Nauto, Inc.

Nauto Global Limited

Stefan Heck

Suchitra Sathyanarayana

Ravi Satzoda

Benjamin O Alpert

Charles Hornbaker

Ludmila Levkova

Alex Thompson

Tahmida Binte Mahmud

Ilan Hornstein

Shobana Sankaran

Michael Gleeson-May

Vivek Sekar

Frederick Soo

Piyush Chandra

Alexander Dion Wu

Mohammad Saad Billah

Jeremy Marschke

Gary Kwan

Ruslan Belkin

Rohit Saboo

Ludmilla Levkova

Ravi Kumar Satzoda

Suchitra Sathynarayana

A method for determining an adverse vehicle event, including: sampling sensor data an onboard vehicle system coupled to an ego-vehicle; at the onboard vehicle system, extracting a set of event parameters from the sensor data, wherein the vehicle event data is associated with a vehicle event occurring within the time interval; computing a loss probability based on the set of event parameters in response to the loss probability exceeding a threshold probability, transforming the set of event parameters into insurance claim data; and automatically transmitting the insurance claim data to an endpoint, wherein the endpoint is determined based on the participant identifier.

System and method for adverse vehicle event determination

An apparatus includes: a first sensor configured to provide a first input; a second sensor configured to provide a second input; and a processing unit configured to receive the first input from the first sensor, and a second input from the second sensor; wherein the processing unit is configured to determine a first probability of a first predicted event, and a second probability of a second predicted event, wherein the first predicted event and the second predicted event are associated with an operation of the vehicle; and wherein the processing unit is configured to calculate a risk score based on the first probability of the first predicted event, and based on the second probability of the second predicted event.

Devices and methods for assisting operation of vehicles based on situational assessment fusing expoential risks (safer)

An apparatus includes: a first camera configured to view an environment outside a vehicle; a second camera configured to view a driver of the vehicle; and a processing unit configured to receive a first image from the first camera, and a second image from the second camera; wherein the processing unit is configured to determine first information indicating a risk of intersection violation based at least partly on the first image; wherein the processing unit is configured to determine second information indicating a state of the driver based at least partly on the second image; and wherein the processing unit is configured to determine whether to provide a control signal for operating a device or not based on (1) the first information indicating the intersection violation, and (2) the second information indicating the state of the driver.

Devices and methods for predicting intersection violations and/or collisions

An apparatus includes: a first sensor configured to provide a first input associated with an environment outside a vehicle; a second sensor configured to provide a second input associated with an operation of the vehicle; and a processing unit having a first-stage processing system and a second-stage processing system; the first-stage processing system configured to receive the first input and the second input, process the first input to obtain a first time series of information, and process the second input to obtain a second time series of information; wherein the second-stage processing system comprises a neural network model configured to receive the first time series of information and a second time series of information in parallel; and wherein the neural network is configured to process the first time series and the second time series to determine a probability of a predicted event associated with an operation of the vehicle.

Devices and methods for assisting operation of vehicles based on situational assessment fusing expoential risks (SAFER)

An apparatus includes: a first camera configured to view an environment outside a vehicle; a second camera configured to view a driver of the vehicle; and a processing unit configured to receive a first image from the first camera, and a second image from the second camera; wherein the processing unit is configured to determine first information indicating a risk of collision with the vehicle based at least partly on the first image; wherein the processing unit is configured to determine second information indicating a state of the driver based at least partly on the second image; and wherein the processing unit is configured to determine whether to provide a control signal for operating a device or not based on (1) the first information indicating the risk of collision with the vehicle, and (2) the second information indicating the state of the driver.

Devices and methods for predicting collisions and/or intersection violations

A method for driver identification including recording a first image of a vehicle driver; extracting a set of values for a set of facial features of the vehicle driver from the first image; determining a filtering parameter; selecting a cluster of driver identifiers from a set of clusters, based on the filtering parameter; computing a probability that the set of values is associated with each driver identifier of the cluster; determining, at the vehicle sensor system, driving characterization data for the driving session; and in response to the computed probability exceeding a first threshold probability: determining that the new set of values corresponds to one driver identifier within the selected cluster, and associating the driving characterization data with the one driver identifier.

System and method for determining probability that a vehicle driver is associated with a driver identifier

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

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 safe route determination, including determining a geographic risk map for a geographic region, wherein the geographic risk map is determined based on a vehicle event dataset aggregated from a plurality of vehicles enabled with an onboard vehicle system, and automatically determining a route between two locations in the geographic region based on the geographic risk map.

Systems and methods for safe route determination

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.

Growing community of inventors

Palo Alto, CA, United States of America

Stefan Heck