Abstract: Systems and method are provided for generating map information in an autonomous vehicle. In one embodiment, a method includes: receiving image data associated with an environment of the autonomous vehicle; receiving object data associated with detected objects within the environment of the autonomous vehicle; processing the image data, the object data, and road level information using a deep learning network to obtain a first map, wherein the first map is in image coordinates; processing the first map with a second map in geographic coordinates to generate a maplet; and controlling the autonomous vehicle based on the maplet.

Abstract: A method in a vehicle for performing multiple on-board sensing tasks concurrently in the same network using deep learning algorithms is provided. The method includes receiving vision sensor data from a sensor on the vehicle, determining a set of features from the vision sensor data using a plurality of feature layers in a convolutional neural network, and concurrently estimating, using the convolutional neural network, bounding boxes for detected objects, free-space boundaries, and object poses for detected objects from the set of features determined by the plurality of feature layers. The neural network may include a plurality of free-space estimation layers configured to determine the boundaries of free-space in the vision sensor data, a plurality of object detection layers configured to detect objects in the image and to estimate bounding boxes that surround the detected objects, and a plurality of object pose detection layers configured to estimate the direction of each object.

Abstract: A method of determining velocity of a target and a fusion system on a moving platform to determine the velocity of the target are described. The method includes obtaining, using a radar system, position and radial velocity of the target relative to the moving platform, obtaining, using a vision system, optical flow vectors based on motion of the target relative to the moving platform, and estimating a dominant motion vector of the target based on the optical flow vectors. The method also includes processing the position, the radial velocity, and the dominant motion vector and determining the velocity of the target in two dimensions.

Abstract: Systems and methods for detecting objects from a moving platform are provided. The method includes receiving, from a camera coupled to the moving platform, first and second image frames, wherein the second image frame is subsequent in time to the first image frame. Within the first image frame, a first target is defined characterizing the location of an object detected in the first image frame. A first score associated with the first target and a second score associated with the second target are determined, and a first predicted target is determined based on a tracking process applied to the first target. A third score associated with the first predicted target is determined. The method determines a merged target corresponding to the second target when the second score is greater than the third score and a proximity measure between the second target and the first predicted target is above a threshold.

Abstract: A user-centric driving-support system for implementation at a vehicle of transportation. The system in various embodiments includes one or more vehicle sensors, such as a camera, a RADAR, and a LiDAR, and a hardware-based processing unit. The system further includes a non-transitory computer-readable storage device including an activity unit and an output-structuring unit. The activity unit, when executed by the hardware-based processing unit, determines, based on contextual input information, at least one of an alert-assessment output and a scene-awareness output, wherein the contextual input information includes output of the vehicle sensor. The output-structuring unit, when executed by the hardware-based processing unit, determines an action to be performed at the vehicle based on at least one of the alert-assessment output and the scene-awareness output determined by the activity unit.

Abstract: Methods and system for detecting an object are provided. In one embodiment, a method includes: receiving, by a processor, image data from a single camera, the image data representing an image of scene; determining, by the processor, stixel data from the image data; detecting, by the processor, an object based on the stixel data; and selectively generating, by the processor, an alert signal based on the detected object.

Abstract: A method for monitoring a vehicle operator can be executed by a controller and includes the following steps: (a) receiving image data of a vehicle operator's head; (b) tracking facial feature points of the vehicle operator based on the image data; (c) creating a 3D model of the vehicle operator's head based on the facial feature points in order to determine a 3D position of the vehicle operator's head; (d) determining a gaze direction of the vehicle operator based on a position of the facial feature points and the 3D model of the vehicle operator's head; (e) determining a gaze vector based on the gaze direction and the 3D position of the vehicle operator's head; and (f) commanding an indicator to activate when the gaze vector is outside a predetermined parameter.

Abstract: A method of determining velocity of a target and a fusion system on a moving platform to determine the velocity of the target are described. The method includes obtaining, using a radar system, position and radial velocity of the target relative to the moving platform, obtaining, using a vision system, optical flow vectors based on motion of the target relative to the moving platform, and estimating a dominant motion vector of the target based on the optical flow vectors. The method also includes processing the position, the radial velocity, and the dominant motion vector and determining the velocity of the target in two dimensions.

Abstract: The invention provides a device for towing a motorcycle behind a land vehicle with a front wheel of the motorcycle off the ground. The device has a platform connectable via a support bar to a hitch receiver of the towing vehicle. The platform is pivotally connected to the support bar in order to allow the motorcycle to tilts as the towing vehicle turns. The platform has a stationary front stopper for the front side of the motorcycle wheel supported by the platform and a rear stopper which is pivotally connected to the platform and which forms a mechanism for automatically locking the front wheel of the motorcycle from behind when the rear stopper is turned during loading the wheel onto the platform.

Abstract: Methods and system for detecting an object are provided. In one embodiment, a method includes: receiving, by a processor, image data from a single camera, the image data representing an image of scene; determining, by the processor, stixel data from the image data; detecting, by the processor, an object based on the stixel data; and selectively generating, by the processor, an alert signal based on the detected object.

Abstract: A method for monitoring a vehicle operator can be executed by a controller and includes the following steps: (a) receiving image data of a vehicle operator's head; (b) tracking facial feature points of the vehicle operator based on the image data; (c) creating a 3D model of the vehicle operator's head based on the facial feature points in order to determine a 3D position of the vehicle operator's head; (d) determining a gaze direction of the vehicle operator based on a position of the facial feature points and the 3D model of the vehicle operator's head; (e) determining a gaze vector based on the gaze direction and the 3D position of the vehicle operator's head; and (f) commanding an indicator to activate when the gaze vector is outside a predetermined parameter.

Abstract: A method for detecting an eyes-off-the-road condition based on an estimated gaze direction of a driver of a vehicle includes monitoring facial feature points of the driver within image input data captured by an in-vehicle camera device. A location for each of a plurality of eye features for an eyeball of the driver is detected based on the monitored facial features. A head pose of the driver is estimated based on the monitored facial feature points. The gaze direction of the driver is estimated based on the detected location for each of the plurality of eye features and the estimated head pose.

Abstract: Methods and systems are provided for detecting an object in an image. In one embodiment, a method includes: receiving, by a processor, data from a single sensor, the data representing an image; dividing, by the processor, the image into vertical sub-images; processing, by the processor, the vertical sub-images based on deep learning models; and detecting, by the processor, an object based on the processing.

Abstract: A system and method may compare an image vector representing an image feature of a first image fragment of an image to database vectors representing the image feature of database image fragments of database images. It may be determined based on the comparison a first matching database vector of the database vectors which most closely, among the database vectors, describes the first image feature represented by the image vector. The system or method may determine, using a data structure in conjunction with the first matching database vector and previously matched database vectors, a second of the database vectors which includes the first matching database vector and the previously matched database vectors and most closely describes a second image fragment including the first image fragment. The system or method may determine an object feature based on the second database vector.

Abstract: A method of creating a shadow-reduced image from a captured image. An image of a scene exterior of a vehicle is captured by a vehicle-based image capture device. A first object profile of an object in the captured image is identified by a processor. A second object profile of the object is detected using a non-vision object detection device. Shadows in the captured image are removed by the processor as a function of the first object profile and the second object profile. A shadow reduced image is utilized in a vehicle-based application.

Abstract: Systems and methods for detecting objects from a moving platform are provided. The method includes receiving, from a camera coupled to the moving platform, first and second image frames, wherein the second image frame is subsequent in time to the first image frame. Within the first image frame, a first target is defined characterizing the location of an object detected in the first image frame. A first score associated with the first target and a second score associated with the second target are determined, and a first predicted target is determined based on a tracking process applied to the first target. A third score associated with the first predicted target is determined. The method determines a merged target corresponding to the second target when the second score is greater than the third score and a proximity measure between the second target and the first predicted target is above a threshold.

Abstract: A method for determining an Eyes-Off-The-Road (EOTR) condition exists includes capturing image data corresponding to a driver from a monocular camera device. A detection of whether the driver is wearing eye glasses based on the image data using an eye glasses classifier. When it is detected that the driver is wearing eye glasses, a driver face location is detected from the captured image data and it is determined whether the EOTR condition exists based on the driver face location using an EOTR classifier.

Abstract: A method of creating a shadow-reduced image from a captured image. An image of a scene exterior of a vehicle is captured by a vehicle-based image capture device. A first object profile of an object in the captured image is identified by a processor. A second object profile of the object is detected using a non-vision object detection device. Shadows in the captured image are removed by the processor as a function of the first object profile and the second object profile. A shadow reduced image is utilized in a vehicle-based application.

Abstract: A method for detecting an eyes-off-the-road condition based on an estimated gaze direction of a driver of a vehicle includes monitoring facial feature points of the driver within image input data captured by an in-vehicle camera device. A location for each of a plurality of eye features for an eyeball of the driver is detected based on the monitored facial features. A head pose of the driver is estimated based on the monitored facial feature points. The gaze direction of the driver is estimated based on the detected location for each of the plurality of eye features and the estimated head pose.

Abstract: A method for determining an Eyes-Off-The-Road (EOTR) condition exists includes capturing image data corresponding to a driver from a monocular camera device. A detection of whether the driver is wearing eye glasses based on the image data using an eye glasses classifier. When it is detected that the driver is wearing eye glasses, a driver face location is detected from the captured image data and it is determined whether the EOTR condition exists based on the driver face location using an EOTR classifier.