Abstract: According to some embodiments, a system calculates a first trajectory based on a map and a route information. The system performs a path optimization based on the first trajectory, traffic rules, and an obstacle information describing obstacles perceived by the ADV. The path optimization is performed by performing a spline curve based path optimization on the first trajectory, determining whether a result of the spline curve based path optimization satisfies a first predetermined condition, performing a finite element based path optimization on the first trajectory in response to determining that the result of the spline curve based path optimization does not satisfy the first predetermined condition, performing a speed optimization based on a result of the path optimization, and generating a second trajectory based on the path optimization and the speed optimization to control the ADV.

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: According to some embodiments, a system generates a driving trajectory from a starting point to a destination point for an ADV. In one embodiment, the system calculates a first trajectory based on a map and a route information. The system generates a path profile based on the first trajectory, traffic rules, and blocking obstacles perceived by the ADV. The system determines non-blocking obstacles perceived by the ADV. The system generates a speed profile of the ADV for the path profile based on the non-blocking obstacles to identify a speed of the ADV to avoid the blocking obstacles in view of the non-blocking obstacles. The system generates a second trajectory based on the path profile and the speed profile to control the ADV autonomously according to the second trajectory.

Abstract: In one embodiment, sensor data are collected from one or more sensors mounted on an autonomous driving vehicle (ADV) while the ADV is moving within a region of interest (ROI) that includes a number of obstacles. The collected sensor data are operated on to obtain obstacle data associated with the obstacles, location data, and a number of timestamps that correspond to the obstacle data and the location data. For each of the timestamps, positions of the obstacles are mapped to some of the obstacle data that correspond to the timestamp based on the location data, thereby generating mapped information of the obstacles. The mapped information is automatically labelled to generate labelled data, where the labelled data is utilized to subsequently train a machine learning algorithm to recognize obstacles during autonomous driving of an ADV.

Abstract: According to some embodiments, a system operates an ADV. In one embodiment, the system perceives a driving environment surrounding the ADV based on sensor data obtained from a plurality of sensors, including perceiving a moving obstacle that is moving relative to the ADV. The system projects the moving obstacle as a figure onto a station-time (ST) coordinate system, wherein the ST coordinate system indicates a distance between the figure and a reference point at different points in time. And the system, for each of a plurality of predetermined processing time intervals, determines two points of the figure in the ST coordinate system to represent a shape of the figure, wherein the shape of the figure is utilized to plan a trajectory to drive the ADV to avoid colliding with the moving obstacle.

Abstract: A method and a device for establishing an Evolved Packet System (EPS) bearer, where the method includes establishing, by a first base station to which a user equipment (UE) belongs, a first EPS bearer based on a first component carrier (CC) for the UE, wherein the first EPS bearer is an EPS bearer between the first base station and the UE; and instructing, by the first base station, a second base station to establish a second EPS bearer based on a second CC for the UE, wherein the second EPS bearer is an EPS bearer between the second base station and the UE. The present disclosure enables the UE to aggregate carriers of different frequency ranges from the first base station to which the UE belongs and the second base station to which the UE belongs to transmit data, thus improving the throughput of the data transmitted by the UE.

Abstract: In one embodiment, a method, apparatus, and system for automatically detecting and notifying a user of a traffic rule violation using sensors and a perception module of an autonomous driving vehicle is disclosed. The operations performed comprise: capturing surrounding road traffic information at an autonomous driving vehicle (ADV) based on sensor data obtained from a plurality of sensors of the ADV; automatically detecting at the ADV a traffic rule violation of a first vehicle within a perception range of the ADV based on the surrounding road traffic information and a set of pre-configured traffic rules maintained by the ADV, including analyzing the surrounding road traffic information in view of the pre-configured traffic rules; and generating an alert in response to the detected traffic rule violation.

Abstract: In one embodiment, a method, apparatus, and system for trajectory planning for autonomous driving in an autonomous driving vehicle equipped with a low accuracy localization and perception module is disclosed.

Abstract: A real-time perception adjustment and correction and driving adaption method for autonomous driving is provided. The system will analyze the driving behaviors of surrounding vehicles surrounding an ADV, which is utilized to improve its original perception based on the analysis and to adapt to the updated driving environment. In one embodiment, in addition to the perception information provided by the sensors, the system analyzes the behaviors of the surrounding vehicles based on the perception information. Based on the behaviors of the surrounding vehicles, the system may detect there is may be an obstacle that has not been detected based on the perception information. Alternatively, the system may detect that an obstacle determined based on the perception information actually may not exist based on the behaviors of the surrounding vehicles. The paths created for the ADV may then adjusted accordingly to improve the autonomous driving of the ADV.

Abstract: A station-time (S-T) graph may be obtained in response to a first reference line representing a path from a first location to a second location associated with an autonomous driving vehicle (ADV). One or more kernels may be applied to the S-T graph. Each of the one or more kernels may indicate a plurality of points on the S-T graph. One or more constraints may be applied to the S-T graph. Each of the one or more constraints may indicate a condition for points in the S-T graph. A set of speeds for portions of the path is determined based on the one or more kernels and the one or more constraints.

Abstract: A method efficiently determines whether a trajectory of an obstacle to an autonomous driving vehicle (ADV) will require navigation adjustment for the ADV. The trajectory of the obstacle is represented by an ordered plurality of points, {P0 . . . PM}, and a reference line of the ADV is represented by an ordered plurality of points, {R0 . . . RN}, N>M. If the obstacle and ADV are heading in the same direction, then, for a first point P0 on the obstacle trajectory, the ADV finds a point S0 in {R0 . . . RN} that is the least distance from P0 to the reference line. For each of the remaining obstacle points, Pi, the search for a least distance point, Si, is limited to the portion of the reference line Si?1 to RN. If the obstacle and ADV are heading in opposing directions, the search process can be performed from PM, toward P0, in a similar fashion.

Abstract: In one embodiment, when an ADV is driving on a trajectory generated based on a reference line, a separate processing thread is executed to precalculate a new reference line as a future reference line for a future planning cycle in parallel. The future reference line is being created while the ADV is moving along a trajectory generated based on the original reference line and before reaching a location corresponding to the starting point of the future reference line. The future reference line is overlapped with an end section of the original reference line, such that the future reference line can be connected to the end section of the original reference line. The future reference line serves an extension of the original reference line before the ADV reaches the end section of the original reference line.

Abstract: According to various embodiments, systems and methods described in the disclosure combine mapped features with point cloud features to improve object detection precision of an autonomous driving vehicle (ADV). The map features and the point cloud features can be extracted from a perception area of the ADV within a particular angle view at each driving cycle based on a position of the ADV. The map features and the point cloud features can be concatenated and provided to a neutral network for object detections.

Abstract: A method and a device for establishing an evolved packet system EPS bearer are disclosed. The method includes establishing, by a first base station to which UE belongs, a first EPS bearer based on a first CC for the UE, wherein the first EPS bearer is an EPS bearer between the first base station and the UE; and instructing, by the first base station, a second base station to establish a second EPS bearer based on a second CC for the UE, wherein the second EPS bearer is an EPS bearer between the second base station and the UE. The present invention enables the UE to aggregate carriers of different frequency ranges from the first base station to which the UE belongs and the second base station to which the UE belongs to transmit data, thus improving the throughput of the data transmitted by the UE.

Abstract: According to some embodiments, a system receives a first set of reference points based on a map and a route information, the first plurality of reference points representing a reference line in which the ADV is to follow. The system selects a second set of reference points along the reference line, including iteratively performing, selecting a current reference point from the first set of reference points, determining a sampling distance along the first set of reference points based on the currently selected reference point using a nonlinear algorithm, and selecting a next reference point based on the determined sampling distance such that a density of the second set of reference points closer to the ADV is higher than a density of the selected reference points farther away from the ADV. The system plans a trajectory for the ADV using the second set of reference points to control the ADV.

Abstract: A server may determine whether traffic control devices in an environment have changed, based on one or more report messages and sensor data. If the traffic control devices in an environment have changed, the server may generate updated map data and transmit the updated map data to an autonomous driving vehicle (ADV).

Abstract: The present invention provides a method for controlling communication, user equipment, and a base station. The method includes: obtaining, by user equipment from a base station when needing to perform first communication, a first communication resource required for the first communication, where the obtaining, by user equipment from a base station, a first communication resource required for the first communication includes: obtaining, if the user equipment is in an RRC idle state, the first communication resource by receiving a SIB broadcast by the base station; or entering an RRC connected state if the user equipment is in an RRC idle state, and sending a first communication resource request to the base station; or sending a first communication resource request to the base station if the user equipment is in an RRC connected state.

Abstract: In an embodiment, a method for representing a surrounding environment of an ego autonomous driving vehicle (ADV) is described. The method represents the surrounding environment using a first set of features from a definition (HD) map and a second set of features from a target object in the surrounding environment. The first set of features are extracted from the high definition map using a convolutional neural network (CNN), and the second set of features are handcrafted features from the target object during a predetermined number of past driving cycles of the ego ADV. The first set of features and the second set of features are concatenated and provided to a number of fully connected layers of the CNN to predict behaviors of the target object. In one embodiment, the operations in the method can be repeated for each driving cycle of the ego ADV.

Abstract: In one embodiment, a method, apparatus, and system may predict behavior of environmental objects using machine learning at an autonomous driving vehicle (ADV). One or more yield/overtake decisions are made with respect to one or more objects in the ADV's surrounding environment using a data processing architecture comprising at least a first, a second, and a third neural networks, the first, the second, and the third neural networks having been trained with a training data set. Driving signals are generated based at least in part on the yield/overtake decisions to control operations of the ADV.

Abstract: In one embodiment, a method, apparatus, and system may predict behavior of environmental objects using machine learning at an autonomous driving vehicle (ADV). A data processing architecture comprising at least a first neural network and a second neural network is generated, the first and the second neural networks having been trained with a training data set. Behavior of one or more objects in the ADV's environment is predicted using the data processing architecture comprising the trained neural networks. Driving signals are generated based at least in part on the predicted behavior of the one or more objects in the ADV's environment to control operations of the ADV.