Gm Global Technolgoy Operations, Inc.

The University of Waterloo

Seyedalireza Kasaiezadeh Mahabadi

Bakhtiar Brian Litkouhi

Naser Mehrabi

Hualin Tan

Seyedeh Asal Nahidi

Ehsan Asadi

Nikolai K Moshchuk

Reza Zarringhalam

Jin-Jae Chen

Reza Hajiloo

Hassan Askari

Mohammad Pournazeri

Ali Reza Armiyoon

Shih-Ken Chen

Mohammadali Shahriari

Amir Khajepour

James Heath Holbrook

Sayyed Rouhollah Jafari Tafti

Edward T Heil

Yubiao Zhang

Ibrahim A Badiru

Saurabh Kapoor

Ehsan Hashemi

Douglas J Spry

Khizar Ahmad Qureshi

Arash Hashemi

Gianmarc Coppola

Sresht Gurumoorthi Annadevara

Mustafa Hakan Turhan

Ramakrishna Kamath Vijayakumar

Ehsan Mohammadbagher

Nauman Sohani

Cody Dunham

A vehicle wheel force control system includes multiple vehicle wheel height sensors, a vehicle IMU configured to measure lateral and longitudinal acceleration, and a vehicle control module is configured to obtain vehicle wheel height parameters from the multiple vehicle wheel height sensors, obtain a vehicle lateral and longitudinal acceleration parameters from the vehicle IMU, estimate a pitch arm rotation value of the vehicle, determine a pitch center of the vehicle based on the pitch arm rotation value, the vehicle wheel height parameters and the vehicle lateral acceleration parameter and the vehicle longitudinal acceleration parameter, calculate a center of gravity of the vehicle according to the determined pitch center, determine an estimated normal force on at least one wheel of the vehicle based, at least in part, on the calculated center of gravity of the vehicle, and control operation of at least one vehicle component according to the estimated normal force.

Vehicle wheel force control systems

A method for downforce control includes receiving sensor data from sensors, using a feedforward control to determine a first requested normal force at the first axle of the vehicle and a second requested normal force at the second axle of the vehicle and the sensor data, using a feedback control to determine a first requested normal force adjustment at the first axle of the vehicle and a second requested normal force adjustment at the second axle of the vehicle using the sensor data, fusing the first requested normal force at the first axle of the vehicle with the first requested normal force adjustment to determine a first-adjusted normal force request at the first axle, and fusing the second requested normal force with the second requested normal force adjustment to determine a second-adjusted normal force request at the second axle.

Method and system for determining the desired tire grip in active downforce control

A vehicle steering control system including a road wheel angle sensor for detecting a road wheel angle, a steering feedback motor for rotating a steering wheel in response to the road wheel angle and for generating a steering torque value in response to a physical rotation of the steering wheel by a vehicle driver, a processor for receiving the road wheel angle and the steering torque value, for estimating a steering wheel angle in response to the steering torque value and for generating a validated steering angle in response to a difference between the road wheel angle and the steering wheel angle being less than a threshold value, and a steering controller for controlling the steering of a vehicle in response to the validated steering angle.

Steering wheel angle sensor estimation and validation system

A method for controlling a steer-by-wire system, comprising receiving vehicle data and a steering request from a vehicle, determining whether a steering road wheel actuator of the vehicle has failed using the vehicle data, in response to determining that the steering road wheel actuator of the vehicle has failed, determining a target wheel slip of the vehicle based on the steering request, maintaining the target wheel slip of the vehicle while the vehicle is in motion; and adjusting a wheel speed of at least one wheel of the vehicle based on a feedback signal, wherein the feedback signal is indicative of a road wheel angle while the vehicle is in motion.

Architecture and method for steer-by-wire failure backup using direct yaw control actuators

A method for estimating lateral force includes receiving vehicle data. The vehicle includes a plurality of tires. The method further includes using a bicycle model to determine first lateral forces at each of the plurality of tires of the vehicle, using a double-track model to determine second lateral forces at each of the plurality of tires of the vehicle, fusing the first lateral forces determined using the bicycle model and the second lateral forces using the double-track model to determine third lateral forces at each of the plurality of tires of the vehicle, and controlling an actuator of the vehicle using the third lateral forces at each of the plurality of tires of the vehicle.

Integrated tire longitudinal and lateral force estimation methodology

A method for downforce control includes receiving a plurality of vehicle inputs from a vehicle. The plurality of vehicle inputs includes sensor data from a plurality of sensors of the vehicle. The method further includes determining a downforce acting on the vehicle, by: (a) determining a predicted half-car model uncertainties using a neural network; and (b) determining a front normal force at the front axle and a rear normal force at the rear axle using the vehicle inputs, the predicted half-car model uncertainties, and a half-car model. The method further includes determining a first position of the first aerodynamic body relative to the vehicle body and a second position of the second aerodynamic body relative to the vehicle body based on the front normal force at the front axle and a rear normal force at the rear axle, respectively.

Method and system for downforce estimation using a half-car model with a data driven uncertainties estimator

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 probabilistic autonomous vehicle control includes receiving a plurality of unfiltered sensor signals from a sensor system of a vehicle and determining, in real time, a failure probability of the sensor system using the unfiltered sensor signals. The method further includes determining a failure probability of a plurality of estimation signals at each time step using the failure probability of the sensor system and determining a failure probability of a plurality of Advanced Driver Assistance System (ADAS) subfunction commands using the failure probability of the plurality of estimation signals at each time step. Further, the method includes determining remedial actions for the plurality of ADAS subfunction commands based on the failure probability of the plurality of ADAS subfunction commands.

Probabilisic system and method for autonomous vehicle control

A system for managing vehicle body and wheel motion control with a dual clutch differential includes sensors and actuators disposed on the vehicle, the sensors measuring real-time static and dynamic data and the actuators altering static and dynamic behavior of the motor vehicle. A control module executes program code portions stored in memory. The program code portions receive the real-time static and dynamic data; selectively prioritize torque output from a prime mover of the vehicle through the differential to driven wheels of the vehicle to control a body and the driven wheels; model and estimate clutch torque for each clutch of the dual clutch differential; model and estimate a joint clutch torque, a tire force, and corner torque; and generate a torque output for each clutch of the dual clutch differential that is selected to maintain one or more of body control, wheel control, and stability of the motor vehicle.

Management of body and wheel motion controls with dual clutch differential

A method is provided for autonomously operating a vehicle. The method includes receiving, at a processor, at least vehicle state data and vehicle object environment data; generating, with the processor, an optimal path for the vehicle with a cost function based on the vehicle state data and the vehicle object environment data; identifying, with the processor, at least one critical condition constraint based on at least one of the vehicle or vehicle environment; modifying, with the processor, at least a first portion of the optimal path based on the at least one critical condition constraint to result in a short-range trajectory portion; generating a resulting trajectory with the short-range trajectory portion; and implementing the resulting trajectory on the vehicle.

Autonomous vehicle driving systems and methods for critical conditions

A combined slip based driver command interpreter for a vehicle is provided which may be communicatively coupled to a steering wheel angle sensor, an acceleration pedal position sensor and a brake pedal position sensor, the combined slip based driver command interpreter including, but not limited to a memory configured to store a non-linear combined lateral slip model and a non-linear combined longitudinal slip model, and a processor, the processor configured to determine a driver's intended vehicle lateral velocity and a driver's intended vehicle yaw rate based upon the angle of the steering wheel, the position of the acceleration pedal, the position of the brake pedal, a longitudinal velocity of the vehicle, the non-linear combined lateral slip model and the non-linear combined longitudinal slip model.

Combined slip-based driver command interpreter

Methods and systems are provided for determining vehicle spin-out conditions including conditions indicative of a vehicle spin-out ahead of the vehicle actually spinning-out. The methods and systems receive motion parameters of a vehicle based on sensed signals from at least one vehicle sensor of an electronic power steering system and an inertial measurement unit. The method and systems estimate pneumatic trail based on a rate of change of self-aligning torque with respect to axle lateral force. The methods and systems determine vehicle spin-out conditions based on the estimated pneumatic trail. The methods and systems control at least one feature of a vehicle in response to the determined vehicle spin-out conditions.

Growing community of inventors

Shelby Township, MI, United States of America

Seyedalireza Kasaiezadeh Mahabadi