Abstract: An example operation includes one or more of traveling, by a first transport, in a first lane, determining, by the first transport, that a speed of a second transport is greater than a speed of the first transport when the second transport is behind the first transport, determining, by the first transport, that no other transports are ahead of the first transport by a first distance in the first lane and beside the first transport by a second distance in a second lane, maneuvering, by the first transport, to the second lane allowing the second transport to pass the first transport in the first lane, and maneuvering, by the first transport, to the first lane when there are no other transports traveling in the first lane at a third distance behind the first transport and at or near the speed of the second transport.

Abstract: An example operation includes one or more of obtaining, by a moving vehicle, a first data including one or more of at least one first video and at least one first image of a moving transport, analyzing the first data, by the moving vehicle, to determine an initial issue, querying, by the moving vehicle, a server for a second data based on the analyzing, wherein the second data is one or more of at least one second video and at least one second image of the moving transport at a previous time, verifying, by the moving vehicle, an issue exists based on a delta above a threshold between the first data and the second data, and sending, by the moving vehicle, the verified issue to the server.

Abstract: An example operation includes one or more of obtaining, by a moving vehicle, a first data including one or more of at least one first video and at least one first image of a moving transport, analyzing the first data, by the moving vehicle, to determine an initial issue, querying, by the moving vehicle, a server for a second data based on the analyzing, wherein the second data is one or more of at least one second video and at least one second image of the moving transport at a previous time, verifying, by the moving vehicle, an issue exists based on a delta above a threshold between the first data and the second data, and sending, by the moving vehicle, the verified issue to the server.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: An example operation includes one or more of detecting, by a processor of a transport, an exit on a road, calculating, by the processor of the transport, a probability that the transport is not prepared to exit, requesting, by the processor of the transport, at least one other transport proximate to the transport to alter its speed if the probability exceeds a threshold, and responsive to a detecting of an altering of the speed by the at least one other transport, triggering the transport to exit the road.

Abstract: An example operation includes one or more of obtaining, by a moving vehicle, a first data including one or more of at least one first video and at least one first image of a moving transport, analyzing the first data, by the moving vehicle, to determine an initial issue, querying, by the moving vehicle, a server for a second data based on the analyzing, wherein the second data is one or more of at least one second video and at least one second image of the moving transport at a previous time, verifying, by the moving vehicle, an issue exists based on a delta above a threshold between the first data and the second data, and sending, by the moving vehicle, the verified issue to the server.

Abstract: An example operation includes one or more of obtaining, by a moving vehicle, a first data including one or more of at least one first video and at least one first image of a moving transport, analyzing the first data, by the moving vehicle, to determine an initial issue, querying, by the moving vehicle, a server for a second data based on the analyzing, wherein the second data is one or more of at least one second video and at least one second image of the moving transport at a previous time, verifying, by the moving vehicle, an issue exists based on a delta above a threshold between the first data and the second data, and sending, by the moving vehicle, the verified issue to the server.

Abstract: A method for determining actual wear and tear of fleet cars is provided. The method includes at a cloud server comprising a processor, a memory and a database, receiving, from a first fleet car, first data streams generated by a first group of sensors mounted on the first fleet car, receiving from a second fleet car second data streams generated by a second group of sensors mounted on the second fleet car, and determining, with the processor, actual wear and tear of the first fleet car and the second fleet car based on the first wear and tear element and the second wear and tear element.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: A vehicle motion adaptation system includes a processor and a memory coupled to the processor and storing a machine learning program. The machine learning program, upon execution by the processor, performs at least the following operations of (i) determining a selective set of data points to be stored on board, (ii) storing the selected set of data points based on the determination, (iii) determining a learned response to one or more driving events based on the selected set of data points stored onboard, and (iv) adapting a motion of a vehicle based on the learned response.

Abstract: An example operation includes one or more of traveling, by a first transport, in a first lane, determining, by the first transport, that a speed of a second transport is greater than a speed of the first transport when the second transport is behind the first transport, determining, by the first transport, that no other transports are ahead of the first transport by a first distance in the first lane and beside the first transport by a second distance in a second lane, maneuvering, by the first transport, to the second lane allowing the second transport to pass the first transport in the first lane, and maneuvering, by the first transport, to the first lane when there are no other transports traveling in the first lane at a third distance behind the first transport and at or near the speed of the second transport.

Abstract: An example operation includes one or more of obtaining, by a moving vehicle, a first data including one or more of at least one first video and at least one first image of a moving transport, analyzing the first data, by the moving vehicle, to determine an initial issue, querying, by the moving vehicle, a server for a second data based on the analyzing, wherein the second data is one or more of at least one second video and at least one second image of the moving transport at a previous time, verifying, by the moving vehicle, an issue exists based on a delta above a threshold between the first data and the second data, and sending, by the moving vehicle, the verified issue to the server.

Abstract: An example operation includes one or more of detecting, by a processor of a transport, an exit on a road, calculating, by the processor of the transport, a probability that the transport is not prepared to exit, requesting, by the processor of the transport, at least one other transport proximate to the transport to alter its speed if the probability exceeds a threshold, and responsive to a detecting of an altering of the speed by the at least one other transport, triggering the transport to exit the road.

Abstract: An example operation includes one or more of traveling, by a first transport, in a first lane, determining, by the first transport, that a speed of a second transport is greater than a speed of the first transport when the second transport is behind the first transport, determining, by the first transport, that no other transports are ahead of the first transport by a first distance in the first lane and beside the first transport by a second distance in a second lane, maneuvering, by the first transport, to the second lane allowing the second transport to pass the first transport in the first lane, and maneuvering, by the first transport, to the first lane when there are no other transports traveling in the first lane at a third distance behind the first transport and at or near the speed of the second transport.

Abstract: An example operation may include one or more of connecting, by a processor of a transport, to a network comprised of a plurality of transports, receiving, by the processor of the transport, an access key request to the transport from a device, broadcasting, by the processor of the transport, the request to the network, collecting, by the processor of the transport, authorizations from a subset of the plurality of the transports, and sending a one-time access key to the device, if the authorizations are received from a minimal required number of the transports.

Abstract: An example operation may include one or more of requesting, by a processor of a device, permissions to provide access to a transport to a diagnostic location from a network of a plurality of diagnostic locations, in response to receiving the permissions from the network of the plurality of the diagnostic locations, authenticating the diagnostic location, by the device, providing a pre-stored one-time access key to the transport to the diagnostic location, and receiving, by the device, diagnostics from the diagnostic location.

Abstract: An example operation may include one or more of connecting, by a processor of a transport, to a network comprised of a plurality of transports, receiving, by the processor of the transport, an access key request to the transport from a device, broadcasting, by the processor of the transport, the request to the network, collecting, by the processor of the transport, authorizations from a subset of the plurality of the transports, and sending a one-time access key to the device, if the authorizations are received from a minimal required number of the transports.

Abstract: A vehicle motion adaptation system includes a processor and a memory coupled to the processor and storing a machine learning program. The machine learning program, upon execution by the processor, performs at least the following operations of (i) determining a selective set of data points to be stored on board, (ii) storing the selected set of data points based on the determination, (iii) determining a learned response to one or more driving events based on the selected set of data points stored onboard, and (iv) adapting a motion of a vehicle based on the learned response.

Abstract: A method for determining actual wear and tear of fleet cars is provided. The method includes at a cloud server comprising a processor, a memory and a database, receiving, from a first fleet car, first data streams generated by a first group of sensors mounted on the first fleet car, receiving from a second fleet car second data streams generated by a second group of sensors mounted on the second fleet car, and determining, with the processor, actual wear and tear of the first fleet car and the second fleet car based on the first wear and tear element and the second wear and tear element.