Abstract: Provided are systems, methods, and computer program products for monitoring, testing, or debugging transportation services, generating or transmitting an initiating message from a global manager cloud to an external service cloud, to invoke a transportation as a service (TaaS) message from external service clouds that comprise confirmation, also including generating or transmitting a simulated message from the global manager cloud to mirror the TaaS message, or a portion, transmitted on a TaaS link from the external service cloud to the on-vehicle modem, determining, a confidence threshold for a capability or security of the TaaS link, validating AV service data sent from the global manager cloud to a TaaS component in an on-vehicle black box of the autonomous vehicle system, validating AV compute data sent from the autonomous vehicle system to the TaaS component in the on-vehicle black box, validating TaaS message data received from the external service cloud.

Abstract: Methods and systems that use a phantom vehicle to help generate a planned path for a real-world vehicle are described. The system will identify a starting point and a destination for a trip of the real-world vehicle. The system will select, from the data store of vehicle profiles, a phantom vehicle having an associated motion planning system that corresponds to a system that is deployed on the real-world vehicle. The system will use a high definition map to generate a planned route for the real-world vehicle from the starting point to the destination in the map. The system will run a simulation in which the phantom vehicle moves along the planned route in the map. The system will then output a record of the simulation to a user of the real world-vehicle or to a system of the real-world vehicle.

Abstract: Methods and systems for generating a planned path for a vehicle are disclosed. Upon receiving a trip service request, a processor will access a data store containing multiple candidate motion planning systems, each of which is associated with at least one vehicle or fleet. The processor will identify a starting point and a destination for the trip service request, and it will use an identifier for the vehicle or its fleet to select, from the candidate systems, a motion planning system. The processor will use the functions of the selected motion planning system to generate candidate trajectories for the first vehicle from the starting point to the destination in a high definition map. The processor will select a planned route from the candidate trajectories, and it will output trip instructions to cause the vehicle to move along the planned route.

Abstract: Methods and systems for obtaining serviceable areas for a robotic system in a metropolitan area are described. A computing device obtains information about places where (i) the system can route to and from in the area and/or (ii) the system can stop in the area. The computing device uses the information to generate clusters of places where the robotic system can route or stop in the metropolitan area. The computing device creates a geometric shape for each cluster, wherein each shape which has a boundary defined by outermost places contained in the cluster. The computing device uses the geometric shapes to define the serviceable areas for the robotic system within the metropolitan area. The computing device uses the serviceable areas to generate a map displaying at least one geographic area representing a portion of the metropolitan area where a concentrated number of the places exist.

Abstract: Methods and systems for estimating a time of arrival for a vehicle at a destination are disclosed. The system will access an adjacency graph comprising nodes and edges. Each node is associated with a unique location in a geographic area in which the vehicle is traveling. Each edge connects two of the nodes and is associated with an estimated travel time between the two connected nodes. The system will select, from the locations in adjacency graph, a first location that is near the vehicle and a second location that is near the destination. The location and destination are each associated with nodes in adjacency graph. The system will calculate a shortest path along the edges in the adjacency graph from the location and destination nodes, and it will calculate an estimated time of arrival for the vehicle as a function of the estimated travel times along the shortest path.

Abstract: Systems and methods for managing permissions and authorizing access to a service supported by a computing device. The methods comprise by a computing device: intercepting a request to access the service sent along with a certificate including a first tenant identifier (the first tenant identifier identifying a first business entity other than a second business entity providing the service); using the first tenant identifier to obtain permission information from a datastore (the permission information specifying which resources of a plurality of resources can be returned in response to requests from the first business entity); generating a web authentication token including the first tenant identifier and the permission information; and initiating operations of the service in response to a validation of the web authentication token.

Abstract: Provided are systems, methods, and computer program products for monitoring, testing, or debugging transportation services, generating or transmitting an initiating message from a global manager cloud to an external service cloud, to invoke a transportation as a service (TaaS) message from external service clouds that comprise confirmation, also including generating or transmitting a simulated message from the global manager cloud to mirror the TaaS message, or a portion, transmitted on a TaaS link from the external service cloud to the on-vehicle modem, determining, a confidence threshold for a capability or security of the TaaS link, validating AV service data sent from the global manager cloud to a TaaS component in an on-vehicle black box of the autonomous vehicle system, validating AV compute data sent from the autonomous vehicle system to the TaaS component in the on-vehicle black box, validating TaaS message data received from the external service cloud.

Abstract: Devices, systems, and methods are provided for enhanced rider pairing of an autonomous vehicle (AV). A system may pair a first user profile of a first user located at a first location with a first autonomous vehicle (AV) to complete a trip to a destination selected by the first user. The system may detect a second AV at the first location, wherein the second AV is associated with a second user profile. The system may connect the second AV with the first user using a connection mechanism. The system may select a profile status of the first user profile based on the connection to the second AV. The system may pair the first user profile with the second AV based on the profile status.

Abstract: Described is a technology in which client content requests to a server over a wide area network (WAN) are responded to with hash information by which the client may locate the content among one or more peer sources coupled to the client via a local area network (LAN). The hash information may be in the form of a segment hash that identifies multiple blocks of content, whereby the server can reference multiple content blocks with a single hash value. Segment boundaries may be adaptive by determining them according to criteria, by dividing streamed content into segments, and/or by processing the content based on the content data (e.g., via RDC or content/application type) to determine split points. Also described is content validation using the hash information, including by generating and walking a Merkle tree to determine higher-level segment hashes in order to match a server-provided hash value.

Abstract: Described is a technology in which client content requests to a server over a wide area network (WAN) are responded to with hash information by which the client may locate the content among one or more peer sources coupled to the client via a local area network (LAN). The hash information may be in the form of a segment hash that identifies multiple blocks of content, whereby the server can reference multiple content blocks with a single hash value. Segment boundaries may be adaptive by determining them according to criteria, by dividing streamed content into segments, and/or by processing the content based on the content data (e.g., via RDC or content/application type) to determine split points. Also described is content validation using the hash information, including by generating and walking a Merkle tree to determine higher-level segment hashes in order to match a server-provided hash value.

Abstract: Described is a technology in which client content requests to a server over a wide area network (WAN) are responded to with hash information by which the client may locate the content among one or more peer sources coupled to the client via a local area network (LAN). The hash information may be in the form of a segment hash that identifies multiple blocks of content, whereby the server can reference multiple content blocks with a single hash value. Segment boundaries may be adaptive by determining them according to criteria, by dividing streamed content into segments, and/or by processing the content based on the content data (e.g., via RDC or content/application type) to determine split points. Also described is content validation using the hash information, including by generating and walking a Merkle tree to determine higher-level segment hashes in order to match a server-provided hash value.

Abstract: Described is a technology in which client content requests to a server over a wide area network (WAN) are responded to with hash information by which the client may locate the content among one or more peer sources coupled to the client via a local area network (LAN). The hash information may be in the form of a segment hash that identifies multiple blocks of content, whereby the server can reference multiple content blocks with a single hash value. Segment boundaries may be adaptive by determining them according to criteria, by dividing streamed content into segments, and/or by processing the content based on the content data (e.g., via RDC or content/application type) to determine split points. Also described is content validation using the hash information, including by generating and walking a Merkle tree to determine higher-level segment hashes in order to match a server-provided hash value.