A method for performing an at least partially automatic vehicle function of a vehicle depending on a travel route to be assessed by means of a driver assistance system is disclosed. The method comprises providing a plurality of clusters from route data with respect to at least one known travel route, wherein the clusters group the route data sectionwise according to predefined geometric parameters. The method comprises providing recorded course data that indicate a course of the travel route to be assessed and applying the clusters to the course data in order to divide the travel route to be assessed into route sections corresponding to the clusters. The method comprises determining at least one uncertainty quantity which is characteristic of an uncertainty with respect to the assignment made and determining a control quantity as a function of the uncertainty quantity and providing the control quantity for performing the vehicle function.

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 method determines an anticipated occupation of charging points and a charging strategy for a specified route. The method provides traffic data which is representative for the current traffic density on the route specified. An anticipated occupation of charging points along the specified route can be determined on the basis of the traffic data. A charging strategy can be determined on the basis of the traffic data and the determined anticipated occupation of charging points. The provision of information regarding a charging strategy to a driver allows the time required for the specified route to be reduced.

A parking assist apparatus is configured to: detect a level difference existing around the vehicle; determine whether the detected level difference is a passing target level difference required to be passed without avoidance or an avoidance target level difference required to be avoided; and control a notification apparatus to output, toward an inside of a compartment of the vehicle, a notification indicating a passing plan of the passing target level difference in response to the detected level difference, which exists on a traveling route to be traveled by the vehicle within a parking area for parking purpose, being determined as the passing target level difference. The notification apparatus is controlled to output the notification indicating the passing plan of the passing target level difference in a notification mode that distinguishes the passing target level difference from the avoidance target level difference.

According to one example there is provided a method comprising selecting a first location from a set of locations and analysing, by a processor, data collected from a first vehicle located within a first distance of the first location. A first value representative of a first performance parameter of the first vehicle is generated. A second value representative of a second performance parameter of the first vehicle is generated. At least one of the first and second values is compared with a first threshold and, when one of the first and second values is greater than the first threshold, a safety alert is issued greater than (in some examples, less than).

Embodiments herein include a method executable by a processor coupled to a memory. The processor is local to a vehicle can operable to determine initial location and direction information associated with the vehicle at an origin of a trip request. The processor receives one or more frames captured while the vehicle is traveling along a navigable route relative to the trip request and estimates an execution time for each of one or more computations respective to an analyzing of the one or more frames. The processor, also, off-loads the one or more computations to processing resources of a cloud-based system that is in communication with the processor of the vehicle in accordance with the corresponding execution times.

Systems, methods, and other embodiments described herein relate to automatically learning parking preferences for a driver and generating parking recommendations. In one embodiment, a method 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.

Aspects of the disclosure provide a method of providing a destination to an autonomous vehicle in order to enable the autonomous vehicle to collect data according to a targeted driving goal. For instance, a current location of an autonomous vehicle may be received. A set of destinations may be selected from a plurality of predetermined destinations. A route may be determined for each destination. A relevance score may be determined for each destination based on the determined routes and the targeted driving goal. Each destination may be assigned to one of a set of two or more buckets based on the relevance scores. A destination of the set may be selected based on a predetermined sampling probability. The selected destination is sent to the autonomous vehicle in order to cause the autonomous vehicle to travel to the selected destination in an autonomous driving mode.

An edge information handling system (IHS) manager includes a storage for storing a labeled data associated with a use counter; and a vehicle counter; and a processor. The processor is programmed to: update an inference module using the labeled data, determine, after the updating, whether the use counter of the labeled data has exceeded a current use threshold, and in response to the use counter of the labeled data exceeding the current use threshold, initiating replacing of the labeled data with new labeled data from a central IHS. The current use threshold is based on the vehicle counter.

A setting device including a processor is provided. The processor is configured to, when occurrence of a secondary disaster is predicted after occurrence of a disaster, set a region in which the secondary disaster is predicted to a charging-prohibited zone in which use of a charging facility for charging a battery installed in a vehicle is prohibited.