Abstract: A display device includes: a plurality of pixels connected to gate lines and data lines; a gate driver to supply a gate signal to the gate lines; and a data driver to supply a data signal to the data lines. The gate driver includes: a first transistor including a first active layer at a first layer; and a second transistor including a second active layer at a second layer on the first layer.

Abstract: In some examples, systems and methods for multiple-sensor object tracking are provided. For example, a method includes: receiving a first sensor feed and a second sensor feed from a plurality of sensors respectively. The first sensor feed includes a set of first images. The second sensor feed includes a set of second images. In some examples, the method further includes generating an image transformation based on at least one first image in the set of first images and at least one second image in the set of second images, applying the image transformation to the set of second images, aggregating the set of first images and the set of transformed second images to generate a set of aggregated images, and applying a multiple object tracking model to the set of aggregated images to identify a plurality of objects.

Abstract: In some examples, systems and methods for user-assisted object detection are provided. For example, a method includes: receiving a first image frame in a sequence of image frames, performing object tracking using an object tracker to identify a first object of interest and a second object of interest in the first image frame based at least in part on one or more first templates associated with the first object of interest, one or more second templates associated with the second object of interest, and a spatial relationship between the first object of interest and the second object of interest, outputting a first indicator associated with a first image portion corresponding to the identified first object of interest, and outputting a second indicator associated with a second image portion corresponding to the identified second object of interest.

Abstract: In some examples, systems and methods for object tracking are provided. For example, a method includes: receiving an image frame in a sequence of image frames; identifying an object of interest in the image frame using a single-object tracker (SOT) based upon one or more templates associated with the object of interest in a template repository; generating a SOT output based on the identified object of interest; and detecting one or more objects in the image frame using a multiple-object tracker (MOT). In some examples, the MOT including a machine-learning model. In some examples, the method further includes conducting a matching between the SOT output and each detected object of the one or more detected objects to generate a match result; and generating a tracker output based at least in part on the SOT output, the one or more detected objects, and the match result.

Abstract: In some examples, systems and methods for user-assisted object detection are provided. For example, a method includes: receiving a first image frame of a sequence of image frames, performing object detection using an object tracker to identify an object of interest in the first image frame, based upon one or more templates associated with the object of interest in a template repository, outputting a first indicator associated with a first image portion corresponding to the identified object of interest, and receiving a user input associated with the object of interest. In some examples, the user input indicates an identified image portion in the image frame. In some examples, the method further includes generating a retargeted template, based at least in part on the identified image portion, and determining a second image portion associated with the object of interest in a second image frame of the sequence of image frames using the object tracker, based at least in part on the retargeted template.

Abstract: In some examples, systems and methods for user-assisted object detection are provided. For example, a method includes: receiving an input image, and performing object detection by a software detector to identify a set of detected objects. The software detector includes a machine-learning model. The method further includes outputting one or more indicators of the set of detected objects. Each detected object in the set of detected objects is associated with a confidence level. The method further includes receiving a user input; identifying a template including an image portion associated with the user input; determining a similarity metric between the template and an object in the set of detected objects; modifying a confidence level of the object based at least in part on the determined similarity metric; and generating an output including an indicator of the object based at least in part on the modified confidence level.

Abstract: A computer-implemented method for detecting one or more three-dimensional structures in a proximity of a vehicle at runtime includes generating, by a processor, a birds-eye-view (BEV) camera image of the proximity of the vehicle, the BEV camera image comprising two-dimensional coordinates of one or more structures in the proximity. The method further includes generating, by the processor, a BEV height image of the proximity of the vehicle, the BEV height image providing height of the one or more structures in the proximity. The method further includes detecting one or more edges of the three-dimensional structures based on the BEV camera image and the BEV height image. The method further includes generating models of the three-dimensional structures by plane-fitting based on the edges of the one or more three-dimensional structures. The method further includes reconfiguring a navigation system receiver based on the models of the three-dimensional structures.

Abstract: Systems and methods are provided for generating a map for use in controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, aerial image data depicting an environment; processing, by the processor, the aerial image data with a plurality of trained deep learning models to produce a predicted radar map; and controlling the vehicle based on the predicted radar map.

Abstract: A computer-implemented method for detecting one or more three-dimensional structures in a proximity of a vehicle at runtime includes generating, by a processor, a birds-eye-view (BEV) camera image of the proximity of the vehicle, the BEV camera image comprising two-dimensional coordinates of one or more structures in the proximity. The method further includes generating, by the processor, a BEV height image of the proximity of the vehicle, the BEV height image providing height of the one or more structures in the proximity. The method further includes detecting one or more edges of the three-dimensional structures based on the BEV camera image and the BEV height image. The method further includes generating models of the three-dimensional structures by plane-fitting based on the edges of the one or more three-dimensional structures. The method further includes reconfiguring a navigation system receiver based on the models of the three-dimensional structures.

Abstract: Systems and methods are provided for localizing a vehicle. In one embodiment, a method includes: receiving, by a processor, radar data from a radar of the vehicle; creating, by the processor, a local map based on the radar data; retrieving, by the processor, a map of an environment from a datastore based on a predicted vehicle pose; correlating, by the processor, the local map and the retrieved map; determining, by the processor, a localized vehicle pose based on the correlating; and controlling, by the processor, the vehicle based on the localized vehicle pose.

Abstract: Systems and methods are provided for generating a map for use in controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, aerial image data depicting an environment; processing, by the processor, the aerial image data with a plurality of trained deep learning models to produce a predicted radar map; and controlling the vehicle based on the predicted radar map.

Abstract: Systems and methods are provided for localizing a vehicle. In one embodiment, a method includes: receiving, by a processor, radar data from a radar of the vehicle; creating, by the processor, a local map based on the radar data; retrieving, by the processor, a map of an environment from a datastore based on a predicted vehicle pose; correlating, by the processor, the local map and the retrieved map; determining, by the processor, a localized vehicle pose based on the correlating; and controlling, by the processor, the vehicle based on the localized vehicle pose.

Abstract: An automotive vehicle includes a vehicle steering system, an actuator configured to control the steering system, a first controller, and a second controller. The first controller is in communication with the actuator. The first controller is configured to communicate an actuator control signal based on a primary automated driving system control algorithm. The second controller is in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal deviating from a desired route by a threshold distance, control the actuator to maintain a current actuator setting. The second controller is also configured to, in response to the first predicted vehicle path not deviating from the desired route by the threshold distance, control the actuator according to the actuator control signal.

Abstract: An automotive vehicle includes a vehicle steering system, an actuator configured to control the steering system, a first controller, and a second controller. The first controller is in communication with the actuator. The first controller is programmed with a primary automated driving system control algorithm and is configured to communicate an actuator control signal based on the primary automated driving system control algorithm. The second controller is in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal deviating from a current lane, control the actuator to maintain a current actuator setting. The second controller is also configured to, in response to the first predicted vehicle path being within the current lane, control the actuator according to the actuator control signal.

Abstract: An automotive vehicle a vehicle steering system, an actuator configured to control the steering system, a first controller, and a second controller. The first controller is in communication with the actuator, and the second controller is in communication with the actuator and with the first controller. The first controller is configured to communicate an actuator control signal based on a primary automated driving system control algorithm. The actuator control signal includes a commanded actuator setting. The second controller is configured to, in response to a first condition being satisfied, control the actuator according to the actuator control signal. The second controller is also configured to, in response to a second condition being satisfied, control the actuator according to a modified actuator control signal. The modified actuator control signal corresponds to an intermediate actuator setting between the commanded actuator setting and a current actuator setting.

Abstract: An automotive vehicle includes a vehicle steering system, an actuator configured to control the steering system, a first controller, and a second controller. The first controller is in communication with the actuator. The first controller is programmed with a primary automated driving system control algorithm and is configured to communicate an actuator control signal based on the primary automated driving system control algorithm. The second controller is in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal deviating from a current lane, control the actuator to maintain a current actuator setting. The second controller is also configured to, in response to the first predicted vehicle path being within the current lane, control the actuator according to the actuator control signal.

Abstract: An automotive vehicle includes a vehicle steering system, an actuator configured to control the steering system, a first controller, and a second controller. The first controller is in communication with the actuator. The first controller is configured to communicate an actuator control signal based on a primary automated driving system control algorithm. The second controller is in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal deviating from a desired route by a threshold distance, control the actuator to maintain a current actuator setting. The second controller is also configured to, in response to the first predicted vehicle path not deviating from the desired route by the threshold distance, control the actuator according to the actuator control signal.

Abstract: An automotive vehicle includes a vehicle steering system, an actuator configured to control the steering system, and first and second controllers. The first controller is in communication with the actuator, and is configured to communicate an actuator control signal based on a primary automated driving system control algorithm. The second controller is in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal passing within a first threshold distance of a detected obstacle, control the actuator to maintain a current actuator setting. The second controller is also configured to in response to the first predicted vehicle path not passing within the first threshold distance of a detected obstacle, control the actuator according to the actuator control signal.

Abstract: Methods and systems are provided for aligning a steering system of a vehicle. In one embodiment, a method includes: determining when the vehicle is driving a straight-line path; determining a steering wheel position error when the vehicle is driving the straight-line path; computing a rear wheel steering offset based on the steering wheel position error and a closed loop control method; and generating a control signal to a rear wheel steering system based on the rear wheel steering offset.

Abstract: Methods and systems are provided for aligning a steering system of a vehicle. In one embodiment, a method includes: determining when the vehicle is driving a straight-line path; determining a steering wheel position error when the vehicle is driving the straight-line path; computing a rear wheel steering offset based on the steering wheel position error and a closed loop control method; and generating a control signal to a rear wheel steering system based on the rear wheel steering offset.