Abstract: The present disclosure discloses a stator assembly and motor. The stator assembly comprises a stator core and a multi-branch rectangular conductor winding, an even number of stator slots are provided on an inner circumference of the stator core, and each of the stator slots is divided into multiple layers for accommodating rectangular conductors. The multi-branch rectangular conductor winding is provided in the stator slots, and the multi-branch rectangular conductor winding comprises B number of parallel branches, and B is an integer multiple of 4. The number of slots per phase per pole, Q, of the stator assembly is an odd number, and Q and B satisfy a relationship that both (Q+1)*2/B and (Q?1)*2/B are integers.

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: A perception module is configured to perceive a driving environment surrounding an autonomous driving vehicle (ADV) based on sensor data, and to generate perception information using various perception models or methods. The perception information describes the perceived driving environment. Based on the perception information, a planning module is configured to plan a trajectory representing a route or a path for a current planning cycle. The ADV is then controlled and driven based on the trajectory. In addition, the planning module determines a critical region (also referred to as a critical area) surrounding the ADV based on the trajectory in view of a current location or position of the ADV. The metadata describing the critical region is transmitted to the perception module via an application programming interface (API) to allow the perception module to generate perception information for a next planning cycle in view of the critical region.

Abstract: In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Abstract: Via a first processing thread, an ADV is controlled according to a first trajectory that was generated based on a first reference line starting at a first location. Concurrently via a second processing thread, a second reference line is generated based on a second location of the first trajectory that the ADV will likely reach within a predetermined period of time in future. The predetermined period of time is greater than or equals to an amount of time to generate a reference line for the ADV. The second reference line is generated while the ADV is moving according to the first trajectory and before reaching the second location. Subsequently, in response to determining that the ADV is within a predetermined proximity of the second location, a second trajectory is generated based on the second reference line without having to calculate the second reference line at the second location.

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: 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: System and method for training a machine learning model are disclosed. In one embodiment, for each of the driving scenarios, responsive to sensor data from one or more sensors of a vehicle and the driving scenario, driving statistics and environment data of the vehicle are collected while the vehicle is driven by a human driver in accordance with the driving scenario. Upon completion of the driving scenario, the driver is requested to select a label for the completed driving scenario and the selected label is stored responsive to the driver selection. Features are extracted from the driving statistics and the environment data based on predetermined criteria. The extracted features include some of the driving statistics and some of the environment data collected at the different points in time during the driving scenario.

Abstract: A system and a method for controlling an autonomous driving vehicle. The system includes vehicle sensors and a controller. The controller has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: receive vehicle parameters from the vehicle sensors; obtain a vehicle dynamic model by adding a dynamics error bound to a state space model, wherein the dynamics error bound is estimated using linear least square; minimize a linear quadratic regulator cost function based on the vehicle dynamic model; and control the vehicle using control input obtained from the minimized cost function.

Abstract: In one embodiment, a system monitors states of an autonomous driving vehicle (ADV) using a number of sensors mounted on the ADV. The system perceives a driving environment surrounding the ADV using at least a portion of the sensors. The system analyzes the states in view of the driving environment to determine a real-time traffic condition at a point in time. The system determines whether the real-time traffic condition of the driving environment matches at least a predetermined traffic condition. The system transmits data concerning the real-time traffic condition to a remote server over a network to allow the remote server to generate an updated map having real-time traffic information, in response to determining the real-time traffic condition is unknown. In response to receiving the updated map, the system plans and controls the ADV based on the updated map.

Abstract: In one embodiment, instead of using map data, a relative coordinate system is utilized to assist perception of the driving environment surrounding an ADV for some driving situations. One of such driving situations is driving on a highway. Typically, a highway has fewer intersections and exits. The relative coordinate system is utilized based on the relative lane configuration and relative obstacle information to control the ADV to simply follow the lane and avoid potential collision with any obstacles discovered within the road, without having to use map data. Once the relative lane configuration and obstacle information have been determined, regular path and speed planning and optimization can be performed to generate a trajectory to drive the ADV. Such a perception system is referred to as a relative perception system based on a relative coordinate system.

Abstract: A rotor core is provided for a step-skewing motor that includes rotor core segments mutually staggered by a preset angle. Each of the rotor core segments includes magnet slots along a circumferential direction, with a magnet provided in the magnet slot. An outer circular surface of each of the rotor core segments is provided thereon with a number of auxiliary grooves extending across the segment in an axial direction, and positions and/or cross-sectional shapes of the auxiliary grooves on the rotor core segments are not completely same so as to suppress torque ripple and vibration noise when the motor rotates.

Abstract: A rotor structure of a magnet motor includes a rotating shaft and an iron core on the rotating shaft. Magnet grooves are disposed inside the iron core along a circumferential direction with a magnet provided therein. A distance between an edge line of the magnet groove close to a circumferential edge of the iron core and the circumferential edge of the iron core varies so that a width of a flux barrier formed varies. One end of a long side of the magnet groove close to the circumferential edge is formed with an anti-demagnetization groove communicating with the magnet groove, and an edge line of the anti-demagnetization groove tilts toward the circumferential edge of the iron core. Process slots are provided between the magnet grooves and the circumferential edge of the iron core that are used to increase a salient rate and reluctance torque of the motor.

Abstract: According to some embodiments, a system receives a first control command and a speed measurement of the ADV. The system determines an expected acceleration of the ADV based on the speed measurement and the first control command. The system receives an acceleration measurement of the ADV. The system determines a feedback error based on the acceleration measurement and the expected acceleration. The system updates a portion of the calibration table based on the determined feedback error. The system generates a second control command to control the ADV based on the calibration table having the updated portion to control the ADV autonomously according to the second control command.

Abstract: An autonomous driving vehicle (ADV) may determine a predicted path for a moving obstacle and speeds for different portions of the path. The ADV use multiple threads in parallel to determine the path and speeds for the different portions of the path.

Abstract: In some implementations, a method is provided. The method includes determining a path for an autonomous driving vehicle. The path is located within a first lane of an environment in which the autonomous driving vehicle is currently located. The method also includes obtaining sensor data. The sensor data indicates a set of speeds for a set of moving obstacles located in a second lane of the environment and wherein the second lane is adjacent to the first lane. The method further includes determining whether the set of speeds is lower than a threshold speed. The method further includes determining a new speed for the autonomous driving vehicle in response to determining that the set of speeds is lower than the threshold speed. The method further includes controlling the autonomous driving vehicle based on the path and the new speed.

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, 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 learning based speed planner for autonomous driving vehicles (ADV) is disclosed. An ADV is set into human-driving mode. Driving control elements are under control of a human driver, and other ADV logic is enabled. The ADV plans a route path on a segment of the route having an obstacle. ADV logic generates a station-time graph for the path of the segment, and a grid of cells to encompass the path and obstacle. A feature vector is generated from the grid. Human driving behavior is recorded as the ADV is navigated along the path. Recorded driving data for a large plurality of paths, obstacles and ADVs is transmitted to a server to generate a speed model. The speed model is downloaded to one or more ADVs for use in autonomous driving mode, to determine an initial speed to use in similar driving situations.

Abstract: A parking system for autonomous driving vehicles optimizes a solution to a parking problem. The ADV detects a parking lot and selects a parking space. The ADV defines constraints for the parking lot, parking space, and kinematic constraints of the ADV, and generates a plurality of potential parking paths to the parking space, taking into account the constraints of the parking lot, parking space, and kinematics of the ADV, but without taking into any obstacles that may be surrounding the ADV. The ADV determines a cost for traversing each of the parking paths. One or more least cost candidate paths are selected from the parking paths, then one or more candidate paths are eliminated based on obstacles surrounding the ADV. Remaining candidates can be analyzed using a quadratic optimization system. A best parking path can be selected from the remaining candidates to navigate the ADV to the parking space.