System

A system comprising:

• a communication interface including a network interface controller coupled to a packet-switched network and configured to transmit and receive data packets;
• a memory storing instructions, a recurrent neural network model, a generative neural network model comprising a transformer architecture with a plurality of self-attention layers, and a scheduling data structure;
• and
• circuitry comprising one or more processors coupled to the memory and configured to execute the instructions to:
• receive, via the communication interface, geolocation data streams from a plurality of terminal devices and time-series demand data from at least one data source;
• preprocess the geolocation data streams into spatial density maps and input the spatial density maps together with the time-series demand data to the recurrent neural network model to compute predicted demand values for a plurality of spatial regions;
• generate a sequence of waypoint coordinates and dwell-time parameters for an autonomous mobile unit based on the predicted demand values and real-time traffic data, and transmit the sequence to the autonomous mobile unit via the communication interface;
• construct a prompt data structure encoding statistical features and prediction error metrics, transmit the prompt data structure to the generative neural network model, and receive parameter adjustment data for updating the recurrent neural network model;
• allocate time-slot entries in the scheduling data structure, each associating a terminal device identifier with a waypoint coordinate and a time interval;
• and
• generate cryptographic authentication tokens for controlling access to a compartment of the autonomous mobile unit.

A method performed by circuitry of a server coupled to a packet-switched network via a communication interface, the method comprising:

• receiving, via the communication interface, geolocation data streams from a plurality of terminal devices and time-series demand data from at least one data source coupled to the packet-switched network;
• inputting the geolocation data streams and the time-series demand data to a recurrent neural network to compute predicted demand values for each of a plurality of spatial regions over a prediction horizon;
• generating, based on the predicted demand values and real-time traffic data received via the communication interface, a sequence of waypoint coordinates and dwell-time parameters for an autonomous mobile unit comprising a LiDAR sensor array, an image sensor, and a satellite positioning receiver;
• transmitting the sequence of waypoint coordinates and the dwell-time parameters to the autonomous mobile unit via the communication interface;
• constructing a prompt data structure encoding statistical features of the geolocation data streams and prediction error metrics, transmitting the prompt data structure to a generative neural network model, and receiving from the generative neural network model parameter adjustment data for updating weights of the recurrent neural network;
• and
• allocating time-slot entries in a scheduling data structure stored in a storage device coupled to the packet-switched network, each time-slot entry associating a terminal device identifier with a waypoint coordinate and a time interval, and generating cryptographic authentication tokens for controlling access to a compartment of the autonomous mobile unit.