Ford Global Technolgoies, LLC

Arpan Kusari

Hongtei Eric Tseng

Dimitar Petrov Filev

Shankar Narayan Mohan

Ryan Lee Baker

Christopher Cruise

Leda Daehler

Subramanya Nageshrao

A system includes a processor and memory storing instructions executable by the processor. The instructions include determining whether a currently-evaluated point is part of a segment. This may include: determining feature vectors for the currently-evaluated point and for each of a plurality of previously-evaluated points tagged to the segment; using the feature vectors to determine a probability score; and determine whether the score is greater than a threshold.

Distinguishing virtual objects from one another

A computing system can be programmed to determine a vehicle action based on vehicle sensor data input to a deep neural network (DNN) trained using an inverse reinforcement learning (IRL) system that includes a variational auto-encoder (VAE). The computing system can be further programmed to operate a vehicle based on the vehicle action.

Vehicle neural network processing

A computing system can determine a vehicle action based on inputting vehicle sensor data to a first neural network including a first safety agent that can determine a probability of unsafe vehicle operation. The first neural network can be adapted, at a plurality of times, by a periodically retrained deep reinforcement learning agent that includes a second deep neural network including a second safety agent. A vehicle can be operated based on the vehicle action.

Vehicle adaptive learning

One or more target areas are identified proximate to a moving vehicle. The vehicle can be maneuvered to a target area selected according to a reinforcement learning reward function.

Growing community of inventors

East Lansing, MI, United States of America

Arpan Kusari