Abstract: A computer-implemented method of detecting distracted driving comprises: determining, by one or more processors, a primary preview region (PPR) in a representation of an environment; determining, by the one or more processors, a gaze point for a driver based on a sequence of images of the driver; determining, by the one or more processors, that the gaze point is outside of the PPR; based on the determined gaze point being outside of the PPR, decreasing, by the one or more processors, an attention level for the PPR; based on the attention level for the PPR, generating, by the one or more processors, an alert.

Abstract: A method and device for mitigating passenger-related item loss in a travel-for-hire context are disclosed. An item within a vehicle passenger region is identified, and the identified item is associated with an identified passenger. A presence of the identified item is detected within the vehicle cabin passenger region in response to detecting an exit of the identified passenger, and an announcement message is generated based on the presence of the identified item within the vehicle cabin passenger region.

Abstract: The disclosure provides for head pose and gaze angle estimation that includes using a face alignment method with an image of a two-dimensional (2D) face to generate a facial UV position image and facial UV texture image of the 2D face in which to decouple movement of the head and eyes of the 2D face. An eye UV texture image is extracted from the facial UV texture image and stored in a database. The eye region of the facial UV texture image may then be replaced with the eye UV texture image stored in the database to generate a modified facial UV texture image. The modified facial UV texture image is reconstructed to generate a modified 2D face image as training data stored in the database, and the head pose and gaze angles of a person are simultaneously estimated by training a convolutional neural network with the training data.

Abstract: The disclosure relates to technology for monitoring driver attentiveness in a vehicle. A driver distraction system collects vehicle data and scene information from the vehicle while traveling on a route. The vehicle data and scene information are then processed to generate a reference heat map. At the same time, the driver distraction system may capture a gaze of a driver to track a gaze direction and duration of the driver while driving the vehicle on the route. The gaze direction and duration are processed to generate a driver gaze heat map. The driver gaze heat map and reference heat map are analyzed to determine a level of driver distraction of the driver in the vehicle, and a recommendation or warning is output to the driver of the vehicle according to the level of driver distraction.

Abstract: A computer-implemented method of detecting distracted driving comprises: determining, by one or more processors, a primary preview region (PPR) in a representation of an environment; determining, by the one or more processors, a gaze point for a driver based on a sequence of images of the driver; determining, by the one or more processors, that the gaze point is outside of the PPR; based on the determined gaze point being outside of the PPR, decreasing, by the one or more processors, an attention level for the PPR; based on the attention level for the PPR, generating, by the one or more processors, an alert.

Abstract: An activity recognition device comprises a port configured to receive a video stream from a video source for a first object and a second object; a memory configured to store instructions and image frames of the video stream; and one or more processors, wherein the one or more processors execute the instructions stored in the memory, the one or more processors configured to: select portions of the image frames based on presence of the first object; determine areas within the portions of image frames, wherein locations of the first object in the video frames are bounded by the determined areas; determine motion of the first object and locations of a second object within the areas of the image frames; and identify an activity according to the determined motion and locations of the second object, and generate an alert according to the identified activity.

Abstract: A computer-implemented method for estimating head pose angles of a user includes determining a first rotation between a first head pose axis associated with a first image of a plurality of images of the user and a camera axis associated with a camera taking the images. A second rotation is determined between a second head pose axis associated with a second image of the user and the camera axis. The first and second head pose axes are determined based on light reflections within the plurality of images. A head pose angle of the user can be estimated based on the first rotation and the second rotation. An alert can be generated based on the estimated head pose angle.

Abstract: A computer-implemented method of detecting distracted driving comprises: determining, by one or more processors, a primary preview region (PPR) in a representation of an environment; determining, by the one or more processors, a gaze point for a driver based on a sequence of images of the driver; determining, by the one or more processors, that the gaze point is outside of the PPR; based on the determined gaze point being outside of the PPR, decreasing, by the one or more processors, an attention level for the PPR; based on the attention level for the PPR, generating, by the one or more processors, an alert.

Abstract: A method and device for mitigating passenger-related item loss in a travel-for-hire context are disclosed. An item within a vehicle passenger region is identified, and the identified item is associated with an identified passenger. A presence of the identified item is detected within the vehicle cabin passenger region in response to detecting an exit of the identified passenger, and an announcement message is generated based on the presence of the identified item within the vehicle cabin passenger region.

Abstract: An activity recognition device comprises a port configured to receive a video stream from a video source for a first object and a second object; a memory configured to store instructions and image frames of the video stream; and one or more processors, wherein the one or more processors execute the instructions stored in the memory, the one or more processors configured to: select portions of the image frames based on presence of the first object; determine areas within the portions of image frames, wherein locations of the first object in the video frames are bounded by the determined areas; determine motion of the first object and locations of a second object within the areas of the image frames; and identify an activity according to the determined motion and locations of the second object, and generate an alert according to the identified activity.

Abstract: A computer-implemented method for estimating head pose angles of a user includes determining a first rotation between a first head pose axis associated with a first image of a plurality of images of the user and a camera axis associated with a camera taking the images. A second rotation is determined between a second head pose axis associated with a second image of the user and the camera axis. The first and second head pose axes are determined based on light reflections within the plurality of images. A head pose angle of the user can be estimated based on the first rotation and the second rotation. An alert can be generated based on the estimated head pose angle.

Abstract: An activity recognition device comprises a port configured to receive a video stream from a video source for a first object and a second object; a memory configured to store instructions and image frames of the video stream; and one or more processors, wherein the one or more processors execute the instructions stored in the memory, the one or more processors configured to: select portions of the image frames based on presence of the first object; determine areas within the portions of image frames, wherein locations of the first object in the video frames are bounded by the determined areas; determine motion of the first object and locations of a second object within the areas of the image frames; and identify an activity according to the determined motion and locations of the second object, and generate an alert according to the identified activity.

Abstract: A computer-implemented method for estimating head pose angles of a user includes determining a first rotation between a first head pose axis associated with a first image of a plurality of images of the user and a camera axis associated with a camera taking the images. A second rotation is determined between a second head pose axis associated with a second image of the user and the camera axis. The first and second head pose axes are determined based on light reflections within the plurality of images. A head pose angle of the user can be estimated based on the first rotation and the second rotation. An alert can be generated based on the estimated head pose angle.

Abstract: A computer-implemented method of acquiring visual data is provided that comprises: determining, by one or more processors, a gaze point of a person in a vehicle; detecting, by the one or more processors, a gesture by the person in the vehicle; and in response to the detection of the gesture, causing, by the one or more processors, a camera to acquire visual data corresponding to the gaze point of the person.

Abstract: A computer-implemented method of detecting distracted driving comprises: determining, by one or more processors, a primary preview region (PPR) in a representation of an environment; determining, by the one or more processors, a gaze point for a driver based on a sequence of images of the driver; determining, by the one or more processors, that the gaze point is outside of the PPR; based on the determined gaze point being outside of the PPR, decreasing, by the one or more processors, an attention level for the PPR; based on the attention level for the PPR, generating, by the one or more processors, an alert.

Abstract: A vehicle is configured to receive, from a passenger, a pickup request including an approximate location of the passenger, to scan for the passenger after arriving at the approximate location of the passenger, and to determine whether the passenger has been identified by comparing passenger attribute information to results of the scan, and to transmit an approximate location of the vehicle and vehicle identification information to the passenger when the passenger has not been identified or is not accessible for pickup. The passenger is picked up by the vehicle when the passenger has been identified and is accessible for pickup. In addition, the passenger may also use a portable electronic device to identify the vehicle based on the received approximate location of the vehicle and vehicle identification information.

Abstract: An activity recognition device comprises a port configured to receive a video stream from a video source for a first object and a second object; a memory configured to store instructions and image frames of the video stream; and one or more processors, wherein the one or more processors execute the instructions stored in the memory, the one or more processors configured to: select portions of the image frames based on presence of the first object; determine areas within the portions of image frames, wherein locations of the first object in the video frames are bounded by the determined areas; determine motion of the first object and locations of a second object within the areas of the image frames; and identify an activity according to the determined motion and locations of the second object, and generate an alert according to the identified activity.

Abstract: A computer-implemented method for estimating head pose angles of a user includes determining a first rotation between a first head pose axis associated with a first image of a plurality of images of the user and a camera axis associated with a camera taking the images. A second rotation is determined between a second head pose axis associated with a second image of the user and the camera axis. The first and second head pose axes are determined based on light reflections within the plurality of images. A head pose angle of the user can be estimated based on the first rotation and the second rotation. An alert can be generated based on the estimated head pose angle.

Abstract: A system for determining distress of a driver of a vehicle is provided, comprising a plurality of sensors, including interior vehicle image sensors, an interior vehicle audio sensor, vehicle data sensors, and Global Positioning System (GPS) data sensors. The system also includes one or more processors configured to receive inputs from the plurality of sensors, and process the received inputs to obtain a driver heat change estimate, a driver expression estimate, a driver gesture estimate, an on-board diagnostics (OBD) estimate, and a GPS estimate. The one or more processors are further configured to store the estimates in a memory, use the stored estimates to generate deviation scores for each of the estimates, execute a machine learning algorithm to classify driver behavior as normal or impaired based on the deviation scores, and generate a warning based on the classification indicating impaired driver behavior.

Abstract: A method and device for mitigating passenger-related item loss in a travel-for-hire context are disclosed. An item within a vehicle passenger region is identified, and associated the identified item with a passenger identified by a passenger identifier. A vehicle cabin inventory database is updated with an entry based on the identified item associated with the passenger identifier. A presence of the identified item is detected within the vehicle cabin passenger region when a passenger exit event occurs, and message data is generated including the entry that relates to the identified item based on the presence of the identified item within the vehicle cabin passenger region.