Abstract: A system in a vehicle includes a first sensor to obtain first sensor data from a first field of view and provide a first top-view feature representation. The system also includes a second sensor to obtain second sensor data from a second field of view with an overlap with the first field of view and provide a second top-view feature representation. Processing circuitry implements a neural network and provides a top-view stixel representation based on the first top-view feature representation and the second top-view feature representation. The top-view three-dimensional stixel representation is used to control an operation of the vehicle.

Abstract: A vehicle having an imaging sensor that is arranged to monitor a field-of-view (FOV) that includes a travel surface proximal to the vehicle is described. Detecting the travel lane includes capturing a FOV image of a viewable region of the travel surface. The FOV image is converted, via an artificial neural network, to a plurality of feature maps. The feature maps are projected, via an inverse perspective mapping algorithm, onto a BEV orthographic grid. The feature maps include travel lane segments and feature embeddings, and the travel lane segments are represented as line segments. The line segments are concatenated for the plurality of grid sections based upon the feature embeddings to form a predicted lane. The concatenation, or clustering is accomplished via the feature embeddings.

Abstract: A system in a vehicle includes a first sensor to obtain first sensor data from a first field of view and provide a first top-view feature representation. The system also includes a second sensor to obtain second sensor data from a second field of view with an overlap with the first field of view and provide a second top-view feature representation. Processing circuitry implements a neural network and provides a top-view stixel representation based on the first top-view feature representation and the second top-view feature representation. The top-view three-dimensional stixel representation is used to control an operation of the vehicle.

Abstract: A system for end to end prediction of lane detection uncertainty includes a sensor device of a host vehicle generating data related to a road surface and a navigation controller including a computerized processor operable to monitor an input image from the sensor device, utilize a convolutional neural network to analyze the input image and output a lane prediction and a lane uncertainty prediction, and generate a commanded navigation plot based upon the lane prediction and the lane uncertainty prediction. The convolutional neural network is initially trained using a per point association and error calculation, including associating a selected ground truth lane to a selected set of data points related to a predicted lane and then associating at least one point of the selected ground truth lane to a corresponding data point from the selected set of data points related to the predicted lane.

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 having an imaging sensor that is arranged to monitor a field-of-view (FOV) that includes a travel surface proximal to the vehicle is described. Detecting the travel lane includes capturing a FOV image of a viewable region of the travel surface. The FOV image is converted, via an artificial neural network, to a plurality of feature maps. The feature maps are projected, via an inverse perspective mapping algorithm, onto a BEV orthographic grid. The feature maps include travel lane segments and feature embeddings, and the travel lane segments are represented as line segments. The line segments are concatenated for the plurality of grid sections based upon the feature embeddings to form a predicted lane. The concatenation, or clustering is accomplished via the feature embeddings.

Abstract: A system for end to end prediction of lane detection uncertainty includes a sensor device of a host vehicle generating data related to a road surface and a navigation controller including a computerized processor operable to monitor an input image from the sensor device, utilize a convolutional neural network to analyze the input image and output a lane prediction and a lane uncertainty prediction, and generate a commanded navigation plot based upon the lane prediction and the lane uncertainty prediction. The convolutional neural network is initially trained using a per point association and error calculation, including associating a selected ground truth lane to a selected set of data points related to a predicted lane and then associating at least one point of the selected ground truth lane to a corresponding data point from the selected set of data points related to the predicted lane.

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.