Systems and methods for determining the performance of a driver of a vehicle based on changes, over time, in the context and environment in which the vehicle operates, and any resultant driver behavior are disclosed. A set of driver response data is created from based on an analysis of time-series data indicative of the driver's operation of the vehicle in conjunction with time-series data indicative of changes in the vehicle's context/environment. The driver response data indicates the types and magnitudes of the driver's responses to various changes in the vehicle's operating context/environment and the driver's time-to-respond for each of the responses. That is, the driver response data indicates how a driver compensated his or her behavior (if at all) in response to different changes in the vehicle's context and/or environment. The driver response data may be compared to one or more thresholds to determine the driver's performance.

Systems, methods, and computer readable media for performing task assignment, completion, and management within a crowdsourced surveillance platform. A remote server may identify targets for image capture and may assign capture tasks to users based on travel plans of the user. Users may be assigned task to capture image of target locations lying along a travel path. The remote server may aggregate data related to the captured images and use it to update a map and log changes to the target location over time.

A system, a method, and a computer program product may be provided for generating traffic congestion estimation data of one or more lanes in a region. A system may include a memory configured to store computer program code and a processor configured to execute the computer program code to obtain image data associated with the region. The processor may be configured to determine a count of one or more first movable objects in one or lanes, based on image data, calculate a lane object static capacity of the one or more lanes, based on one or more map free flow or speed limit attributes associated with the one or more lanes and generate the traffic congestion estimation data based on count of first movable objects in the one or more lanes, the moving speed of movable objects crossing multiple image frames, the lane object static capacity of lanes.

A method, apparatus and computer program product are provided for predicting a state of visibility for a road object. For example, at least one processor receives road sign attribute data indicating at least one attribute of a road sign. The processor further receives weather forecast data indicating a weather forecast of a location in which the road sign is disposed, and using the road sign attribute data and the weather forecast data, a state of visibility for the road sign is identified.

A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

Examples described herein relate to apparatuses and methods for or simulating and improving performance of an artificial intelligence (AI) driver, including but not limited to generating sensor data corresponding to a virtual environment, generating a pixelated image corresponding to the virtual environment based on the sensor data, determining actuator commands responsive to pixels in the pixelated image, wherein the decision module determines the actuator commands based on the AI driver, and simulating behaviors of the ego vehicle object using the actuator commands.

In an example, a method may assign a first drone of a drone network a first task, the first task may instruct the first drone to transport a first package to a first destination in a geographic area. The method may receive roadway traffic data for a plurality of roadway vehicles in the geographic area; determine, based on the roadway traffic data and during transit of the first package to the first destination by the first drone, to transfer the first package to a second drone in the drone network; and transfer the first package to the second drone in the drone network.

The invention provides systems and methods for an Intelligent Road Infrastructure System (IRIS), which facilitates vehicle operations and control for connected automated vehicle highway (CAVH) systems. IRIS systems and methods provide vehicles with individually customized information and real-time control instructions for vehicle to fulfill the driving tasks such as car following, lane changing, and route guidance. IRIS systems and methods also manage transportation operations and management services for both freeways and urban arterials. In some embodiments, the IRIS comprises or consists of one of more of the following physical subsystems: (1) Roadside unit (RSU) network, (2) Traffic Control Unit (TCU) and Traffic Control Center (TCC) network, (3) vehicle onboard unit (OBU), (4) traffic operations centers (TOCs), and (5) cloud information and computing services. The IRIS manages one or more of the following function categories: sensing, transportation behavior prediction and management, planning and decision making, and vehicle control. IRIS is supported by real-time wired and/or wireless communication, power supply networks, and cyber safety and security services.

Techniques are provided to enable quick previews of what a modified document would look like. In an implementation, a set of page images are stored. Each page image represents a page of a document, the page having been converted to a page image for a first version of the document to permit the document to be viewed in a viewer program. A command is received to modify the document. The requested modification may include, for example, reordering pages, deleting pages, or extracting pages. A preview is generated for a second version of the document. The preview reflects the modification and uses at least one page image from the set of page images created for the first version of the document. Reusing page images allows the preview to be generated very quickly.

Graphs are powerful structures made of nodes and edges. Information can be encoded in the nodes and edges themselves, as well as the connections between them. Graphs can be used to create manifolds which in turn can be used to efficiently train more robust AI systems. Systems and methods for graph-based AI training in accordance with embodiments of the invention are illustrated. In one embodiment, a graph interface system including a processor, and a memory configured to store a graph interface application, where the graph interface application directs the processor to obtain a set of training data, where the set of training data describes a plurality of scenarios, encode the set of training data into a first knowledge graph, generate a manifold based on the first knowledge graph, and train an AI model by traversing the manifold.