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). 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.

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: A first localization system performs a first localization using a first set of sensors to track locations of the ADV along the path from a starting point to a destination point. A first localization curve is generated as a result representing the locations of the ADV along the path tracked by the first localization system. Currently, a second localization system performs a second localization using a second set of sensors to track the locations of the ADV along the path. A second localization curve is generated as a result representing the locations of the ADV along the path tracked by the second localization system. A system delay of the second localization system is determined by comparing the second localization curve against the first localization curve as a localization reference.

Abstract: According to one embodiment, perception data describing a set of trajectories driven by a number of vehicles is received at a server from the vehicles or data collection agents over a network. The vehicles were driving through a road segment of a road over a period of time and their driving trajectories were captured. A trajectory analysis is the performed on the perception data using a set of rules to determine driving behaviors of the corresponding vehicles. A lane configuration of the road segment is then determined based on the driving behaviors. A map segment of a navigation map is then updated based on the lane configuration of one or more lanes within the road segment. A higher definition map can be generated based on the updates of the navigation map and the lane configuration, which can be utilized to autonomously drive an ADV subsequently.

Abstract: In one embodiment, perception data is received from a number of autonomous driving vehicles (ADVs) over a network. The perception data includes information describing a set of trajectories that a number of vehicles having driven through a road segment of a road and perceived by one or more ADVs using their respective sensors driving on the same road segment. In response to the perception data, an analysis is performed on the perception data, i.e., the trajectories, to determine one or more lanes within the road segment. For each of the lanes, a lane reference line associated with the lane is calculated based on the trajectories within the corresponding lane. The lane metadata describing the lane reference lines for the one or more lanes are stored in a lane configuration data structure such as a database. The lane configuration database can then be utilized for autonomous driving at real-time subsequently without having to use a high definition map.

Abstract: A method of determining a smooth reference line for navigating an autonomous vehicle in a manner similar to human driving is disclosed. A high density map is used to generate a centerline for a lane of roadway. Using the centerline, a number of sample points is generated that is related to a curvature of the centerline. Adjustment points are generated at each sample point, a few on either side of the centerline at each sample point. Candidate points at a sample point include the adjustment points and sample point. A least cost path is determined through each of the candidate points at each of the sample points. Path cost is based an angle of approach and departure through a candidate point, and a distance of the candidate point from the centerline.

Abstract: In one embodiment, a real time map can be generated by an autonomous driving vehicle (ADV) based on a navigation guideline for a lane and associated lane boundaries for the lane on a particular segment of a road. When travelling in the lane on the road segment, the ADV can use the navigation guideline as a reference line and use the lane boundaries as boundaries. The navigation guideline can be derived from manual driving path data collected by a manned vehicle that has travelled multiple times on the particular segment of the road.

Abstract: According to some embodiments, a system selects a number of polynomials representing a number of time segments of a time duration to complete the path trajectory. The system selects an objective function based on a number of cost functions to smooth speeds between the time segments. The system defines a set of constraints to the polynomials to at least ensure the polynomials are smoothly joined together. The system performs a quadratic programming (QP) optimization on the objective function in view of the set of constraints, such that a cost associated with the objective function reaches a minimum while the set of constraints are satisfied. The system generates a smooth speed for the time duration based on the optimized objective function to control the ADV autonomously.

Abstract: Embodiments of the present disclosure provide a communication method, a network side device, and a user terminal. The method includes: establishing, by a user terminal, a connection to a first base station by using a first component carrier; sending, by the first base station, a preprocessing indication message to the user terminal, to indicate that the user terminal is to perform uplink synchronization with a second base station on a second component carrier; and obtaining, by the first base station, a target second base station, and sending a first indication message to the target second base station, to indicate that the user terminal is to perform data transmission with the target second base station.