Nauto, Inc.

Nauto Global Limited

Ravi Satzoda

Suchitra Sathyanarayana

Stefan Heck

Ludmila Levkova

Michael Gleeson-May

Benjamin O Alpert

Alex Thompson

Vivek Sekar

Ludmilla Levkova

A method for determining event data including: sampling a first data stream within a first time window at a first sensor of an onboard vehicle system coupled to a vehicle, extracting interior activity data from the first data stream; determining an interior event based on the interior activity data; sampling a second data stream within a second time window at a second sensor of the onboard vehicle system; extracting exterior activity data from the second image stream; determining an exterior event based on the exterior activity data; correlating the exterior event and the interior event to generate combined event data; automatically classifying the combined event data to generate an event label; and automatically labeling the first time window of the first data stream and the second time window of the second data stream with the combined event label to generate labeled event data.

System and method for contextualized vehicle operation determination

A method for determining distraction of a driver of a vehicle, including sampling sensor measurements at an onboard system of the vehicle; generating an output indicative of a distracted state; determining that the driver of the vehicle is characterized by the distracted state, based on the output; generating, at a second distraction detection module of a remote computing system, a second output indicative that the driver is characterized by the distracted state, based on the sensor measurements; computing a distraction score, at a scoring module of the remote computing system, in response to generating the second output and based on the sensor measurements and the distracted state.

System and method for driver distraction determination

The present embodiments relate to efficient localization of a vehicle within a lane region. An imaging device onboard the vehicle may capture an image stream depicting an environment surrounding the vehicle. An image may be inspected to identify pixel bands indicative of lane markings of a road depicted in the image. Based on the identified pixel bands, a lane region indicating a lane of a roadway can be extracted. A lane drift metric may be generated that indicates an offset of the vehicle relative to a center of the lane region. An output action may be initiated based on the offset indicated in the lane drift metric. The lane region can be translated from a first frame to a second frame providing a top-down perspective of the vehicle within the lane region using a transformation matrix to assist in efficiently deriving the lane drift metric.

System and method for vehicle localization

Systems and methods for driving. A driving data set for each of plurality of human-driven vehicles is determined. For each driving data set, exterior scene features of an exterior scene of the respective vehicle are extracted from the exterior image data. A driving response model is trained based on the exterior scene features and the vehicle control inputs from the selected driving data sets.

Method for determining driving policy

A method for near-collision detection, including determining a risk map for a vehicle and automatically detecting a near-collision event with an object based on vehicle behavior relative to the risk map.

Systems and methods for near-crash determination

A method for digital environment reconstruction, including: capturing data using an onboard vehicle system; determining a vehicle event and an event class of the vehicle event; extracting a set of agent parameters from the data; generating a virtual model based on the set of agent parameters; and providing the virtual model to a remote system.

System and method for digital environment reconstruction

A method for image analysis, including recording an image sequence at a vehicle system mounted to a vehicle; automatically detecting an object within the image sequence with a detection module; automatically defining a bounding box about the detected object within each image of the image sequence; modifying the image sequence with the bounding boxes for the detected object to generate a modified image sequence; at a verification module associated with the detection module, labeling the modified image sequence as comprising one of a false positive, a false negative, a true positive, and a true negative detected object based on the bounding box within at least one image of the modified image sequence; training the detection module with the label for the modified image sequence; and automatically detecting objects within a second image sequence recorded with the vehicle system with the trained detection module.

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Palo Alto, CA, United States of America

Ravi Satzoda