Abstract: A method of training a disparity estimation network. The method includes obtaining an eye-gaze dataset having first images with at least one gaze direction associated with each of the first images. A gaze prediction neural network is trained based on the eye-gaze dataset to develop a model trained to provide a gaze prediction for an external image. A depth database is obtained that includes second images having depth information associated with each of the second images. A disparity estimation neural network for object detection is trained based on an output from the gaze prediction neural network and an output from the depth database.

Abstract: A vehicle, system and method of navigating the vehicle. The system includes a sensor and a processor. The sensor is configured to obtain a first set of detection points representative of a lane of a road section at a first time step and a second set of detection points representative of the lane at a second time step. The processor is configured to determine a set of predicted points for the second time step from the first set of detection points, obtain a set of fused points from the second set of detection points and the set of predicted points, and navigate the vehicle using the set of fused points.

Abstract: Systems and methods include obtaining observation points of a lane line using a sensor of a vehicle. Each observation point indicates a location of a point on the lane line. A method includes generating or updating a lane model with the observation points. The lane model indicates a path of the lane line and the lane model is expressed in a lane-specific coordinate system that differs from a vehicle coordinate system that is defined by an orientation of the vehicle. The method also includes transforming the lane-specific coordinate system to maintain a correspondence between the lane-specific coordinate system and the vehicle coordinate system based on a change in orientation of the vehicle resulting in a change in the vehicle coordinate system.

Abstract: Systems and methods involve obtaining observation points of a lane line using one or more sensors of a vehicle. Each observation point indicates a location of a point on the lane line. A method includes obtaining uncertainty values, each uncertainty value corresponding with one of the observation points. A lane model is generated or updated using the observation points. The lane model indicates a path of the lane line. An uncertainty model is generated or updated using the uncertainty values corresponding with the observation points. The uncertainty model indicates uncertainty associated with each portion of the lane model.

Abstract: A vehicle, system and method of navigating the vehicle. The system includes a sensor and a processor. The sensor is configured to obtain a first set of detection points representative of a lane of a road section at a first time step and a second set of detection points representative of the lane at a second time step. The processor is configured to determine a set of predicted points for the second time step from the first set of detection points, obtain a set of fused points from the second set of detection points and the set of predicted points, and navigate the vehicle using the set of fused points.

Abstract: Systems and methods include obtaining observation points of a lane line using a sensor of a vehicle. Each observation point indicates a location of a point on the lane line. A method includes generating or updating a lane model with the observation points. The lane model indicates a path of the lane line and the lane model is expressed in a lane-specific coordinate system that differs from a vehicle coordinate system that is defined by an orientation of the vehicle. The method also includes transforming the lane-specific coordinate system to maintain a correspondence between the lane-specific coordinate system and the vehicle coordinate system based on a change in orientation of the vehicle resulting in a change in the vehicle coordinate system.

Abstract: Systems and methods involve obtaining observation points of a lane line using one or more sensors of a vehicle. Each observation point indicates a location of a point on the lane line. A method includes obtaining uncertainty values, each uncertainty value corresponding with one of the observation points. A lane model is generated or updated using the observation points. The lane model indicates a path of the lane line. An uncertainty model is generated or updated using the uncertainty values corresponding with the observation points. The uncertainty model indicates uncertainty associated with each portion of the lane model.

Abstract: A vehicle, system and method of detecting an object. The system includes an image network, a radar network and a head. The image network receives image data and proposes a boundary box from the image data and an object proposal. The radar network receives radar data and the boundary box and generates a fused set of data including the radar data and the image data. The head determines a parameter of the object from the object proposal and the fused set of data.

Abstract: Methods and apparatus are provided for detecting and assigning objects to sensed values. An object detection arrangement includes a processor that is programmed to execute a first branch of instructions and a second branch of instructions. Each branch of instructions includes receiving a modality from at least one sensor of a group of sensors via a respective interface and determining an output value based on the modality. The object detection arrangement includes an association distance matrix. Modalities of different branches of instructions define different modalities of an object external to the object detection arrangement. The object detection arrangement cumulates the output values, and the association distance matrix associates an object to the cumulated output values to thereby detect and track the object external to the object detection arrangement.

Abstract: A vehicle, system and method of detecting an object. The system includes an image network, a radar network and a head. The image network receives image data and proposes a boundary box from the image data and an object proposal. The radar network receives radar data and the boundary box and generates a fused set of data including the radar data and the image data. The head determines a parameter of the object from the object proposal and the fused set of data.

Abstract: Methods and apparatus are provided for detecting and assigning objects to sensed values. An object detection arrangement includes a processor that is programmed to execute a first branch of instructions and a second branch of instructions. Each branch of instructions includes receiving a modality from at least one sensor of a group of sensors via a respective interface and determining an output value based on the modality. The object detection arrangement includes an association distance matrix. Modalities of different branches of instructions define different modalities of an object external to the object detection arrangement. The object detection arrangement cumulates the output values, and the association distance matrix associates an object to the cumulated output values to thereby detect and track the object external to the object detection arrangement.

Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method includes projecting, by a processing device, tracked targets onto a virtual cylindrical omni-directional camera (VCOC). The method further includes projecting, by the processing device, detected targets onto the VCOC. The method further includes computing, by the processing device, a two-dimensional intersection-over-union (2D-IOU) between the tracked targets and the detected targets. The method further includes performing, by the processing device, an association between the tracked targets and the detected targets based at least in part on the computed IOU. The method further includes controlling, by the processing device, a vehicle based at least in part on the association.

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 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 processor includes a front end including a decoder, an execution unit including a shift-sum multiplier (SSM), and a retirement unit. The decoder includes logic identify a multiplication instruction to multiply a first number and a second number. The execution unit includes logic to, based on the instruction, access a look-up table based on the second number to determine a plurality of shift parameters and one or more flag parameters. The SSM includes logic to use the shift parameters to shift the first number to determine a plurality of partial products, and the flag parameters to determine signs of the partial products. The SSM also includes logic to sum the partial products to yield a result of the multiplication instruction.

Abstract: System, apparatus, method, and computer readable media for texture enhanced non-local means (NLM) image denoising. In embodiments, detail is preserved in filtered image data through a blending between the noisy input target pixel value and the NLM pixel value that is driven by self-similarity and further informed by an independent measure of local texture. In embodiments, the blending is driven by one or more blending weight or coefficient that is indicative of texture so that the level of detail preserved by the enhanced noise reduction filter scales with the amount of texture. Embodiments herein may thereby denoise regions of an image that lack significant texture (i.e. are smooth) more aggressively than more highly textured regions. In further embodiments, the blending coefficient is further determined based on similarity scores of candidate patches with the number of those scores considered being based on the texture score.

Abstract: A processor includes a front end including a decoder, an execution unit including a shift-sum multiplier (SSM), and a retirement unit. The decoder includes logic identify a multiplication instruction to multiply a first number and a second number. The execution unit includes logic to, based on the instruction, access a look-up table based on the second number to determine a plurality of shift parameters and one or more flag parameters. The SSM includes logic to use the shift parameters to shift the first number to determine a plurality of partial products, and the flag parameters to determine signs of the partial products. The SSM also includes logic to sum the partial products to yield a result of the multiplication instruction.

Abstract: System, apparatus, method, and computer readable media for texture enhanced non-local means (NLM) image denoising. In embodiments, detail is preserved in filtered image data through a blending between the noisy input target pixel value and the NLM pixel value that is driven by self-similarity and further informed by an independent measure of local texture. In embodiments, the blending is driven by one or more blending weight or coefficient that is indicative of texture so that the level of detail preserved by the enhanced noise reduction filter scales with the amount of texture. Embodiments herein may thereby denoise regions of an image that lack significant texture (i.e. are smooth) more aggressively than more highly textured regions. In further embodiments, the blending coefficient is further determined based on similarity scores of candidate patches with the number of those scores considered being based on the texture score.