Abstract: Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.

Abstract: Hierarchical scanning begins with communicating probes over the Internet to ports and networks addresses to determine publicly accessible devices. Based on responses to those probes, follow-up probes are determined to obtain additional information about the publicly accessible devices. The probes are transmitted from a system that is external to the networks corresponding to the network addresses. This provides an external view of the scanned networks and facilitates a probing paradigm that scales beyond a few networks.

Abstract: Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.

Abstract: Techniques for top-down scene generation are discussed. A generator component may receive multi-dimensional input data associated with an environment. The generator component may generate, based at least in part on the multi-dimensional input data, a generated top-down scene. A discriminator component receives the generated top-down scene and a real top-down scene. The discriminator component generates binary classification data indicating whether an individual scene in the scene data is classified as generated or classified as real. The binary classification data is provided as a loss to the generator component and the discriminator component.

Abstract: Techniques relating to training a model for detecting that a vehicle is likely to perform a cut-in maneuver are described. Computing device(s) can receive log data associated with vehicles in an environment and can detect an event in the log data, wherein an event corresponds to a cut-in maneuver performed by a vehicle. In an example, the computing device(s) can generate training data based at least in part on converting a portion of the log data that corresponds to the event into a top-down representation of the environment and inputting the training data into a model, wherein the model is trained to output an indication of whether another vehicle is likely to perform another cut-in maneuver.

Abstract: Hierarchical scanning begins with communicating probes over the Internet to ports and networks addresses to determine publicly accessible devices. Based on responses to those probes, follow-up probes are determined to obtain additional information about the publicly accessible devices. The probes are transmitted from a system that is external to the networks corresponding to the network addresses. This provides an external view of the scanned networks and facilitates a probing paradigm that scales beyond a few networks.

Abstract: Techniques for top-down scene generation are discussed. A generator component may receive multi-dimensional input data associated with an environment. The generator component may generate, based at least in part on the multi-dimensional input data, a generated top-down scene. A discriminator component receives the generated top-down scene and a real top-down scene. The discriminator component generates binary classification data indicating whether an individual scene in the scene data is classified as generated or classified as real. The binary classification data is provided as a loss to the generator component and the discriminator component.

Abstract: Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.

Abstract: A system for detecting network assets or attributes related to a network entity includes an input interface and a processor. The input interface is to receive a seed. The seed is associated with the network entity. The processor is to determine a first set of network assets or attributes associated with the seed and to determine a second set of network assets or attributes based at least in part on the first set of assets or attributes.

Abstract: Techniques relating to training a model for detecting that a vehicle is likely to perform a cut-in maneuver are described. Computing device(s) can receive log data associated with vehicles in an environment and can detect an event in the log data, wherein an event corresponds to a cut-in maneuver performed by a vehicle. In an example, the computing device(s) can generate training data based at least in part on converting a portion of the log data that corresponds to the event into a top-down representation of the environment and inputting the training data into a model, wherein the model is trained to output an indication of whether another vehicle is likely to perform another cut-in maneuver.

Abstract: Techniques are disclosed for re-executing a data processing pipeline following a failure of at least one of its components. The techniques may include a syntax for defining a compute graph associated with the data processing pipeline and receiving such a compute graph in association with a specific data processing pipeline. The technique may include executing the data processing pipeline, determining that a component of the data processing pipeline failed, and determining a portion of the data processing pipeline to execute/re-execute based at least in part on dependencies defined by the data processing pipeline in association with the failed component. Re-executing the one or more components may comprise retrieving an output saved in association with a component upon which the failed component depends.

Abstract: Hierarchical scanning begins with communicating probes over the Internet to ports and networks addresses to determine publicly accessible devices. Based on responses to those probes, follow-up probes are determined to obtain additional information about the publicly accessible devices. The probes are transmitted from a system that is external to the networks corresponding to the network addresses. This provides an external view of the scanned networks and facilitates a probing paradigm that scales beyond a few networks.

Abstract: Techniques relating to detecting that a vehicle is likely to enter a lane region in front of another vehicle is described. In an example, computing device(s) onboard a first vehicle can receive sensor data associated with an environment of the first vehicle. Based at least in part on an attribute determined from the sensor data, the computing device(s) can determine that a second vehicle proximate the first vehicle is predicted to enter a lane region in front of the first vehicle from a different direction of travel (e.g., by performing a u-turn, n-point turn, exiting a parting spot or driveway, etc.). In an example, the computing device(s) can determine an instruction for controlling the first vehicle based at least in part on the determining that the second vehicle is predicted to enter the lane region in front of the first vehicle.

Abstract: Techniques relating to training a model for detecting that a vehicle is likely to perform a cut-in maneuver are described. Computing device(s) can receive log data associated with vehicles in an environment and can detect an event in the log data, wherein an event corresponds to a cut-in maneuver performed by a vehicle. In an example, the computing device(s) can generate training data based at least in part on converting a portion of the log data that corresponds to the event into a top-down representation of the environment and inputting the training data into a model, wherein the model is trained to output an indication of whether another vehicle is likely to perform another cut-in maneuver.

Abstract: A system for detecting network assets or attributes related to a network entity includes an input interface and a processor. The input interface is to receive a seed. The seed is associated with the network entity. The processor is to determine a first set of network assets or attributes associated with the seed and to determine a second set of network assets or attributes based at least in part on the first set of assets or attributes.

Abstract: Techniques are disclosed for re-executing a data processing pipeline following a failure of at least one of its components. The techniques may include a syntax for defining a compute graph associated with the data processing pipeline and receiving such a compute graph in association with a specific data processing pipeline. The technique may include executing the data processing pipeline, determining that a component of the data processing pipeline failed, and determining a portion of the data processing pipeline to execute/re-execute based at least in part on dependencies defined by the data processing pipeline in association with the failed component. Re-executing the one or more components may comprise retrieving an output saved in association with a component upon which the failed component depends.

Abstract: A system for detecting network assets or attributes related to a network entity includes an input interface and a processor. The input interface is to receive a seed. The seed is associated with the network entity. The processor is to determine a first set of network assets or attributes associated with the seed and to determine a second set of network assets or attributes based at least in part on the first set of assets or attributes.

Abstract: A system for hierarchical scanning includes an interface and a processor. The interface is to receive an indication to scan using a payload; provide the payload to a set of addresses on a set of ports; and receive a set of responses. Each response is associated with an address and a port. The processor is to: for each response of the set of responses: determine whether a follow-up probe exists associated with the response; and in the event the follow-up probe exists associated with the response: execute the follow-up probe on the address and the port associated with the response; and store the set of data received in response to the follow-up probe in a database.

Abstract: A system for hierarchical scanning includes an interface and a processor. The interface is to receive an indication to scan using a payload; provide the payload to a set of addresses on a set of ports; and receive a set of responses. Each response is associated with an address and a port. The processor is to: for each response of the set of responses: determine whether a follow-up probe exists associated with the response; and in the event the follow-up probe exists associated with the response: execute the follow-up probe on the address and the port associated with the response; and store the set of data received in response to the follow-up probe in a database.

Abstract: A system for hierarchical scanning includes an interface and a processor. The interface is to receive an indication to scan using a payload; provide the payload to a set of addresses on a set of ports; and receive a set of responses. Each response is associated with an address and a port. The processor is to: for each response of the set of responses: determine whether a follow-up probe exists associated with the response; and in the event the follow-up probe exists associated with the response: execute the follow-up probe on the address and the port associated with the response; and store the set of data received in response to the follow-up probe in a database.