Abstract: Provided are systems, methods, and computer program products for ensemble based vehicle motion planning. A model ensemble including a routing model and multiple planning models may be trained and applied to generate a trajectory for navigating a vehicle in a scenario. In some cases, the routing model may select, from multiple candidate trajectories generated by the planning models based on the scenario, the trajectory generated by the best performing planning model. Alternatively, the routing model may successively activate one or more of the planning models to generate one or more candidate trajectories based on the scenario until the routing model identifies a trajectory satisfying one or more criteria.

Abstract: Provided are methods for generating future maneuver parameters in a planner, which include receiving at least one first set of parameters associated with one or more previous maneuvers of a vehicle and at least one second set of parameters associated with a maneuver goal of the vehicle, generating, using the first and second sets of parameters, a future maneuver parameter corresponding to a future maneuver of the vehicle, training at least one data model by comparing the generated future maneuver parameter to one or more reference maneuver parameters, generating, based on training, a corrected future maneuver parameter. The corrected future maneuver parameter includes a future maneuver of the vehicle and a correction to the future maneuver of the vehicle. The first set of parameters includes the generated corrected future maneuver parameter, which is used to correct at least one first parameter. The training is executed using the corrected first parameter.

Abstract: Provided are methods for generating future maneuver parameters in a planner, which include receiving at least one first set of parameters associated with one or more previous maneuvers of a vehicle and at least one second set of parameters associated with a maneuver goal of the vehicle, generating, using the first and second sets of parameters, a future maneuver parameter corresponding to a future maneuver of the vehicle, training at least one data model by comparing the generated future maneuver parameter to one or more reference maneuver parameters, generating, based on training, a corrected future maneuver parameter. The corrected future maneuver parameter includes a future maneuver of the vehicle and a correction to the future maneuver of the vehicle. The first set of parameters includes the generated corrected future maneuver parameter, which is used to correct at least one first parameter. The training is executed using the corrected first parameter.

Abstract: Visual analytics tool for updating object detection models in autonomous driving applications. In one embodiment, an object detection model analysis system including a computer and an interface device. The interface device includes a display device. The computer includes an electronic processor that is configured to extract object information from image data with a first object detection model, extract characteristics of objects from metadata associated with image data, generate a summary of the object information and the characteristics, generate coordinated visualizations based on the summary and the characteristics, generate a recommendation graphical user interface element based on the coordinated visualizations and a first one or more user inputs, and update the first object detection model based at least in part on a classification of one or more individual objects as an actual weakness in the first object detection model to generate a second object detection model for autonomous driving.

Abstract: A system and method for domain adaptation involves a first domain and a second domain. A machine learning system is trained with first sensor data and first label data of the first domain. Second sensor data of a second domain is obtained. Second label data is generated via the machine learning system based on the second sensor data. Inter-domain sensor data is generated by interpolating the first sensor data of the first domain with respect to the second sensor data of the second domain. Inter-domain label data is generated by interpolating first label data of the first domain with respect to second label data of the second domain. The machine learning system is operable to generate inter-domain output data in response to the inter-domain sensor data. Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data.

Abstract: Methods, systems, and non-transitory computer-readable media for generating augmented data to train a deep neural network to detect traffic lights in image data. The method includes receiving a plurality of real roadway scene images and selecting a subset of the plurality of real roadway scene images. The method also includes selecting an image from the subset and determining a distribution indicting how likely each location in the selected image can contain a traffic light. The method further includes selecting a location in the selected image by sampling the distribution and superimposing a traffic light image onto the selected image at the selected location to generate an augmented roadway scene image. The method also includes processing each image in the subset to generate a plurality of augmented roadway scene images. The method further includes training a deep neural network model using the pluralities of real and augmented roadway scene images.

Abstract: Systems, methods, and non-transitory computer-readable media for detecting small objects in a roadway scene. A camera is coupled to a vehicle and configured to capture a roadway scene image. An electronic controller is coupled to the camera and configured to receive the roadway scene image from the camera. The electronic controller is also configured to generate a Generative Adversarial Network (GAN) model using the roadway scene image. The electronic controller is further configured to determine a distribution indicting how likely each location in the roadway scene image can contain a roadway object using the GAN model. The electronic controller is also configured to determine a plurality of locations in the roadway scene image by sampling the distribution. The electronic controller is further configured to detect the roadway object at one of the plurality of locations in the roadway scene images.

Abstract: A method for quantum key fusion-based virtual power plant security communication includes: identity authentication, performing identity authentication between a client and a server in a virtual power plant based on a communication requirement to acquire a root key; key distribution: generating a key encryption key and a message authentication key based on the acquired root key and performing negotiation on a data encryption key to obtain the data encryption key; and data encryption: encrypting to-be-encrypted data using the data encryption key, and implementing communication of the data. During the identity authentication or the key distribution, negotiation on a quantum key is performed by a quantum key server, and the quantum key obtained by the negotiation is used for implementing the identity authentication or used as the data encryption key. A device for quantum key fusion-based virtual power plant security communication and a computer storage medium are provided.

Abstract: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data, encode the input via an encoder, during a first sequence, obtain a first latent variable defining an attribute of the input data, generate a sequential reconstruction of the input data utilizing the decoder and at least the first latent variable, obtain a residual between the input data and the reconstruction utilizing a comparison of at least the first latent variable, and output a final reconstruction of the input data utilizing a plurality of residuals from a plurality of sequences.

Abstract: Systems, methods, and non-transitory computer-readable media for detecting small objects in a roadway scene. A camera is coupled to a vehicle and configured to capture a roadway scene image. An electronic controller is coupled to the camera and configured to receive the roadway scene image from the camera. The electronic controller is also configured to generate a Generative Adversarial Network (GAN) model using the roadway scene image. The electronic controller is further configured to determine a distribution indicting how likely each location in the roadway scene image can contain a roadway object using the GAN model. The electronic controller is also configured to determine a plurality of locations in the roadway scene image by sampling the distribution. The electronic controller is further configured to detect the roadway object at one of the plurality of locations in the roadway scene images.

Abstract: Methods, systems, and non-transitory computer-readable media for generating augmented data to train a deep neural network to detect traffic lights in image data. The method includes receiving a plurality of real roadway scene images and selecting a subset of the plurality of real roadway scene images. The method also includes selecting an image from the subset and determining a distribution indicting how likely each location in the selected image can contain a traffic light. The method further includes selecting a location in the selected image by sampling the distribution and superimposing a traffic light image onto the selected image at the selected location to generate an augmented roadway scene image. The method also includes processing each image in the subset to generate a plurality of augmented roadway scene images. The method further includes training a deep neural network model using the pluralities of real and augmented roadway scene images.

Abstract: A system and method for domain adaptation involves a first domain and a second domain. A machine learning system is trained with first sensor data and first label data of the first domain. Second sensor data of a second domain is obtained. Second label data is generated via the machine learning system based on the second sensor data. Inter-domain sensor data is generated by interpolating the first sensor data of the first domain with respect to the second sensor data of the second domain. Inter-domain label data is generated by interpolating first label data of the first domain with respect to second label data of the second domain. The machine learning system is operable to generate inter-domain output data in response to the inter-domain sensor data. Inter-domain loss data is generated based on the inter-domain output data with respect to the inter-domain label data. Parameters of the machine learning system are updated upon optimizing final loss data that includes at least the inter-domain loss data.

Abstract: Visual analytics tool for updating object detection models in autonomous driving applications. In one embodiment, an object detection model analysis system including a computer and an interface device. The interface device includes a display device. The computer includes an electronic processor that is configured to extract object information from image data with a first object detection model, extract characteristics of objects from metadata associated with image data, generate a summary of the object information and the characteristics, generate coordinated visualizations based on the summary and the characteristics, generate a recommendation graphical user interface element based on the coordinated visualizations and a first one or more user inputs, and update the first object detection model based at least in part on a classification of one or more individual objects as an actual weakness in the first object detection model to generate a second object detection model for autonomous driving.

Abstract: Weaknesses may be exposed in image object detectors. An image object is overlaid onto a background image at each of a plurality of locations, the background image including a scene in which the image objects can be present. A detector model is used to attempt detection of the image object as overlaid onto the background image, the detector model being trained to identify the image object in background images, the detection resulting in background scene detection scores indicative of likelihood of the image object being detected at each of the plurality of locations. A detectability map is displayed overlaid on the background image, the detectability map including, for each of the plurality of locations, a bounding box of the image object illustrated according to the respective detection score.

Abstract: Weaknesses may be exposed in image object detectors. An image object is overlaid onto a background image at each of a plurality of locations, the background image including a scene in which the image objects can be present. A detector model is used to attempt detection of the image object as overlaid onto the background image, the detector model being trained to identify the image object in background images, the detection resulting in background scene detection scores indicative of likelihood of the image object being detected at each of the plurality of locations. A detectability map is displayed overlaid on the background image, the detectability map including, for each of the plurality of locations, a bounding box of the image object illustrated according to the respective detection score.

Abstract: A system includes a display device, a memory configured to store a visual analysis application and image data including a plurality of images including detectable objects; and a processor, operatively connected to the memory and the display device. The processor is configured to execute the visual analysis application to learn generative factors from objects detected in the plurality of images, visualize the generative factors in a user interface provided to the display device, receive grouped combinations of the generative factors and values to apply to the generative factors to control object features, create generated objects by applying the values of the generative factors to the objects detected in the plurality of images, combine the generated objects into the original images to create generated images, and apply a discriminator to the generated images to reject unrealistic images.

Abstract: A computer-program product storing instructions which, when executed by a computer, cause the computer to receive an input data, encode the input via an encoder, during a first sequence, obtain a first latent variable defining an attribute of the input data, generate a sequential reconstruction of the input data utilizing the decoder and at least the first latent variable, obtain a residual between the input data and the reconstruction utilizing a comparison of at least the first latent variable, and output a final reconstruction of the input data utilizing a plurality of residuals from a plurality of sequences.

Abstract: The systems and methods provided herein are directed to the uploading and transmission of vehicle data to a remote system when a physiological event for a driver has been detected using one or more sensors. Information such as the driver's heart rate, temperature, voice inflection or facial expression may be monitored to detect the physiological event. Vehicle data, such as gathering or control system data, may be sent once the event has been detected. Selected vehicle data associated with the event or all data during the time of the event may be sent. After receiving the vehicle data, the remote system may process or store it where it may be used to modify automated driving functionalities.

Abstract: A system includes a display device, a memory configured to store a visual analysis application and image data including a plurality of images including detectable objects; and a processor, operatively connected to the memory and the display device. The processor is configured to execute the visual analysis application to learn generative factors from objects detected in the plurality of images, visualize the generative factors in a user interface provided to the display device, receive grouped combinations of the generative factors and values to apply to the generative factors to control object features, create generated objects by applying the values of the generative factors to the objects detected in the plurality of images, combine the generated objects into the original images to create generated images, and apply a discriminator to the generated images to reject unrealistic images.

Abstract: A method for quantum key fusion-based virtual power plant security communication includes: identity authentication, performing identity authentication between a client and a server in a virtual power plant based on a communication requirement to acquire a root key; key distribution: generating a key encryption key and a message authentication key based on the acquired root key and performing negotiation on a data encryption key to obtain the data encryption key; and data encryption: encrypting to-be-encrypted data using the data encryption key, and implementing communication of the data. During the identity authentication or the key distribution, negotiation on a quantum key is performed by a quantum key server, and the quantum key obtained by the negotiation is used for implementing the identity authentication or used as the data encryption key. A device for quantum key fusion-based virtual power plant security communication and a computer storage medium are provided.