Gm Global Technolgoy Operations, Inc.

Khizar Ahmad Qureshi

Mohammadali Shahriari

Hassan Askari

Reza Zarringhalam

Mehdi Abroshan

Ami Woo

Seyedalireza Kasaiezadeh Mahabadi

Apral S Hara

Puneet Bagga

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

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

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.

Growing community of inventors

Pickering, Canada

Khizar Ahmad Qureshi