Abstract: According to one embodiment, when a predicted trajectory is received, a set of one or more features are extracted from at least some of the trajectory points of the predicted trajectory. The predicted trajectory is predicted using a prediction method or algorithm based on perception data perceiving an object within a driving environment surrounding an autonomous driving vehicle (ADV). The extracted features are fed into a predetermined DNN model to generate a similarity score. The similarity score represents a difference or similarity between the predicted trajectory and a prior actual trajectory that was used to train the DNN model. The similarity score can be utilized to evaluate the prediction method that predicted the predicted trajectory.

Abstract: In one embodiment, in response to perception data perceiving a driving environment surrounding an ADV, a map image of a map covering a location associated with the driving environment is obtained. An image recognition is performed on the map image to recognize one or more objects from the map image. An object may represent a particular road, a building structure (e.g., a parking lot, an intersection, or a roundabout). One or more features are extracted from the recognized objects, where the features may indicate or describe the traffic condition of the driving environment. Behaviors of one or more traffic participants perceived from the perception data are predicted based on the extracted features. A trajectory for controlling the ADV to navigate through the driving environment is planned based on the predicted behaviors of the traffic participants. A traffic participant can be a vehicle, a cyclist, or a pedestrian.

Abstract: A surrounding environment of an autonomous vehicle is perceived to identify one or more vehicles nearby. For each of the identified vehicles, based on a current location of the identified vehicle, vehicle-independent information is obtained to determine context surrounding the identified vehicle, where the vehicle-independent information includes vehicle surrounding information that defines physical constraints imposed on the identified vehicle. For each of the identified vehicles, one or more trajectories for the identified vehicle are predicted based at least in part on the vehicle-independent information associated with the identified vehicle. The autonomous vehicle is controlled based on the one or more predicted trajectories of the one or more identified vehicles.

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 method and apparatus is disclosed herein for generating visualizations of documents based on semantic information. In one embodiment, the method comprises selecting a plurality of image segments having a plurality of attributes that includes a semantic importance information for each of the plurality of document image segments and determining a plurality of positions and a plurality of geometric operations to be performed on the plurality of document image segments according to the plurality of attributes and display constraints.

Abstract: A method and apparatus is disclosed herein for generating visualizations of documents based on semantic information. In one embodiment, the method comprises selecting a plurality of image segments having a plurality of attributes that includes a semantic importance information for each of the plurality of document image segments and determining a plurality of positions and a plurality of geometric operations to be performed on the plurality of document image segments according to the plurality of attributes and display constraints.