A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.

A method and/or system is able to provide driver fingerprint via metadata extraction managed by a driver rating (DR) model trained by a machine learning center (MLC) coupled to a cloud based network (CBN). In one embodiment, a DR system includes a set of outward facing cameras, a set of inward facing cameras, and a vehicle onboard computer (VOC). The set of outward facing cameras mounted on a vehicle is used to collect external images representing a surrounding environment in which the vehicle operates. The set of inward facing cameras mounted in the vehicle is used to collect internal images including operator body expression representing at least operator's attention. The VOC is configured to determine the identity of operator and current operating style in response to the collected internal images, the collected external images, and historical stored data.

A navigation system and various methods of using the system are described herein. Search query results are refined by the system and are prioritized based at least in part upon sub-search categories selected during the searching process. Sub-searches can be represented by graphical icons displayed on the user interface.

In some implementations, a mobile device transmit traffic information to a server for analysis. The traffic information includes movement information including detected stops and durations of detected stops. The traffic information is analyzed to detect traffic patterns that indicate locations of stop signs and/or stop lights. The traffic information is analyzed to determine durations of stops at stop signs and/or stop lights. The durations of stops is associated with a time of day and/or day of the week. In some implementations, navigational routes is determined based stop sign and/or stop light information, including the delays attributable to detected stop signs and/or stop lights.

The disclosed technology provides solutions for facilitating the selection of a parking space by a user of a parking application. A process of the disclosed technology can include steps for monitoring sensor data and location data associated with a user device, retrieving, based on the location data associated with a user device, listing data associated with one or more parking spaces in a vicinity of the user device, and capturing image data that includes at least a portion of the one or more parking spaces. In some aspects, the process may further include steps for overlaying one or more graphical objects onto the image data, wherein the one or more graphical objects are based on the listing data associated with the one or more parking spaces. Systems and machine-readable media are also provided.

A method or system is able to refine Global Positioning System (GPS) information for guiding a vehicle via extracted metadata using a GPS refinement (GR) model managed by a virtuous cycle containing sensors, machine learning center (MLC), and a cloud based network (CBN). The GR system includes a set of outward facing cameras, a vehicle onboard computer (VOC), and GR model. The outward facing cameras mounted on a vehicle are capable of collecting external images representing a surrounding environment in which the vehicle operates. The VOC is configured to generate a positional vehicle location with respect to the surrounding environment in accordance with the external images and historical stored data obtained from CBN. The GR model is configured to generate a driving guidance based on combined information between the positional vehicle location and GPS data.

A method or system that is able to adjust an exterior mirror of a vehicle via an automatic mirror-setting (AM) model managed by a virtuous cycle containing a cloud based network (CBN). The system includes a set of mirrors, a set of inward facing cameras, a vehicle onboard computer (VOC), and AM module. In one embodiment, the mirrors, attached to a vehicle, are configured to capture at least a portion of the external environment in which the vehicle operates. The inward facing cameras, mounted in the vehicle, are configured to collect internal images, including the operator's facial features showing operator visual characteristics. The VOC, which is coupled to the CBN, is configured to determine operator vision metadata based on the internal images, operator visual characteristics, and historical stored data. The AM module is able to adaptively set a mirror to an optimal orientation so that the area of the external blind spot is minimized.

In some implementations, a mobile device can transmit a request for traffic information to a server. The traffic information can include movement information including detected stops and durations of detected stops. The traffic information is analyzed to detect traffic patterns that indicate locations of stop signs and/or stop lights. The traffic information is analyzed to determine durations of stops at stop signs and/or stop lights. The durations of stops are associated with a time of day and/or day of the week. In some implementations, navigational routes are determined based stop sign and/or stop light information, including the delays attributable to detected stop signs and/or stop lights.

An automatic driving method and an automatic driving device are provided. The method includes: determining whether a target vehicle travels on a pre-defined commuting route, where the commuting route is a fixed route on which the target vehicle travels at a frequency higher than a predetermined frequency threshold; acquiring target travel information corresponding to the commuting route if the target vehicle travels on the pre-defined commuting route, where the target travel information includes a historical running trajectory of the target vehicle on the commuting route; and controlling the target vehicle to perform automatic drive on the commuting route based on the target travel information.

A traffic light information providing apparatus, a vehicle system including the same, and a method thereof may include: a storage configured to accumulate and store intersection traffic light information; and a processor configured to convert turn-on information of a traffic light of an intersection in front of a vehicle into a database in the storage during driving of the vehicle, and configured to pre-provide a user with traffic light information at the intersection or traffic light information at a crosswalk in a turning direction before or while passing through the intersection based on the converted database of the turn-on information of the intersection traffic light.