Systems and methods are provided for estimating travel time and distance. Such method may comprise obtaining a vehicle trip dataset comprising an origin, a destination, a time-of-day, a trip time, and a trip distance associated with each of a plurality of trips, and training a neural network model with the vehicle trip dataset to obtain a trained model. The neural network model may comprise a first module and a second module, the first module may comprise a first number of neuron layers, the first module may be configured to obtain the origin and the destination as first inputs to estimate a travel distance, the second module may comprise a second number of neuron layers, and the second module may be configured to obtain the information of a last layer of the first module and the time-of-day as second inputs to estimate a travel time.

Disclosed are a car-sharing service device and a method of operating the same capable of improving convenience of the use of a car-sharing service and increasing participation in the service by providing an environment in which targets requiring the car-sharing service are specified and the common use of a shared car is recommended to each of the specified targets.

A processor-implemented method includes storing, for each of multiple road sections, section-specific road-nonlinearity values relating to road-nonlinearity characteristics of the respective road section. The method includes receiving, from a user, user-desired route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The method further includes calculating, from the stored section-specific road-nonlinearity values, for a candidate route, one or one route-specific road-nonlinearity values relating to the road-nonlinearity characteristics. The candidate route's route-specific road-nonlinearity values are compared to the user-desired route-specific road-nonlinearity values. Based on results of the comparison, the candidate route is select to be a recommended route. The recommended route is communicated to the user.

Systems and methods for reconstructing a trajectory from anonymized data are provided. In some aspects, a method includes receiving anonymized data corresponding to a trajectory of a user or object, and assembling, based on the anonymized data, a state-space model. The method also includes executing a prediction algorithm, based on the state-space model, to generate predicted data from the anonymized data, and reconstructing the trajectory of the user or object using the predicted data. The method further includes generating a report indicative of the trajectory.

A method and a device for determining emergency trajectories. A method and a device for operating an automated vehicle are also described.

Systems, methods, computer-readable media, and apparatuses for providing hazard detection and broadcast functions are provided. In some examples, sensor data may be captured by a mobile device, vehicle, or the like. The data may be used to detect a hazard, identify a type of hazard, and the like. One or more users or groups of users for notification of the hazard may be identified and one or more notifications may be transmitted to users within the group.

The present disclosure relates to navigational systems, devices, and methods for preparing route guidance information that incorporates user needs—and ensures fulfilment of those needs under a variety of conditions. In particular, this disclosure relates to preparing route guidance information that ensures a user is directed along a route that includes stops where various needs such as e.g. medical, food, or water can be met while also minimizing traffic. Such a system may be useful, for example, when evacuating from a metro region in the face of an oncoming natural disaster such as a hurricane.

In an approach to improve mobile computation while traveling by dynamically generating one or more routes base on computing resource requirements of one or more endpoint devices. Embodiments identify, in real time, a plurality of autonomous vehicles, wherein the plurality of autonomous vehicles are traveling along a common route. Further embodiments, adjust, in real time, relative positions and speeds of the plurality of autonomous vehicles to maintain the plurality of autonomous vehicles within a predetermined geographic area while traveling along the common route, and wherein the predetermined geographic area is sufficient to collectively provide an amount of edge computing resources to satisfy one or more computing resource requirements of the one or more endpoint devices located within a first autonomous vehicle. Additionally, embodiments adjust, in real time, a route of the first autonomous vehicle based on the common route of the plurality of autonomous vehicles providing the edge computing resources.

A method and an apparatus for controlling personal mobility device (PMD) depending on congestion includes identifying a target area according to an expected route of the PMD, acquiring a degree of congestion of the target area based on at least one of a type of objects, a movement of the objects, a distance between objects, the number of objects that is expected, and a speed limit of the target area, and controlling speed of the PMD according to the degree of congestion.

Methods described herein relate to updating a map model. Methods may include receiving data from a vehicle pertaining to a location; comparing the received data pertaining to the location to historical data pertaining to the same location; and based at least in part upon a comparison of the received data pertaining to the location and the historical data pertaining to the same location, generating an internal model for the location. A corresponding apparatus and computer program product are also provided.