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: 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: 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.

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: A method for digital environment reconstruction, including: capturing data using an onboard vehicle system; determining a vehicle event and an event type 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.

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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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: 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.

Abstract: 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.

Abstract: 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.