A simulating method for an electric vehicle (EV) includes creating a simulation model associating a plurality of target behaviors of a target vehicle with the EV, obtaining a plurality of vehicle parameters of the EV to generate a set of EV control parameters, obtaining a plurality of configuration parameters of the target vehicle, based on the set of EV control parameters and the plurality of configuration parameters of the target vehicle, using the simulation model to provide a set of simulated target-vehicle controls, where the simulation model is a neural network trained to reflect a relationship between the set of EV control parameters and the set of simulated target-vehicle controls, and outputting the set of simulated target-vehicle controls to the EV, such that the EV is controlled to achieve the plurality of target behaviors of the target vehicle based on the set of simulated target-vehicle controls.

Disclosed are systems, methods, and non-transitory computer-readable media for automated fleet tracking. A route management system enables fleet managers to define and assign routes for vehicles in a fleet, as well as set route tracking configurations for customized tracking of the vehicles. For example, the route tracking configuration may include customizations to the scheduled start and/or end time of a route, a threshold for determining that a vehicle has arrived and/or departed from a scheduled stop, and the like.

Disclosed are systems, methods, and non-transitory computer-readable media for automated fleet tracking. A route management system enables fleet managers to define and assign routes for vehicles in a fleet, as well as set route tracking configurations for customized tracking of the vehicles. For example, the route tracking configuration may include customizations to the scheduled start and/or end time of a route, a threshold for determining that a vehicle has arrived and/or departed from a scheduled stop, and the like.

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

A network system uses Wi-Fi signals or other types of short-range transmissions to determine pickup locations for users receiving services provided via the network system. The network system builds a database of search records mapping pickup locations to signatures of short-range transmission detected by users' client devices when they searched for the pickup locations. By comparing a signature detected by a given user's client device to the signatures in the database, the network system can check for similarities between the short-range transmissions. Responsive to finding a match, the network system predicts that the given user is likely to select a similar pickup location as other users whose client devices detected the signatures corresponding to the match. Accordingly, by leveraging the database, the network system can predict pickup locations without requiring the given user to input a search for a pickup location.

A network system uses Wi-Fi signals or other types of short-range transmissions to determine pickup locations for users receiving services provided via the network system. The network system builds a database of search records mapping pickup locations to signatures of short-range transmission detected by users' client devices when they searched for the pickup locations. By comparing a signature detected by a given user's client device to the signatures in the database, the network system can check for similarities between the short-range transmissions. Responsive to finding a match, the network system predicts that the given user is likely to select a similar pickup location as other users whose client devices detected the signatures corresponding to the match. Accordingly, by leveraging the database, the network system can predict pickup locations without requiring the given user to input a search for a pickup location.

An example operation may include one or more of detecting a potential event via sensors on a transport, sending data related to the potential event to other transports within a predefined distance, storing the data at the transports and a server, and performing a transport operation response on the transports.

An example operation may include one or more of detecting a potential event via sensors on a transport, sending data related to the potential event to other transports within a predefined distance, storing the data at the transports and a server, and performing a transport operation response on the transports.

An information processing device includes a control unit configured to: acquire information on a destination of a user who uses a resting unit including equipment on which the user is able to sleep, information on an arrival time at the destination, and information on a place in which the user uses the resting unit; transmit a command to a traveling unit configured to be coupled to the resting unit and carry the resting unit such that the traveling unit is coupled to the resting unit and arrives at the destination at the arrival time; and transmit a command to a stimulation device configured to stimulate the user who is using the resting unit such that the stimulation device is activated before the traveling unit is coupled to the resting unit or before the traveling unit departs for the destination after being coupled to the resting unit.