Abstract: A platoon of electric pallets (e-pallets) includes a follower e-pallet connected to or in wireless communication with a leader e-pallet. The platoon also includes a sensor suite, road wheels, an electric powertrain system, and a local controller. The sensor suite includes a velocity sensor configured to measure a velocity of the follower e-pallet, an angle sensor configured to measure an azimuth angle between the follower and leader e-pallets, and a length or distance sensor configured to measure a distance therebetween. The local controller executes a method to adaptively move a variable target point (VTP) on the leader pallet in response to the velocity, the azimuth angle, and the length, and to thereafter control a dynamic output state of the electric powertrain system using the VTP.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A towing configuration includes a tow vehicle and a trailer. Trailer tracking is controlled to a path of travel by an active rear steering system on the tow vehicle. The path of travel may correspond to a path traversed by the tow vehicle.

Abstract: 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.

Abstract: Systems and methods to park a semi-autonomous follower vehicle involve performing path planning to determine a path from a current location of the follower vehicle to a parking space, and controlling longitudinal movement of the follower vehicle using an accelerator control mechanism and a brake control mechanism operated by a driver of a leader vehicle that is not physically coupled to the follower vehicle. The accelerator control mechanism includes a pedal, knob, or lever and the brake control mechanism includes a pedal, knob, or lever. Lateral movement of the follower vehicle is controlled in order to follow the path to the parking space.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.