Abstract: Driving statistics of an autonomous vehicle are collected. The driving statistics include driving commands issued at different points in time and route selection information of one or more routes while the autonomous vehicle was driven in a manual driving mode by one or more users. For each user of the autonomous vehicle, one or more user driving behaviors and preferences of the user are determined from at least the driving statistics for predetermined driving scenarios. One or more driving profiles for the user are generated based on the determined user behaviors and preferences under the driving scenarios, where the driving profiles are utilized to control the autonomous vehicle under similar driving scenarios when the user is riding in the autonomous vehicle that operates in an autonomous driving mode.

Abstract: Responsive to sensor data received from one or more sensors of an autonomous vehicle, one or more predicted trajectories are generated, with each of the predicted trajectories having an associated probability. One or more driving scenarios that trigger gesture recognition are identified. For each of the identified driving scenarios, one or more gestures from one or more vehicles are detected in accordance with a gesture detection protocol. One or more gestures from the autonomous vehicle are emitted for communication with the vehicles in accordance with a gesture emission protocol based on the detected gestures. The predicted trajectories are modified based on the detected gestures, the emitted gestures and the associated probabilities of the predicted trajectories. The autonomous vehicle is controlled based on the modified predicted trajectories.

Abstract: Route segment information is received from a first autonomous vehicle over a network. The route segment information includes one or more route segments and estimated entrance times, each of the entrance times being associated with one of the route segments and representing a time the first autonomous vehicle enters the route segment. Whether the first autonomous vehicle is within a specific route segment is determined based on an entrance time associated with the specific route segment and a current location of the first autonomous vehicle. If the first autonomous vehicle is within the specific route segment, a request is sent to invite the first autonomous vehicle to join a vehicle group of one or more vehicles associated with the specific route segment. The vehicle group is one of a plurality of vehicle groups that were determined based on route segment information received from a plurality of autonomous vehicles.

Abstract: A user can issue a query about a process that has a number of stages. A stage of the process is determined using the query and location data associated with the query, and a stage prediction model. A stage learning system can select a sample of query logs for a category from a database of millions or billions of users' queries. Queries can be parsed into keywords. A category can be determined from location information associated with each query and from query keywords. Queries are aligned based on location and, optionally, keywords. TF-IDF values are computed for queries and are used to determine a difference significance between aligned, adjacent queries. If aligned, adjacent queries have a substantially difference in keywords and TF-IDF, then a conversion stage is identified. Content can be presented to the user based on the category, keywords, location, and conversion stage.

Abstract: Embodiments of methods, apparatuses, or systems relating to identifying a level of desirability of a hyperlink or search query using experience score.