Nvidia Corporation

Jason Conrad Roche

Niranjan Avadhanam

Sumit Bhattacharya

Atousa Torabi

Sumit Kumar Bhattacharya

Yuzhuo Ren

Sakthivel Sivaraman

Nishant Puri

Shubhadeep Das

Systems and methods are disclosed herein for implementation of a vehicle command operation system that may use multi-modal technology to authenticate an occupant of the vehicle to authorize a command and receive natural language commands for vehicular operations. The system may utilize sensors to receive data indicative of a voice command from an occupant of the vehicle. The system may receive second sensor data to aid in the determination of the corresponding vehicular operation in response to the received command. The system may retrieve authentication data for the occupants of the vehicle. The system authenticates the occupant to authorize a vehicular operation command using a neural network based on at least one of the first sensor data, the second sensor data, and the authentication data. Responsive to the authentication, the system may authorize the operation to be performed in the vehicle based on the vehicular operation command.

Systems and methods for performing commands in a vehicle using speech and image recognition

Interactions with virtual systems may be difficult when users inadvertently fail to provide sufficient information to proceed with their requests. Certain types of inputs, such as auditory inputs, may lack sufficient information to properly provide a response to the user. Additional information, such as image data, may enable user gestures or poses to supplement the auditory inputs to enable response generation without requesting additional information from users.

Multi-modal sensor fusion for content identification in applications of human-machine interfaces

In various examples, systems and methods are disclosed herein for a vehicle command operation system that may use technology across multiple modalities to cause vehicular operations to be performed in response to determining a focal point based on a gaze of an occupant. The system may utilize sensors to receive first data indicative of an eye gaze of an occupant of the vehicle. The system may utilize sensors to receive second data indicative of other data from the occupant. The system may then calculate a gaze vector based on the data indicative of the eye gaze of the occupant. The system may determine a focal point based on the gaze vector. In response to determining the focal point, the system causes an operation to be performed in the vehicle based on the second data.

Systems and methods for performing operations in a vehicle using gaze detection

Systems and methods are disclosed herein for a pedestrian crossing warning system that may use multi-modal technology to determine attributes of a person and provide a warning to the person in response to a calculated risk level to effect a reduction of the risk level. The system may utilize sensors to receive data indicative of a trajectory of a person external to the vehicle. Specific attributes of the person such as age or walking aids may be determined. Based on the trajectory data and the specific attributes, a risk level may be determined by the system using a machine learning model. The system may cause emission of a warning to the person in response to the risk level.

Growing community of inventors

Santa Clara, CA, United States of America

Jason Conrad Roche