Gm Global Technolgoy Operations, Inc.

Hojjat Izadi

Fahim Javid

Carlos E Arreaza

Wei Tong

Joseph K Moore

Alaa M Khamis

Brian K Saylor

Robert P Marble

Robert G Izak

Michael D Alarcon

Steffen P Lindenthal

Larry G Gepfrey

Martin Juhas

Michael W Neal

Jonathan P Rossiter

Johnson Qu

Jonathan From

Mohammad Hossein Beheshti

Mahdokht Ezati

Mohammad-Amin Rajaie

Methods and systems for controlling movement of e-pallets using multi-modal interaction with a user are provided that include: one or more first sensors configured to obtain first sensor data of a first modality pertaining to a command from the user for movement of the e-pallets; one or more second sensors configured to obtain second sensor data of a second modality, different from the first modality, pertaining to the command; and a processor that is coupled to the one or more first sensors and the one or more second sensors, the processor configured to at least facilitate: determining an intended movement of the e-pallets based on both the first sensor data and the second sensor data; and moving the e-pallets in executing the intended movement, in accordance with instructions provided by the processor to actuators of the e-pallets.

Fiadaptive multi-modal e-pallet control

Methods and systems for controlling movement of an e-pallet includes one or more sensors configured to obtain sensor data as to a user of the e-pallet, a second e-pallet, or both; and a processor coupled to the one or more sensors and configured to at least facilitate: determining, using the sensor data, a relative position of the user, the second e-pallet or both, with respect to the e-pallet; determining, using the sensor data, a relative orientation of the user, the second e-pallet or both, with respect to the e-pallet; and taking a control action for the e-pallet, in accordance with instructions provided by the processor, based on both the relative position and the relative orientation.

Virtual e-pallet interface

An electronic pallet (e-pallet) includes a superstructure mounted to a wheeled base platform. A tether device defines an articulation angle with respect to a leading edge of the superstructure, and is grasped by an operator towing the e-pallet. A motor connected to driven road wheels transmits a drive torque to the road wheels responsive to motor control signals, including a desired yaw rate and ground speed. A speed sensor, angle sensor, and length sensor are respectively configured to determine an actual ground speed of the e-pallet, the articulation angle, and a length of the tether device. An electronic controller, in response to the input signals, generates the motor control signals using proportional-integral-derivative (PID) control logic. Coupled lateral and longitudinal dynamics control loops respectively determine the desired yaw rate and ground speed to accommodate for motion of the operator.

Dynamic control of human-tethered e-pallet

A method and apparatus that detect wheel misalignment are provided. The method includes predicting a self-aligning torque parameter based on a regression model determined from a dataset including one or more from among a steering wheel angle parameter, a speed parameter, a torsion bar torque parameter, a lateral acceleration parameter, and a power steering torque parameter, comparing a measured self-aligning torque parameter and the predicted self-aligning torque parameter, and outputting a wheel alignment condition indicating whether the wheel alignment is proper if the self-aligning torque parameter and the predicted self-aligning torque parameter are within a predetermined value based on the comparing.

Apparatus and method that detect wheel alignment condition

Systems and methods for reversing a semi-autonomous follower vehicle involve obtaining a speed of a leader vehicle in front of and unattached to the follower vehicle that is reversing. A method includes estimating a path of the leader vehicle and determining a path for the follower vehicle based on the path of the leader vehicle. A longitudinal movement of the follower vehicle is controlled based on the speed of the leader vehicle and a lateral movement of the follower vehicle is controlled based on the path for the follower vehicle.

Semi-autonomous reversing of a follower vehicle

A method of controlling relative roll torque in vehicles having a front active sway bar and a rear active sway bar is provided. The front active sway bar varies roll torque of a front axle and the rear active sway bar varies roll torque of a rear axle. The method includes monitoring dynamic driving conditions during operation of the vehicle and biasing tire lateral load transfer distribution (TLLTD) relative to the front axle based on the monitored dynamic driving conditions. Positive bias of the TLLTD increases the portion of a total roll torque carried by the front active sway bar. Biasing TLLTD occurs during one or more dynamic bias events triggered as monitored dynamic driving conditions exceed one or more calibrated thresholds.

Variable tire lateral load transfer distribution

An apparatus and method that detect a wheel alignment condition are provided. The method includes receiving a dataset comprising one or more from among a steering wheel angle parameter, a speed parameter, a lateral acceleration parameter, a self-aligning torque parameter and a power steering torque parameter, normalizing the received dataset, analyzing the normalized dataset according to a model for determining a wheel alignment condition, and outputting a value indicating whether the wheel alignment is within a predetermined value based on the model.

Examples of techniques for vehicle suspension system alignment monitoring are disclosed. In one example implementation, a method includes receiving vehicle data and environmental data including, inertial measurement unit (IMU) acceleration data and global positioning system (GPS) velocity data from a GPS associated with the vehicle, and steering wheel angle data, driver applied torque data, and electronic power steering (EPS) applied torque data associated with the steering system. The method further includes mitigating for at least one of a vehicle effect and an environmental effect based on the vehicle data and environmental data. The method further includes detecting a misalignment based at least in part on one or more of the IMU acceleration data, the GPS velocity data, acceleration data, a steering wheel angle, and a self-aligning torque. The method further includes reporting the misalignment of the vehicle based at least in part on detecting the misalignment.

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Toronto, Canada

Hojjat Izadi