The disclosure describes a method for an ego vehicle. The method includes determining that a vehicle needs to park. The method further includes identifying a parking facility within proximity to the vehicle and a corresponding congestion level of the parking facility. The method further includes selecting a first parking model from two or more parking models based on the corresponding congestion level being closer to a first congestion range than a second congestion range. The method further includes identifying a parking spot for the vehicle within the parking facility based on the first parking model.

A method includes receiving a request from a requesting vehicle to locate a parking spot having capacity to receive solar power, determining a parking location having the capacity to receive solar power, based on a location of the requesting vehicle, the request, and a parking map that indicates an expected charging rate at each of a plurality of parking locations, and transmitting the determined parking location to the requesting vehicle.

Systems and methods for determining autonomous vehicle user boarding times are provided. In one example embodiment, a computer implemented method includes obtaining location data associated with a user device associated with a user. The method includes determining an estimated time until the user starts boarding the autonomous vehicle based at least in part on the location data associated with the user device. The method includes obtaining data associated with the user. The method includes determining an estimated time of boarding duration for the user based at least in part on the data associated with the user. The method includes determining an estimated time until the user completes boarding of the autonomous vehicle based at least in part on the estimated time until the user starts boarding the autonomous vehicle and the estimated time of boarding duration for the user.

A system for uploading data from a main vehicle to a cloud server is presented. The system includes a first sensor, a second sensor, a communication unit, and an electronic control unit. The first sensor obtains geo localization information of the main vehicle. The second sensor monitors first and second predetermined micro characteristics of the environment of the main vehicle. The communication unit transmits a report to an external cloud server and receives macro environment information indicating a predetermined macro characteristic of a geographic region in which the main vehicle is traveling. The electronic control unit repeatedly generates reports by collecting and buffering the first micro characteristic and transmits the reports in predetermined time intervals. The time intervals may be modified as a function of the second micro characteristic and the macro environment information.

A method for operating a vehicle hazardous parking warning system for warning a user of a first vehicle about hazardous parking of the first vehicle. The method includes detecting that the first vehicle enters a parking state at a parking position along the roadside; determining whether the parking position of the first vehicle is hazardous in terms of risk that the parked first vehicle being hit by a second, rear-coming, vehicle by retrieving stored forwards visibility information that was registered by a forwards directed sensor unit of the first vehicle while travelling of the first vehicle before entering the parking state; calculating a level of a risk parameter reflecting a risk for the parked first vehicle being hit by the second vehicle, based on the forwards visibility information; and determining that the parking position is a hazardous parking position when the risk parameter exceeds a threshold value.

A parking-stopping point management device includes a determination criterion acquisition unit configured to acquire at least one of vehicle behavior data indicative of a behavior of at least one vehicle and sensing information of a surrounding monitoring sensor mounted in the at least one vehicle in association with position information of the at least one vehicle; a parking-stopping point detection unit configured to detect, as a parking-stopping point, a street parking point which is a point where the parking-stopping vehicle is parked on a normal road; an existence state determination unit configured to determine whether the parking-stopping vehicle still exists at the street parking point detected by the parking-stopping point detection unit based on the information acquired by the determination criterion acquisition unit; and a distribution processing unit configured to distribute information on the street parking point detected by the parking-stopping point detection unit to the at least one vehicle.

A parking control system of a parking facility, the parking control system including: an acquisition unit configured to acquire behavior information on a user; an estimation unit configured to, based on the behavior information, estimate a probability of vehicle exit performed by the user; and an execution unit configured to, based on the probability, perform a process related to vehicle exit in the parking facility.

The present disclosure provides a method for managing a parking lot in a smart city based on an Internet of Things, which is executed by a management platform. The method comprises obtaining a user position of a user platform based on a service platform, determining a candidate parking lot that meets a preset condition; determining time when a vehicle to be parked arrives at the candidate parking lot based on the user position; determining free parking space information when the vehicle to be parked arrives at the candidate parking lot; determining recommendation information based on the free parking space information; and sending the recommendation information to the user platform based on the service platform.

A method for automatically learning parking preferences for a driver and generating parking recommendations includes generating training data based at least in part on: 1) trajectory data indicating a trajectory of a vehicle during a plurality of parking events, each in which a driver of the vehicle selected a parking candidate from among a plurality of parking candidates, and 2) attribute data indicating attributes of each of the plurality of parking candidates, removing, from the training data, data associated with at least one available parking candidate based on one or more conditions that indicate the driver did not consider the at least one available parking candidate, and training a decision model, based on remaining training data, to estimate a preferred parking candidate for the driver from among a set of available parking candidates.

A system for providing information to navigate to a parking space preferred by an operator of a vehicle can include a processor and a memory. The memory can store a preliminary operations module and a communications module. The preliminary operations module can include instructions that cause the processor to obtain, for the operator: (1) a first model for a first duration between a commencement of a parking space identification process and a first threshold and (2) a second model for a second duration between the first threshold and a second threshold. The communications module can include instructions that cause the processor to communicate, to a component of the vehicle for an action, information to navigate to the parking space. The parking space can be identified, in response to a current time being within: (1) the first duration, by the first model and (2) the second duration, by the second model.