Gm Global Technolgoy Operations, Inc.

Zhi Li

Reza Zarringhalam

Mohammadali Shahriari

Jackson Barry McGrory

Mehdi Abroshan

Paul A Adam

Brian M Porto

Ashraf Abualfellat

Klaus Trangbaek

Amir Takhmar

Tetyana V Mamchuk

Jimmy Lu

Mohammed Raju Hossain

Patrick Giancarlo Gabriel Digioacchino

Kin Man Michael Wong

Puneet Bagga

An adaptive method for controlling the steering of an autonomous vehicle includes receiving a steering input. The steering input is indicative of a desired road wheel angle. The method also includes determining a commanded torque to achieve the desired road wheel angle. The method also includes measuring the road wheel angle of the vehicle (i.e., the measured road wheel angle). The method also includes executing a feedback control to determine a torque overlay. The feedback control includes determining, in real time, a current steering error vector of the vehicle. The feedback control includes determining the difference between the current steering error vector and a predetermined-steering error dynamics vector. The feedback control also includes determining a feedback gain correction vector. The feedback gain correction vector is a function of the difference between the current steering error vector and the predetermined-steering error dynamics vector.

Model-reference adaptive steering feedback control to reduce unforeseen oscillations in automated driving

Methods and systems of controlling a vehicle. The methods and systems include switching to a second processor executed mode in response to determining that a range of perception information extends to a range end point that is closer to the vehicle than a first Look Ahead (LA) point. In the second processor executed mode: a motion planner requests a lateral controller to provide a closer LA point relative to the vehicle. The lateral controller determines the closer LA point and sends the closer LA point to the motion planner. The lateral controller generates control commands for an EPS system based on an error between a desired trajectory and an actual trajectory at the closer LA point. The motion planner generates the desired trajectory based on perception data and the closer LA point.

Methods and systems for vehicle control under degraded lane perception range

In accordance with an exemplary embodiment, methods and systems are provided for controlling steering of an autonomous vehicle. The method includes: operating, by a processor, the autonomous vehicle in a semi-automated mode; receiving, by the processor, driver input including a measured driver torque; receiving, by the processor, threat data; determining, by the processor, a steering command bias based on an impedance relation, impedance parameters, the measured driver torque, and the threat data; determining, by the processor, a reference angle based on the steering command bias and a desired angle; and generating, by the processor, control data to control the steering of the autonomous vehicle based on the reference angle.

Methods and systems for adaptive blending of driver and automated steering commands under external threat

A vehicle parameter estimation module is configured to generate a first estimate of a vehicle parameter based on operating conditions of a vehicle measured or estimated at a first time. The vehicle parameter includes at least one of a tire cornering stiffness of the vehicle and an understeer coefficient of the vehicle. The vehicle parameter estimation module is also configured to determine an error value based on the first estimate of the vehicle parameter and values of the vehicle operating conditions measured or estimated at a second time that is later than the first time. The vehicle parameter estimation module is further configured to generate a second estimate of the vehicle parameter based on the first estimate of the vehicle parameter and the error value. A vehicle actuator control module is configured to control an actuator of the vehicle based on the second estimate of the vehicle parameter.

System and method for performing advanced driver-assistance functions including adaptively accounting for impact of different payload or trailer configurations

A system to enable an automobile vehicle automated driving control includes a quality index of an automated driving control system prior to enablement of an automated driving control function of an automobile vehicle. An adaptive forward propagation horizon and a prediction horizon for assessment of the quality index are computed. Vehicle states and road geometry are propagated over the adaptive forward propagation horizon, and the quality index is assessed based on forward propagated states over the prediction horizon. A first signal permits actuation of the automated driving control function of the automobile vehicle and a second signal precludes actuation of the automated driving control function. One of the first signal or the second signal is elected based on the results of assessing a quality control index trajectory over the adaptive forward propagation horizon.

Quality index and real-time forward propagation of virtual controls for smart enablement of automated driving

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Markham, Canada

Zhi Li