Gm Global Technolgoy Operations, Inc.

Ami Woo

Mohammadali Shahriari

Hassan Askari

Reza Zarringhalam

Mehdi Abroshan

Tushita Arun Sikder

Khizar Ahmad Qureshi

Amirreza Mirbeygi Moghaddam

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

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

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

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Calgary, Canada

Ami Woo