;
• passing the common tensor to a recurrent neural network which provides temporal fusion layers of the neural network to perform a temporal fusion of the encoded point features and the encoded tracker features via the temporal fusion layers;
• and
• classifying the tracked object based on the fused encoded point and tracker features via classifying layers of the neural network.

A non-transitory computer readable medium comprising instructions that when executed by a processor of a vehicle having a plurality of radar sensors, configure the processor to execute a neural network to track an object in an environment of the vehicle by:

• capturing local radar detections from an object in the environment of the vehicle via the radar sensors;
• determining point features based on the local radar detections;
• determining tracker features based on the local radar detections;
• encoding the point features via point encoding layers of the neural network;
• encoding the tracker features via track encoding layers of the neural network;
• concatenating the encoded point features and the encoded tracker features to form a common tensor;
• passing the common tensor to a recurrent neural network which provides temporal fusion layers of the neural network to perform a temporal fusion of the encoded point features and the encoded tracker features via the temporal fusion layers;
• and
• classifying the tracked object based on the fused encoded point and tracker features via classifying layers of the neural network.