Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

Described herein are methods of estimating a chance of precipitation in an area that include identifying one or more vehicles in the area and determining the likelihood of precipitation using telematics data for the one or more vehicles in the area. Also described herein are methods that include receiving telematics data from a plurality of vehicles, wherein the telematics data is associated with a location, analyzing the telematics data to identify vehicle events associated with one or more segments of road, analyzing weather information associated with the one or more segments of road, and determining a correlation between the weather information and the vehicle events.

Aspects of the disclosure provide for arranging trips for autonomous vehicles. For instance, a request for a trip may be received by one or more processors of one or more server computing devices. The request may identify an initial location. A weather condition at the initial location may be identified. One or more internal vehicle state conditions and one or more priorities for pulling over may be determined based on the weather condition. A second location may be determined based on the one or more priorities and the initial location. Dispatch instructions may be provided to an autonomous vehicle, the dispatch instructions identifying the second location and the one or more internal vehicle state conditions in order to cause computing devices of the autonomous vehicle to control the autonomous vehicle to the second location and adjust internal vehicle state conditions based on the one or more internal vehicle state conditions.

Some embodiments of the invention provide a novel prediction engine that (1) can formulate predictions about current or future destinations and/or routes to such destinations for a user, and (2) can relay information to the user about these predictions. In some embodiments, this engine includes a machine-learning engine that facilitates the formulation of predicted future destinations and/or future routes to destinations based on stored, user-specific data. The user-specific data is different in different embodiments. In some embodiments, the stored, user-specific data includes data about any combination of the following: (1) previous destinations traveled to by the user, (2) previous routes taken by the user, (3) locations of calendared events in the user's calendar, (4) locations of events for which the user has electronic tickets, and (5) addresses parsed from recent e-mails and/or messages sent to the user. In some embodiments, the prediction engine only relies on user-specific data stored on the device on which this engine executes. Alternatively, in other embodiments, it relies only on user-specific data stored outside of the device by external devices/servers. In still other embodiments, the prediction engine relies on user-specific data stored both by the device and by other devices/servers.

A system includes at least partially autonomous vehicles, at least partially separated interconnected roadways, and a management system. Each of the vehicles is configured to cooperate with another vehicle or an area controller. The management system is configured to receive requests to transport, which may have respective start points and respective destinations. Additionally, the management system is configured, responsive to receiving the request, to assign a vehicle to fulfill the request. The assigned vehicle is configured to transport a person from the respective start point, at least in part via the interconnected roadways, to the respective destination.

Various implementations include approaches for training a surface detection classifier and detecting characteristics of a surface, along with related micromobility vehicles. Certain implementations include a method including: comparing: i) detected movement of a micromobility (MM) vehicle or a device located with a user at the MM vehicle while operating the MM vehicle, with ii) a surface detection classifier for the MM vehicle; and in response to detecting that the MM vehicle is traveling on a restricted surface type for a threshold period, performing at least one of: a) notifying an operator of the MM vehicle about the travel on the restricted surface type, b) outputting a warning at an interface connected with the MM vehicle or the device, c) limiting a speed of the MM vehicle, or d) disabling operation of the MM vehicle.

A method for trajectory planning for a commercial vehicle at a traffic junction, in particular including a traffic circle, by a control unit is provided. Trafficability of a lane of the commercial vehicle within a lane width of the traffic junction is checked prior to traveling into the traffic junction, traveling through a center of the traffic junction via an alternative trajectory is checked when normal negotiating of the traffic junction is not possible, and detection and/or prediction of surrounding traffic and trajectory planning of the alternative trajectory are carried out, taking the detected and/or predicted surrounding traffic into account. Moreover, a control unit, a computer program, and a machine-readable memory medium are provided.

In some embodiments, an electronic device displays user interfaces for viewing information about and/or for reporting information about incidents of different types associated with a (e.g., physical) location.

A route provision apparatus for providing a route to a vehicle including a transceiver; a sensor interface configured to receive sensing information from one or more sensors; and a processor configured to identify a lane of the vehicle, estimate, using map information received from a navigation system and not from the server based on a communication state with the server satisfying a preset condition, an optimal route for the vehicle in lane units, generate autonomous driving visibility information by fusing the sensing information with the optimal route, and fuse dynamic information related to a movable object with the autonomous driving visibility information to update the optimal path based on the dynamic information, and wherein the sensing information is fused with the map information to generate a Simultaneous Localization and Mapping (SLAM) map.

Provided are techniques for thermally efficient route selection. A request is received for a route for a vehicle to travel from a first geographic location to a second geographic location. Route data is retrieved for each of a plurality of routes, where the route data includes, for each portion of each route at a given time and day, air temperature adjacent to a road surface. Vehicle data is retrieved for the vehicle, where the vehicle data includes a desired temperature of an item being carried and a desired inlet air temperature. The route from the first geographic location to the second geographic location is determined using the route data and the vehicle data, where the route is thermally efficient. The vehicle is directed along the route, where the route specifies a lane from a plurality of lanes of a road for each portion of the route.