Gm Global Technolgoy Operations, Inc.

Hassan Askari

Mohammadali Shahriari

SeyedAlireza Kasaiezadeh Mahabadi

Reza Zarringhalam

Saurabh Kapoor

Seyedeh Asal Nahidi

Naser Mehrabi

Ehsan Asadi

Seyedalireza Kasaiezadeh Mahabadi

Shamim Mashrouteh

Khizar Ahmad Qureshi

Ami Woo

Mansour Ataei

Amir Abolhassani

Hualin Tan

Ruixing Long

Ashraf Abualfellat

Bharath Pattipati

Rakesh Kumar

Mehdi Abroshan

Sresht Gurumoorthi Annadevara

Lapo Frascati

Puneet Bagga

Mustafa Hakan Turhan

Vaibhav J Lawand

Nauman Sohani

Daniel James Cornelius

Ricky Lin

Matan Yeger

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

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

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

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 data health monitoring and recording system for a vehicle includes a data recording infrastructure and one or more controllers. The one or more controllers include a multi-layer control software architecture including two or more software layers. The one or more controllers execute instructions to monitor, by a health monitoring structure that is part of the multi-layer control software architecture, one or more parameters calculated by each of the two or more software layers. The one or more controllers compare a respective parameter of a respective software layer with a predetermined corresponding performance metric for a required time horizon. In response to determining the respective parameter of the respective software layer does not meet the predetermined corresponding performance metric for the required time horizon, a trigger that instructs the data recording infrastructure to record the parameters calculated by each of the two or more software layers is generated.

Vehicle data health monitoring and recording system for multi-layer control software architecture

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

A fault remediation system for a vehicle includes one or more controllers in electronic communication with one or more consumed interfaces and one or more provided interfaces. The one or more controllers execute instructions to receive, from the one or more consumed interfaces, a consumed signal and perform fault detection upon the consumed signal to determine the presence of an active fault within the consumed signal. In response to detecting an active fault with the consumed signal, the one or more controllers select a remediation state from a group of two or more prospective remediation states based on a significance analysis of the consumed signal. The one or more controllers evaluate a relevant subfunction that corresponds to the consumed signal that the remediation state addresses for the presence of remediation tolerance and generates arbitration instructions based on the remediation tolerance.

Fault remediation system for a vehicle

Systems and methods of alerting an occupant of a vehicle to an obstacle to a side of the vehicle. The systems and methods include receiving turn indicating data from at least one vehicle system, receiving perception data from a perception system of the vehicle, predicting a vehicle turn based on the turn predicting data, generating an obstacle alert, which is output through an output device of the vehicle, when an obstacle is detected within a first sized detection zone or a second sized detection zone using the perception data, and switching from using the first sized detection zone to using the second sized detection zone for generating the obstacle alert in response to predicting the vehicle turn. The second sized detection zone is extended as compared to the first sized detection zone.

Systems and methods for generating vehicle alerts

A system for adaptive tire force prediction in a motor vehicle includes a control module that executes program code portions that receive real-time static and dynamic data from motor vehicle sensors, that model forces at each tire of the motor vehicle at one or more incremental time steps, that estimate actual forces at each tire of the motor vehicle at each of the one or more incremental time steps, that adaptively predict tire forces at each tire of the motor vehicle at each of the one or more incremental time steps, that generate one or more control commands for actuators of the motor vehicle, that capture discrepancies between real-time force estimations and nominal force calculations at each tire of the motor vehicle, and that apply compensation parameters to reduce tracking errors in the one or more control commands to the one or more actuators of the motor vehicle.

Architecture and methodology of adaptive tire force prediction for integrated vehicle motion control

A data analytics system for a vehicle includes one or more controllers having a multi-layered control software architecture including two or more software layers and a health monitoring structure. The one or more controllers execute instructions to receive a fault, and in response to receiving the fault, generate, by the health monitoring structure, a plurality of unique triggers as the fault propagates throughout the multi-layered control software architecture. The controllers build a network including a plurality of propagation paths and identify one or more main propagation paths that are part of the network based on a data analysis of a mock big data set. The one or more controllers build a complete propagation pathway including two or more main propagation paths. In response to determining only one main propagation path, the controllers determine a root cause of the fault based on the only one main propagation path.

Vehicle data analytics system for multi-layer control software architecture

Growing community of inventors

Markham, Canada

Hassan Askari