Baidu USA LLC

Huawei Technologies Co., Limited

Baidu.com Times Technology (beijing) Co. Ltd.

Jing-jin Electric Technologies Co., Ltd.

Huawei Technologies,co., Ltd.

Liangliang Zhang

Jiangtao Hu

Dong Li

Yifei Jiang

Jiaming Tao

Junren Chang

Fan Zhu

Yajuan Li

Qi Luo

Haoyang Fan

Qi Kong

Yajia Zhang

Bo Lin

Jinghao Miao

Wei Quan

Yan Wang

Yongping Zhang

Weicheng Zhu

Li Chai

Jingao Wang

Zhongbin Qin

Weiwei Song

Qinghai Zeng

Jian Zhang

Hongping Zhang

Yan Peng

Xin Xu

Hui Jiang

Ting Li

Hongyi Sun

Liyun Li

Yuchang Pan

Li Zhuang

I-Hsuan Yang

Shiyuan Fang

Yanling Lu

Feiyi Jiang

Kaihe Zhang

Zhuo Chen

Guang Yu Yang

Jing Liu

Jie Shi

Xiangdong Zhang

Kecheng Xu

Xuan Zou

Chunshuang Feng

Chunxia Cai

Hong Wang

Yang Zhang

Lin Ma

Zhenzhen Cao

Hao Bi

Shulan Feng

Lei Han

Jinyun Zhou

Xiang Yu

Sen Hu

Xiangquan Xiao

Zhongpu Xia

Hsien-Ting Cheng

Xiaoxin Fu

Siyang Yu

Yunbao Zeng

Chunming Zhao

Kunxing Sun

Yu Zhu

Yiqun Fu

Longyu Ju

Biqing Sun

Dandan Qi

Li Dong

Shengxiang Liu

Kairui Yang

Jing Qian

Jingtao Hu

A sensor aggregation framework for autonomous driving vehicles is disclosed. In one embodiment, sensor data is 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 sensor data includes obstacle information of the obstacles and vehicle data of the ADV. Each of the vehicle data is timestamped with a current time at which the vehicle data is captured to generate a number of timestamps that correspond to the vehicle data. The obstacle information, the vehicle data, and the corresponding timestamps are aggregated into training data. The training data is used to train a set of parameters that is subsequently utilized to predict at least in part future obstacle behaviors and vehicle movement of the ADV.

Sensor aggregation framework for autonomous driving vehicles

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&#x2212;1)*2/B are integers. In the stator assembly of the present disclosure, by way of the special winding connection design, there is no phase difference of magnetic fields between the parallel branches, and each parallel branch equally passes through each layer in the stator slots, which fundamentally inhibits the generation of circulating current between the parallel branches, suppresses the harmonics, and improves the efficiency of the motor.

Stator assembly and motor

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.

Rotor structure of interior permanent magnet motor

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.

Rotor core with different groove and skew angle configuration of step-skewing synchronous motor

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.

Comprehensive and efficient method to incorporate map features for object detection with LiDAR

An ADV may determine whether there is preexisting map data for an environment or geographical area/location where the ADV is located/travelling. If there is no preexisting data, the ADV may generate map data based on sensor data obtained from one or more sensors of the ADV. The ADV may determine a path for the ADV based on the generated map data. If there is preexisting map data, the ADV may determine a path for the ADV based on the preexisting map data.

Determining driving paths for autonomous driving vehicles based on map data

One example method includes receiving, by a first node, a first message from a second node, wherein the first message is used to page a third node, instruct the third node to access a network, instruct the first node to provide a relay serving for the third node, instruct to set the third node to a low configuration mode, instruct to set the third node to a high configuration mode, or instruct to update system information. The first node can send a second message to a third node, where the second message is used to instruct the third node to access a network, instruct the third node to perform communication with a second node by using the first node, instruct to set the third node to a low configuration mode, instruct to set the third node to a high configuration mode, or instruct the third node to update system information.

Device-to-device communication method, apparatus, and device

According to some embodiments, described herein is a system and method for handling sensor failures in autonomous driving vehicles (ADV) that is navigating in a world coordination as an absolute coordination system. When the ADV encounters a sensor failure, but still has at least one camera working properly, the sensor failure handling system can switch the ADV from navigating in the world coordination to a local coordination, in which the ADV relies camera-based obstacle detection and lane mark detection to drive safely until human dis-engagement or until the ADV is parked along a road side.

Methods for handling sensor failures in autonomous driving vehicles

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.

Method and system to predict one or more trajectories of a vehicle based on context surrounding the vehicle

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.

Drifting correction between planning stage and controlling stage of operating autonomous driving vehicles

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.

Perception and planning collaboration framework for autonomous driving

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.

Method and system for generating reference lines for autonomous driving vehicles using multiple threads

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.

Method for establishing evolved packet system bearer and base station

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).

Updating map data for autonomous driving vehicles based on sensor data

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.

System and method for training a machine learning model deployed on a simulation platform

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Sunnyvale, CA, United States of America

Liangliang Zhang