Atieva, Inc.

Sharath Avadhanam

Jinwoo Lee

Tor Aksel Notland Heirung

Sumukha Mysore Harish

Farhad A Goodarzi

Jinning Jiang

Kshitij Tukaram Kumbar

A method comprises: activating a full lane centering control mode in an advanced driver assistance system (ADAS) of a vehicle, wherein in the full lane centering control mode the ADAS keeps the vehicle centered on a trajectory of a lane regardless of surroundings of the vehicle and applies a first torque level against steering input that a driver applies using a steering wheel; detecting, in the full lane centering control mode, a driver input corresponding to a departure from the trajectory; and in response to the driver input, instead activating a manual lane biasing mode in which the ADAS applies a second torque level against the steering input, the second torque level lower than the first torque level, the manual lane biasing mode allowing the driver to steer away from the trajectory.

Manual lane biasing mode for ADAS

A computer-implemented method comprises: receiving first telemetry data generated by sensors of respective first vehicles in a fleet; clustering the first telemetry data into groups, each of the groups representing a profile of one or more first drivers of the first vehicles in the fleet; receiving second telemetry data generated by sensors of a second vehicle controlled by a second driver; associating the second driver with a first group of the groups by classifying the received second telemetry data; providing a subset of the first telemetry data corresponding to the first cluster as a baseline dataset for training of machine learning algorithms; generating baseline tuning parameter values using the trained machine learning algorithms; and providing the baseline tuning parameter values to a driver assistance system of a third vehicle controlled by the second driver.

Data driven customization of driver assistance system

A computer-implemented method comprising: receiving sensor data from sensors of a first vehicle associated with a first person, the sensor data generated by the sensors; applying a machine-learning algorithm to the sensor data to define a driving envelope for an assisted-driving system, the assisted-driving system installed in a second vehicle; and providing the driving envelope to the assisted-driving system installed in the second vehicle.

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Milpitas, CA, United States of America

Sharath Avadhanam