Abstract: 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.

Abstract: An apparatus includes: a camera configured to view a driver of a vehicle; and a processing unit comprising a first model and a second model; wherein the first model is configured to process the images of the driver and to output feature information based on the images, and wherein the second model is configured to receive the feature information from the first model, and to generate an output that indicates whether the driver is drowsy or not.

Abstract: An apparatus includes: a camera configured to view a driver of a vehicle; and a processing unit comprising a first model and a second model; wherein the first model is configured to process the images of the driver and to output feature information based on the images, and wherein the second model is configured to receive the feature information from the first model, and to generate an output that indicates whether the driver is drowsy or not.

Abstract: An apparatus includes: a first camera configured to view an environment outside a vehicle; and a processing unit configured to receive images generated at different respective times by the first camera; wherein the processing unit is configured to identify objects in front of the vehicle based on the respective images generated at the different respective times, determine a distribution of the identified objects, and determine a region of interest based on the distribution of the identified objects in the respective images generated at the different respective times.

Abstract: 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.

Abstract: 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.

Abstract: The disclosed embodiments include a onboard driver distraction determination system. The determination system includes a onboard sensing and computing system(s), which includes inertial sensor(s), internal sensor(s), and external sensor(s). The onboard system samples data from the sensor(s) during a driving session to determine steering activity metrics and driver behavior. A steering activity metric is a representation of the steering inputs by the driver during the driving session. Driver behavior is a representation of how distracted the driver is during the driving session. By performing the above mentioned steps, the system can provide an analysis of driver distraction and optionally, take control of the vehicle to avoid aberrant behavior.

Abstract: 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.

Abstract: The present disclosure relates to techniques to implement a vehicle action using a distributed model distributed across the vehicle and a remote node. A local portion of the distributed model at the vehicle may generate a local output model based on vehicle event data collected at the vehicle. The local output model may be sent from the vehicle at a first location to the remote node at a second location. The remote node may generate a remote output model based on the local output model using the remote portion of the distributed model. The vehicle action may be determined based on inspecting a reconstructed version of the vehicle event data included in the remote output model. The determined vehicle action may be implemented at the vehicle. The distributed model may facilitate the transmission of vehicle event data across multiple locations while securing the transmission of personally-identifiable information.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus includes: a camera configured to view a driver of a vehicle; and a processing unit comprising a first model and a second model; wherein the first model is configured to process the images of the driver and to output feature information based on the images, and wherein the second model is configured to receive the feature information from the first model, and to generate an output that indicates whether the driver is drowsy or not.

Abstract: 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.

Abstract: An apparatus includes: a camera configured to view a driver of a vehicle; and a processing unit configured to receive an image of the driver from the camera; wherein the processing unit is configured to process the image of the driver to determine whether the driver is engaged with a driving task or not; and wherein the processing unit is configured to determine whether the driver is engaged with the driving task or not based on a pose of the driver as it appears in the image without a need to determine a gaze direction of an eye of the driver.

Abstract: An apparatus includes: a first camera configured to view an environment outside a vehicle; and a processing unit configured to receive images generated at different respective times by the first camera; wherein the processing unit is configured to identify objects in front of the vehicle based on the respective images generated at the different respective times, determine a distribution of the identified objects, and determine a region of interest based on the distribution of the identified objects in the respective images generated at the different respective times.

Abstract: 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.

Abstract: 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.

Abstract: A method for localization and mapping, including recording an image at a camera mounted to a vehicle, the vehicle associated with a global system location; identifying a landmark depicted in the image with a landmark identification module of a computing system associated with the vehicle, the identified landmark having a landmark geographic location and a known parameter; extracting a set of landmark parameters from the image with a feature extraction module of the computing system; determining, at the computing system, a relative position between the vehicle and the landmark geographic location based on a comparison between the extracted set of landmark parameters and the known parameter; and updating, at the computing system, the global system location based on the relative position.

Abstract: 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.