Abstract: A method for real time analysis, the method includes producing, by a classification unit having a neural network, classification decisions for sensed information units obtained in an environment of a vehicle; automatically generating, by one or more computing devices and having auto-labeling capabilities running in a real-time driving of the vehicle, an automated ground truth labeling for a determined set of sensed information units; detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification detection; and responsive to the detecting, addressing the detected issue in the real-time driving of the vehicle, using a signature associated with at least the classification decision or the detected issue. The neural network is in a same state in the producing the classification decision, detecting the issue, and addressing the issue.

Abstract: A computer-implemented method for navigating a host vehicle may include receiving an image frame acquired by an image capture device associated with the host vehicle; identifying in the image frame a representation of a target vehicle; determining an orientation indicator associated with the target vehicle; based on the determined orientation indicator, identifying a candidate region of the acquired image frame where a representation of a vehicle door of the target vehicle is expected in an open door condition; extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.

Abstract: A method for real time analysis, the method includes producing, by a classification unit having a neural network, classification decisions for sensed information units obtained in an environment of a vehicle; automatically generating, by one or more computing devices and having auto-labeling capabilities running in a real-time driving of the vehicle, an automated ground truth labeling for a determined set of sensed information units; detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification detection; and responsive to the detecting, addressing the detected issue in the real-time driving of the vehicle, using a signature associated with at least the classification decision or the detected issue. The neural network is in a same state in the producing the classification decision, detecting the issue, and addressing the issue.

Abstract: A computer-implemented method for navigating a host vehicle may include receiving an image frame acquired by an image capture device associated with the host vehicle; identifying in the image frame a representation of a target vehicle; determining an orientation indicator associated with the target vehicle; based on the determined orientation indicator, identifying a candidate region of the acquired image frame where a representation of a vehicle door of the target vehicle is expected in an open door condition; extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.

Abstract: A method for classification process evaluation, the method includes (a) receiving, at a processing circuit, classification data generated by a classification process for augmented versions of a test sensed information unit; (b) evaluating the classification data across the augmented versions, by analyzing a distribution of, at least, selected classification values of the classification data; (c) determining, based on the evaluation, a compatibility of the classification process with respect to the received classification data; and (d) issuing a compatibility indication in accordance with the determined compatibility, the compatibility indication indicative of the compatibility of the classification process to classify an element captured by the sensed information unit.

Abstract: A computer-implemented method for navigating a host vehicle may include receiving an image frame acquired by an image capture device associated with the host vehicle; identifying in the image frame a representation of a target vehicle; determining an orientation indicator associated with the target vehicle; based on the determined orientation indicator, identifying a candidate region of the acquired image frame where a representation of a vehicle door of the target vehicle is expected in an open door condition; extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.

Abstract: A method for real time management of detected issues, the method includes producing, by a classification unit having a neural network, a classification decision for sensed information obtained in an environment of a vehicle; generating, by one or more computing devices with auto-labeling capabilities, an automated ground truth labeling for the sensed information; detecting, by the one or more computing devices and based on a performance indication related to the automated ground truth labeling, an issue with respect to the classification decision; and responsive to the detecting, addressing the detected issue in a driving in the environment of the vehicle by a computer device associated with the vehicle, using a signature generated in association with at least the classification decision or with the detected issue. The neural network is in a same state in the producing of the classification decision, the detecting the issue, and the addressing the detected issue.

Abstract: A computer-implemented method for navigating a host vehicle may include receiving an image frame acquired by an image capture device associated with the host vehicle; identifying in the image frame a representation of a target vehicle; determining an orientation indicator associated with the target vehicle; based on the determined orientation indicator, identifying a candidate region of the acquired image frame where a representation of a vehicle door of the target vehicle is expected in an open door condition; extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.

Abstract: The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.

Abstract: A computer-implemented method for navigating a host vehicle may include receiving an image frame acquired by an image capture device associated with the host vehicle; identifying in the image frame a representation of a target vehicle; determining an orientation indicator associated with the target vehicle; based on the determined orientation indicator, identifying a candidate region of the acquired image frame where a representation of a vehicle door of the target vehicle is expected in an open door condition; extracting the candidate region from the image frame; providing the candidate region to an open door detection network; determining a navigational action for the host vehicle in response to an indication from the open door detection network that the candidate region includes a representation of a door of the target vehicle in an open condition; and causing an actuator associated with the host vehicle to implement the navigational action.

Abstract: The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.

Abstract: The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.

Abstract: The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.