A transportation request processing device includes: a memory; and a processor coupled to the memory, and configured to: receive, from a terminal of an orderer, a transportation request to transport a target person or object to a destination by a vehicle traveling by remote driving and to deliver the target person or object to a recipient, divide a task corresponding to the transportation request into a remote driving task of causing the vehicle to travel by remote driving, and a safety check task of checking a periphery of the vehicle when delivering the target person or object to the recipient at the destination, and transmit information of the remote driving task to a first terminal of a first contractor who will undertake the remote driving task, and transmit information of the safety check task to a second terminal of a second contractor who will undertake the safety check task.

The disclosure relates to predicting a demand based on a time-series prediction. In an embodiment, a method is disclosed which includes generating a predicted metric for a future time period; loading a plurality of rules, a given rule in the plurality of rules mapping an amount of the predicted metric to a demand amount; applying the plurality of rules to the predicted metric to compute a set of demand amounts; aggregating the set of demand amounts; and generating a predicted demand based on the aggregated demand amounts.

An autonomous driving method, an autonomous driving apparatus, a computer-readable storage medium, and a computer program product are provided. The method includes: receiving first driving-related information of a first road section ahead of a road on which a first vehicle currently drives and information about a parking waiting area that are sent by a network side device, where the parking waiting area is used to park the first vehicle before the first vehicle drives into the first road section (S304); then determining, based on the first driving-related information, that the first road section does not meet an autonomous driving condition requirement of the first vehicle (S305); finally, controlling the first vehicle to drive into the parking waiting area (S306).

In some embodiments, a method can include receiving a set of job descriptions and a set of candidate profiles. Each job description is associated with a first subset of candidate profiles from the set of candidate profiles. The method can further include executing a model to identify, from the first subset of candidate profiles, a second subset of candidate profiles that satisfy a fit metric and a third subset of candidate profiles that does not satisfy the fit metric. The method can further include calculating a bias metric based on a true positive value, a false positive value, a true negative value, and a false negative value that were calculated based on auditing the second subset of candidate profiles and the third subset of candidate profiles. The method can further include updating the set of job descriptions based on the bias metric.

Systems and methods for dynamically reprioritizing pick jobs in a fulfillment center are described herein. The example systems can be configured to periodically classify pick jobs in order to optimize throughput of the fulfillment center. The classifying can include determining an estimated completion time for pick jobs and identifying at-risk jobs that may complete after their associated due dates. The at-risk jobs can be assigned to autonomous vehicles based primarily on their associated due dates. Other pick jobs that are not at-risk can be assigned to autonomous vehicles based primarily on efficiency. The at-risk pick jobs can be assigned to autonomous vehicles before the other pick jobs.

A timeline generation and solving tool is provided to parameterize a project at an application lifecycle management (“ALM”) tool, and generate and solve a timeline of features according to the parameterized project. The Scrum master may re-parameterize the project, causing a new timeline to be generated, solved, and visualized. In this fashion, the Scrum master may rapidly prototype many project parameterizations, resulting in many possible timelines. Without the use of a timeline generation and solving tool which interfaces with an ALM tool, retrieves project features, and converts project features to a common format suitable for computations based on working speeds, a Scrum master would need to generate and check a timeline by hand, and work indirectly by manually copying data out of the ALM tool, leading to a slow and potentially error-prone project roadmapping process which does not permit rapid prototyping of possible timelines.

Dynamically enabled task delegation controls are provided through a user interface executed on a computing device associated with a member. The task delegation controls are selectively enabled by a task facilitation service that determines whether the delegation controls should be made available to the member based on one or more models, algorithms, etc., associated with the member based on a predicted likelihood that the member will delegate the task for completion by the task facilitation service.

Provided is a process including: receiving a tank-nearly-full message indicating that a tank at an oil or gas related facility is or will be ready for a truck to unload and transport fluid accumulating in the tank; in response to the tank-nearly-full message, creating a tank-run record; sending a description of the tank-run to a mobile device of a driver of an oilfield truck; receiving, from the mobile device of the driver of the oilfield truck, a tank-run claimed message indicating that the driver will drive to the oil or gas related facility and transport at least some of the fluid accumulating in the tank; and after the tank-run claimed message, performing steps including: confirming that the tank-run has not yet been claimed; after the confirmation, designating the tank-run as claimed by the driver; and sending confirmation to the mobile device of the driver.

Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.

A subsequent meeting related to a previous meeting is scheduled, e.g., as a follow-up or forward-to meeting. A session recording of the previous meeting has been generated. Historical data of the previous meeting, e.g., identifiers of attendees, is used to populate fields of an invite form for scheduling the subsequent meeting, along with an indication of the session recording. In some embodiments, the identifiers of the invitees of the subsequent meeting are selected, without user input: from the identifiers of the attendees of the previous meeting, based on a relationship of the invitees of the subsequent meeting to the attendees of the previous meeting, or based on a relationship of the invitees of the subsequent meeting to a subject of the previous meeting. In some embodiments, the session recording of the previous meeting can be presented for review prior to a start time of the subsequent meeting.