Systems and methods for providing customer navigation include a system provider device that may receive a query for directions to a first merchant physical location. Responsive to receiving the query, the system provider device determines a first travel time to the first merchant physical location and a second travel time to a second merchant physical location. The system provider device further determines a first wait time at the first merchant physical location and a second wait time at the second merchant physical location. Additionally, the system provider device determines that a first total time using the first travel time and the first wait time is shorter than a second total time using the second travel time and the second wait time. Based on determining that the first total time is shorter than the second total time, the system provider device may provide navigation to the first merchant physical location.

Map-based security authentication challenges are disclosed. A user authentication method includes prompting a user to select a past route the user traveled for authentication in response to a request to access a banking computing resource. A map corresponding to the past route is transmitted to a mobile device of the user for display on the mobile device. The user is requested to trace the past route on the map. A machine learning model is invoked to compute a similarity score between the past route and a map tracing received in response to the request the user trace the past route. The method verifies a user identity, and when the similarity score satisfies a predetermined.

A boarding/deboarding point providing system includes a probe vehicle group, a data server, and an on-board unit or a mobile terminal. When vehicle data transmitted from the probe vehicle group are input, the data server extracts a boarding/deboarding point suitable for boarding a vehicle or deboarding a vehicle from the vehicle data, and stores the point while updating the boarding/deboarding point information database. When a request for boarding/deboarding point information is received from the on-board unit or the mobile terminal, the boarding/deboarding point information stored in the boarding/deboarding point information database is searched for a boarding/deboarding point that meets the request conditions, and the search result that includes the boarding/deboarding point is transmitted to the on-board unit or the mobile terminal.

A vehicle information DB holds information on whether or not a vehicle falls under a welfare vehicle provided with an assistance apparatus for a driver who has a lower limb impairment. A power feed facility DB holds position information of a power feed facility and information indicating whether the power feed facility falls under a contact type power feed facility or a wireless power feed facility. A processing apparatus creates a travel route such that a larger number of wireless power feed facilities are included along the travel route when the vehicle falls under the welfare vehicle than when the vehicle does not fall under the welfare vehicle.

A database comprising data associated with one or more routes is maintained. The data associated with the one or more routes comprises difficulty level data for utilizing one or more mobility assistive tools to traverse the one or more routes. In response to receiving a query from a given computing device, one or more amounts of physical exertion for a given user to traverse at least a portion of the one or more routes utilizing a given mobility assistive tool are predicted. One or more routes for the given user to traverse are selected based at least in part on the predicted amounts of physical exertion. One or more contextual factors of the given user are estimated to at least one of optimize and prioritize the selected one or more routes for the given user based on analyzing user data.

This disclosure describes systems and methods for personalized route prediction. An example method may include receiving, at a first time, first input data associated with a first route traversed by a vehicle. The example method may also include populating a first database with the input data. The example method may also include receiving, at a third time, second input data associated with a second route traversed by the vehicle. The example method may also include comparing the second input data to the first input data included within the first database. The example method may also include determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data. The example method may also include populating a second database based on the first cluster or the second cluster. The example method may also include determining, using the first database and at a second time, at least one of: predicted departure data, predicted destination data, and/or predicted route data. The example method may also include causing, based on the predicted departure data, predicted destination data, and/or predicted route data, to perform an action in association with the vehicle.

Various technologies described herein pertain to routing autonomous vehicles based upon spatiotemporal factors. A computing system receives an origin location and a destination location of an autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a spatiotemporal statistical model. The spatiotemporal statistical model is generated based upon historical data from autonomous vehicles when the autonomous vehicles undergo operation-influencing events. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of the autonomous vehicle. The spatiotemporal statistical model outputs a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along a candidate route. The autonomous vehicle then follows the route from the origin location to the destination location.

A vehicle and a method of controlling the same are provided. The vehicle may include a storage configured to store map information and store information, an inputter configured to receive an input of a destination and an item list from a user, and a controller configured to generate a traveling route of the vehicle based on the stored map information and the received destination, and determine at least one store of the plurality of stores that is located within a predetermined distance with respect to the generated travelling route and sells at least one item included in the received list of items, wherein the controller modifies the generated travelling route such that the vehicle passes through at least one store of the plurality of stores based on a location of at least one store of the plurality of stores.

This disclosure relates to method and system for providing personalized driving or navigation assistance. The method may include receiving sensory data with respect to a vehicle from a plurality of sensors and multi-channel input data with respect to one or more passengers inside the vehicle from a plurality of onboard monitoring devices, performing fusion of the sensory data and the multi-channel input data to generate multimodal fusion data, determining one or more contextual events based on the multi-modal fusion data using a machine learning model, wherein the machine learning model is trained using an incremental learning process and comprises a supervised machine learning model and an unsupervised machine learning model, analysing the one or more contextual events to generate a personalized driving recommendation, and providing the personalized driving recommendation to a driver passenger or a navigation device.

A system includes a processor configured to receive requirement values, from a vehicle-providing entity, for a plurality of predefined tunable routing parameters. The processor is also configured to select and assign entity-associated, routing parameter consideration-levels, based on the values correlated to a predefined schema of consideration-levels for each parameter. The processor is further configured to receive a plurality of pick-up requests and determine, using the entity-associated routing parameter consideration-levels, a routing-plan for a plurality of vehicles to service the requests such that the received values are met.