Abstract: Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

Abstract: Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

Abstract: Systems and method are provided for monitoring an operator of a vehicle. In one embodiment, a method includes: receiving, by a processor, data generated by the vehicle; determining, by the processor, causal time series event data based on the received data; computing, by the processor, a score for at least one of safety and quality based on a first machine learning model and the causal time series event data; computing, by the processor, at least one explanation for the score based on a second machine learning model; and generating, by the processor, display data to display at least one of the causal time series event data, the score, and the explanation to an end user.

Abstract: A system for analyzing images includes a processing device including a receiving module configured to receive an image associated with a target domain, and a domain adaptation module configured to characterize one or more features represented in the received image based on a domain adaptation model. The domain adaptation model is generated using a machine learning algorithm to train the domain adaptation model, and the machine learning algorithm is configured to train the domain adaptation model based on one or more source domain images associated with a source domain, one or more previously acquired images associated with the target domain, and acquired characterization data associated with the target domain. The system also includes an output module configured to output the received image with characterization data identifying one or more features characterized by the domain adaptation module.

Abstract: A system for analyzing images includes a processing device including a receiving module configured to receive an image associated with a target domain, and a domain adaptation module configured to characterize one or more features represented in the received image based on a domain adaptation model. The domain adaptation model is generated using a machine learning algorithm to train the domain adaptation model, and the machine learning algorithm is configured to train the domain adaptation model based on one or more source domain images associated with a source domain, one or more previously acquired images associated with the target domain, and acquired characterization data associated with the target domain. The system also includes an output module configured to output the received image with characterization data identifying one or more features characterized by the domain adaptation module.

Abstract: An automotive vehicle includes a sensor configured to detect features external to the vehicle, an HMI configured to signal an alert to an operator of the vehicle, and a controller. The controller is in communication with the sensor and the HMI, and is in communication with a non-transient computer-readable storage medium provided with a road furniture database. The road furniture database includes a plurality of items of road furniture having associated road furniture geolocations and road furniture classifications. The controller is configured to, in response to the vehicle being proximate a respective road furniture geolocation for a respective item of road furniture and the sensor not detecting the respective item of road furniture, control the HMI to signal an alert.

Abstract: An attention-based imaging system is described, including a camera that can adjust its field of view (FOV) and resolution and a control routine that can determine one or more regions of interest (ROI) within the FOV to prioritize camera resources. The camera includes an image sensor, an internal lens, a steerable mirror, an external lens, and a controller. The external lens is disposed to monitor a viewable region, and the steerable mirror is interposed between the internal lenses and the external lenses. The steerable mirrors are arranged to project the viewable region from the external lens onto the image sensor via the internal lens. The steerable mirror modifies the viewable region that is projected onto the image sensor and controls the image sensor to capture an image. The associated control routine can be deployed either inside the camera or in a separate external processor.

Abstract: A system and method for object detection and classification includes receiving by a controller a first data of a scene, wherein the data reflects the scene at a first time, and performing a first classification of at least one object within the scene based on the data. The method includes determining a projected location of the at least one object corresponding to an estimated location at a second time. The method includes receiving a second data of the scene reflecting the scene at the second time. The method includes determining whether the projected location of the object corresponds to the location of the object as reflected by the second data. The method includes determining whether performing a second classification of the object is necessary based on the determination of whether the projected location corresponds to the location of the object reflected by the second data.

Abstract: An automotive vehicle includes a sensor configured to detect features external to the vehicle, an HMI configured to signal an alert to an operator of the vehicle, and a controller. The controller is in communication with the sensor and the HMI, and is in communication with a non-transient computer-readable storage medium provided with a road furniture database. The road furniture database includes a plurality of items of road furniture having associated road furniture geolocations and road furniture classifications. The controller is configured to, in response to the vehicle being proximate a respective road furniture geolocation for a respective item of road furniture and the sensor not detecting the respective item of road furniture, control the HMI to signal an alert.

Abstract: Systems and methods are provided for detecting features from multi-modal image-like data representations. The system includes a wavelet transformer configured to, via at least one processor, receive image data and to wavelet transform the image data, thereby providing decomposed image data divided into frequency sub-bands. The system further includes an artificial neural network configured to receive and process at least one sub-band of the decomposed image data to detect image features based thereon, the artificial neural network configured to output the detected image features.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method of controlling a vehicle, includes receiving, via at least one processor, image data from each of plural cameras mounted on the vehicle. The method includes assembling, via the at least one processor, the image data from each of the plural cameras to form assembled image data. The method includes classifying and localizing lane features using an artificial neural network based on the assembled image data, to produce classified and localized lane data. The method includes performing, via the at least one processor, a data fusion process based on the classified and localized lane data, thereby producing fused lane feature data. The method includes controlling, via the at least one processor, the vehicle based, in part, on the fused lane feature data.

Abstract: A system and method for object detection and classification includes receiving by a controller a first data of a scene, wherein the data reflects the scene at a first time, and performing a first classification of at least one object within the scene based on the data. The method includes determining a projected location of the at least one object corresponding to an estimated location at a second time. The method includes receiving a second data of the scene reflecting the scene at the second time. The method includes determining whether the projected location of the object corresponds to the location of the object as reflected by the second data. The method includes determining whether performing a second classification of the object is necessary based on the determination of whether the projected location corresponds to the location of the object reflected by the second data.

Abstract: Techniques for road scene primitive detection using a vehicle camera system are disclosed. In one example implementation, a computer-implemented method includes receiving, by a processing device having at least two parallel processing cores, at least one image from a camera associated with a vehicle on a road. The processing device generates a plurality of views from the at least one image that include a feature primitive. The feature primitive is indicative of a vehicle or other road scene entities of interest. Using each of the parallel processing cores, a set of primitives are identified from one or more of the plurality of views. The feature primitives are identified using one or more of machine learning and classic computer vision techniques. The processing device outputs, based on the plurality of views, result primitives based on the plurality of identified primitives from multiple views based on the plurality of identified entities.

Abstract: A vehicle, system and method of autonomous navigation of the vehicle. A reference trajectory for navigating a training traffic scenario along a road section is received at a processor of the vehicle. The processor determines a coefficient for a cost function associated with a candidate trajectory that simulates the reference trajectory. The determined coefficient is provided to a neural network to train the neural network. The trained neural network generates a navigation trajectory for navigating the vehicle using a cost coefficient determined by the neural network. The vehicle is navigated along the road section using the navigation trajectory.

Abstract: Systems and methods are provided for detecting features from multi-modal image-like data representations. The system includes a wavelet transformer configured to, via at least one processor, receive image data and to wavelet transform the image data, thereby providing decomposed image data divided into frequency sub-bands. The system further includes an artificial neural network configured to receive and process at least one sub-band of the decomposed image data to detect image features based thereon, the artificial neural network configured to output the detected image features.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method of controlling a vehicle, includes receiving, via at least one processor, image data from each of plural cameras mounted on the vehicle. The method includes assembling, via the at least one processor, the image data from each of the plural cameras to form assembled image data. The method includes classifying and localizing lane features using an artificial neural network based on the assembled image data, to produce classified and localized lane data. The method includes performing, via the at least one processor, a data fusion process based on the classified and localized lane data, thereby producing fused lane feature data. The method includes controlling, via the at least one processor, the vehicle based, in part, on the fused lane feature data.

Abstract: Examples of techniques for road feature detection using a vehicle camera system are disclosed. In one example implementation, a computer-implemented method includes receiving, by a processing device, an image from a camera associated with a vehicle on a road. The computer-implemented method further includes generating, by the processing device, a top view of the road based at least in part on the image. The computer-implemented method further includes detecting, by the processing device, lane boundaries of a lane of the road based at least in part on the top view of the road. The computer-implemented method further includes detecting, by the processing device, a road feature within the lane boundaries of the lane of the road using machine learning.

Abstract: A method to equip a vehicle to perform object detection and tracking and a surround view camera system to perform the object detection and tracking involve two or more cameras arranged respectively at two or more locations of the vehicle. The cameras capture images within a field of view of the two or more cameras. A processing system obtains the images from the two or more cameras and performs image processing to detect and track objects in the field of view of the two or more cameras.

Abstract: Techniques for road scene primitive detection using a vehicle camera system are disclosed. In one example implementation, a computer-implemented method includes receiving, by a processing device having at least two parallel processing cores, at least one image from a camera associated with a vehicle on a road. The processing device generates a plurality of views from the at least one image that include a feature primitive. The feature primitive is indicative of a vehicle or other road scene entities of interest. Using each of the parallel processing cores, a set of primitives are identified from one or more of the plurality of views. The feature primitives are identified using one or more of machine learning and classic computer vision techniques. The processing device outputs, based on the plurality of views, result primitives based on the plurality of identified primitives from multiple views based on the plurality of identified entities.