Gm Global Technolgoy Operations, Inc.

Mohammadali Shahriari

Reza Zarringhalam

Ashraf Abualfellat

Brian M Porto

Amir Takhmar

Hassan Askari

Mehdi Abroshan

Tushita Arun Sikder

Mohammed Raju Hossain

Jackson Barry McGrory

Khizar Ahmad Qureshi

Paul A Adam

Klaus Trangbaek

Zhi Li

Ami Woo

Vaibhav J Lawand

Lance G Lamson

Tara Craigen

Paul G Otanez

Kevin Austin O'Dea

Avshalom Suissa

Amanpal S Grewal

Seyedalireza Kasaiezadeh Mahabadi

Tetyana V Mamchuk

Jimmy Lu

Namal P Kumara

Apral S Hara

Milad Jalaliyazdi

Rakesh Kumar

Jayant Sachdev

Haley M Dalzell

Patrick Giancarlo Gabriel Digioacchino

Dorothy Lui

Puneet Bagga

Henry Warren Grasman

Amirreza Mirbeygi Moghaddam

Michelle Zirngibl Taylor

Donovan J Wisner

Benjamin J Gaffney

Klaus Tranbaek

Ricky Lin

Emily Frances Wolfangel

Ryan A MacDonald

Patrick Francis Pirrmann

Michael B Peppard

Daniel James Cornelius

Dhaval Kavindra Sompura

Yongkyun Shin

Ravichandran Manogaran

Sadaf Mohammadi

Christopher Ratliff

Jagannadha Reddy Satti

Matan Yeger

Wenhao Wu

A vehicle includes a plurality of sensors and one or more controllers collectively programmed to execute the following instructions: estimate odometry of the vehicle based on sensor data from the plurality of sensors, to produce a plurality of odometry estimates; monitor integrity of one or more of the sensors; correct sensor data from one or more of the plurality of sensors to provide corrected sensor data; compensate the corrected sensor data to provide compensated data; and use the compensated data to provide at least one of vehicle odometry, vehicle states, and time stamp information.

Robust odometry architecture and methodology

Methods and systems are provided for a vehicle towing a trailer. In one embodiment, a method includes: receiving, by a processor, sensor data from one or more sensors of the vehicle; estimating, by a processor, a steering torque value based on the sensor data; estimating, by the processor, a steering angle value based on the sensor data; determining, by the processor, a trailer tongue value based on the steering torque value and the steering angle value; and generating, by the processor, a control signal to control the vehicle based on the trailer tongue value.

Methods and systems for estimating trailer tongue values

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

A system and method are provided for controlling a vehicle. In one embodiment, a system includes: a sensor system configured to generate sensor data sensed from an environment of the vehicle; and a control module configured to, by a processor, based on the sensor data, identify a gap in a lane line on a roadway in front of the vehicle, determine at least two points within the gap, determine a curve between the at least two points, compute a correlation measure based on the curve, generate lane stitching data based on data based on the curve and an evaluation of the correlation measure, and controlling one or more components of the vehicle based on the stitching data.

Lane stitching methods and systems for robust automated driving control

A computer-implemented method that, when executed by data processing hardware of a vehicle, causes the data processing hardware to perform operations is provided. The operations include gathering sensor data, calculating one or more groove detection confidence values with the sensor data using one or more groove detection modules, evaluating the one or more groove detection confidence values using a road groove detection fusion module, determining whether a road groove is detected, communicating road groove data to an advanced driver-assistance system, and adapting trajectory and steering controls of the vehicle.

System and method for road groove detection and control in automated driving

A method controlling a vehicle includes detecting that an object is along a path of the vehicle and within a predetermined distance from the vehicle, in response to detecting the object that is along the path of the vehicle and within the predetermined distance from the vehicle, determining, in real time, a coefficient of friction between a road and a tire of the vehicle, and in respond to determining the coefficient of friction between the road and the tire of the vehicle, commanding the vehicle to perform a control action.

Method and system for friction-aware impact avoidance

Vehicles and related systems and methods are provided for predicting a future maneuver expected to be executed by a driver. One method involves determining a combined probability for a potential maneuver based on constituent probabilities corresponding to different vehicle state variables, and environmental conditions using one or more tuning parameters specific to the respective combination of maneuver and vehicle state variable, determining an adjusted probability for the potential maneuver based at least in part on the combined probability and one or more contextual state variables corresponding to a current operating context for the vehicle, adapted to driver behavior and driving style, identifying the potential maneuver as an expected maneuver to be executed by a driver of the vehicle based on the adjusted probability, and automatically initiating one or more actions in a manner that is influenced by the expected maneuver.

Vehicle systems and methods for probabilistic maneuver prediction

Methods and systems are provided for a vehicle. In one embodiment, a method includes: initiating, by a processor, steering excitation according to a pattern while the vehicle is stationary; learning, by the processor, a load associated with a front axle of the vehicle based on the steering excitation; translating, by the processor, the load associated with the front axle of the vehicle to a load associated with a tongue of a trailer, and generating, by the processor, a trailer tongue load value based on the load associated with the tongue of the trailer.

Methods and systems for estimating trailer tongue load

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 and method for a vehicle control adaptation to a crosswind and a wind gust including a controller situated within a vehicle to receive wind data inputs. The wind data inputs are based on a location of the vehicle and include a sustained wind velocity, a sustained wind direction, and a wind gust level. The controller further receives vehicle measurement data from one or more sensors situated within the vehicle and determines a wind impact on the vehicle based on the wind data inputs and the vehicle measurement data. The controller further compensates for the wind impact by a feedforward control in response to a low bandwidth component of the wind data inputs and by a feedback control in response to a high bandwidth component of the wind data inputs. The controller generates a feedback and/or a control based on the wind impact.

Vehicle control adaptation to sustained wind levels and gusts

Methods and systems are provided for estimating vehicle parameters of a vehicle. A maximum desired vehicle acceleration and a minimum excitation acceleration in a first direction are determined. The minimum excitation acceleration is associated with generating an estimated vehicle parameter. A first control input for transmission to an advanced driver assistance system (ADAS) to apply a temporary excitation acceleration that is greater than the minimum excitation acceleration and less than the maximum desired vehicle acceleration is generated. The estimated vehicle parameter is generated using a vehicle parameter estimation observation model based on vehicle data received from at least one vehicle sensor. The vehicle data is generated in response to the application of the temporary excitation acceleration to the vehicle. The estimated vehicle parameter is transmitted to the ADAS of the vehicle for replacement of a previous vehicle parameter at the ADAS with the estimated vehicle parameter.

Systems and methods for estimating vehicle parameters

Methods and systems are provided that include one or more sensors configured to obtain sensor data pertaining to both a driver of a vehicle and an environment surrounding the vehicle; and a processor that is coupled to the one or more sensors and that is configured to at least facilitate determining, using the sensor data, when the driver is incapacitated; determining, using the sensor data, when a threat is detected for the vehicle; and taking action to avoid the threat when the driver is incapacitated and the threat is detected, via instructions provided by the processor.

Optimal engagement of automated features to assist incapacited drivers

Methods and systems are provided for determining and mitigating vehicle pull force from passing other vehicles. In an exemplary embodiment, methods and systems are provided that include: obtaining sensor data via one or more sensors of a vehicle, the sensor data including both perception sensor data and vehicle dynamics sensor data; identifying, via a processor, one or more additional vehicles to be passed by the vehicle, using the perception sensor data; and predicting and determining, via the processor, a pull force for the vehicle that is caused by the passing of the vehicle by the one or more additional vehicles along with an impact of the pull force on the vehicle, based on both the perception sensor data and the vehicle dynamics sensor data, and control the vehicle to proactively mitigate the pull force on the vehicle; and vehicle with trailer when towing.

Identification and mitigation control of pull force impacts when passing large vehicles in automated driving

Automated driver assistance systems and methods for towing predict vehicle/trailer instabilities and adapt automated driving control to avoid them. A tow-vehicle includes a controller that, through an actuator system, controls speed and/or steering. A map system and/or a sensor system monitor a roadway on which the vehicle is travelling to identify a road profile located ahead of the vehicle over a prediction horizon. A projected trajectory for navigating the vehicle through the road profile over the prediction horizon and considering environmental conditions is determined. Before travel over the road profile, whether the projected trajectory through the road profile will result in exceeding a vehicle dynamic threshold is determined. When the projected trajectory will result in exceeding the vehicle dynamic threshold through the road profile, a control action is determined to prevent instability and optimize driver experience. The vehicle is operated through the road profile using the control action.

Tow-vehicle driving methods and systems for automated driving

Methods and systems are provided for controlling a vehicle comprising an Electric Power Steering System (EPS). In an embodiment, a method includes learning, by a processor, a non-linearity of a boost factor associated with the EPS; determining, by the processor, a deboost factor based on an inverse relationship of the learned non-linearity of the boost factor associated with the EPS; and generating, by the processor, a steering command to the EPS based on the deboost factor.

Deboost methods and systems for active safety steering control

Growing community of inventors

Markham, Canada

Mohammadali Shahriari