Gm Global Technolgoy Operations, Inc.

Arash Hashemi

SeyedAlireza Kasaiezadeh Mahabadi

Naser Mehrabi

Sresht Gurumoorthi Annadevara

Seyedeh Asal Nahidi

Ehsan Asadi

Seyedalireza Kasaiezadeh Mahabadi

Reza Hajiloo

Shamim Mashrouteh

Lapo Frascati

Nauman Sohani

A method for estimating a lateral velocity of a vehicle includes receiving sensor data from a sensor of the vehicle, determining a physics-based longitudinal velocity estimation of the vehicle using a physics-based model and the sensor data, determining a data-driven longitudinal velocity estimation of the vehicle using a first neural network and the sensor data, determining, using a second neural network, which of the physics-based longitudinal velocity estimation and the data-driven longitudinal velocity estimation is more reliable to determine a selected longitudinal velocity estimation, determining the lateral velocity of the vehicle using the selected longitudinal velocity estimation, and controlling the vehicle based on the lateral velocity.

Real-time reliability assessment method to enhance robustness of data-fusion based vehicle speed estimation

A method for determining, in real-time, an electronic limited-slip differential (eLSD) clutch torque includes receiving vehicle data in real-time, wherein the vehicle data includes a torque request, determining a preliminary eLSD clutch torque using a neural network and the vehicle data, determining clutch torque bounds of the eLSD using a physics-based model, determining whether the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD, adjusting the preliminary eLSD clutch torque using clutch torque bounds to determine a final clutch torque of the eLSD in response to determining that the preliminary eLSD clutch torque is outside the clutch torque bounds of the eLSD, and commanding, in real-time, the eLSD to apply the final clutch torque to a clutch of the eLSD.

Real-time machine learning and physics-based hybrid approach to perform eLSD torque estimation

A vehicle motion control system includes one or more input devices for generating one or more input signals associated with data indicative of a motion of the vehicle. The system further includes a computer, which has one or more processors. The computer further includes a non-transitory computer readable storage medium for storing instructions, such that the processor is programmed to compare a current tire state and a current tire prediction model to the data indicative of the motion of the vehicle. The processor is further programmed to calculate in real-time an adjusted tire state and an adjusted tire prediction model. The processor is further programmed to generate in real-time one or more actuation signals based on the adjusted tire state and the adjusted tire prediction model. The actuators in real-time adjust the motion of the vehicle in response to the actuator receiving the actuation signal from the processor.

Vehicle motion control system using real-time tire state correction

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Waterloo, Canada

Arash Hashemi