Baidu USA LLC

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

International Business Machines Corporation

Oracle International Corporation

Clari Inc.

Sysware Technology Co., Ltd.

Boe Technology Group Co., Ltd.

Paypal, Inc.

Chengdu Boe Optoelectronics Technology Co., Ltd.

Jing-jin Electric Technologies Co., Ltd.

China Enfi Engineering Corporation

Xin Xu

Fan Zhu

Lin Ma

Liangliang Zhang

Ning Yu

Qi Kong

Jiaming Tao

Venkat Rangan

Hui Jiang

Yuchang Pan

Haoyang Fan

Feiyi Jiang

Yu Meng Li

Jingao Wang

Steven Jay Munroe

Peng Sun

Kun Yan Yin

Erik Edward Voldal

Steven Lester Halter

He Li

Jerry Paul Russell

John Clarence Endicott

Leibing Lv

Lei Tang

Haijun Li

Xin Tang

Dongchun Yao

Hongwei Du

Haobo He

Xueliang Zhao

Yue Wang

Yang Zhang

Fan Yang

Lei Han

Yao Liang Chen

Vishal Vishnoi

Jie Zheng

Jingjing Xue

Srinivasa Phani Kumar Gadde

Yongyi Sun

Gautam Singaraju

Weicheng Zhu

Junping Wang

Michael G Sisco

Manish Parekh

Jing Wen

Li Zhuang

Hai B Zou

Yingnan Liu

Crystal C Pan

Pengshan Zhang

Yuehao Wu

Zhongpu Xia

Xiaoxin Fu

Muruganantham Chinnananchi

Bei Liu

Tsanghong Wang

Zhenmin Zhang

Saba Amsalu Teserra

Chunming Zhao

Junhua Zhao

Jae Min John

Yan Xin Li

Greg Ma

Vladimir Egorov

Yi-Zhang Li

Zhen-Hua Wang

Yuan-Yu Chen

Chao Su

Chunyang Li

Stephen Andrew McRitchie

Kaihe Zhang

De Shou Kong

Guohao Gong

Hsinwei Huang

Mark Shuai Sun

Kefei Cao

Xuan Zou

Yu Dong

Xincheng Ma

Dawei Cui

Chuangu Wu

Chunxia Cai

Chunshuang Feng

Shuangjie Feng

Fen Wang

Kaiyue Wu

Chunyue Du

Biqing Sun

Dandan Qi

Provided is a pixel circuit. The pixel circuit includes: a data writing circuit, coupled to a first gate signal terminal, a second gate signal terminal, a third gate signal terminal, a data signal terminal, a first node, a second node and a third node; a light emission control circuit, coupled to a light emission control terminal, a first power supply terminal, the first node, the third node and a light-emitting element; and a drive circuit, wherein an input terminal, a control terminal and an output terminal of the drive circuit are coupled to the first node, the second node and the third node respectively.

Pixel circuit and driving method thereof, and display device

In response to a request to park an ADV in a parking lot, a set of parking-trajectories associated with a set of predetermined locations near one or more parking spots in the parking lot may be obtained, where the set of parking-trajectories was previously generated based on prior collected planning and control data of the parking lot. Each parking-trajectory of the set of parking-trajectories may correspond to one parking spot of the one or more parking spots in the parking lot. A parking-trajectory may be selected from the set of parking-trajectories based on a current location of the ADV. The ADV may be controlled to park in a corresponding parking spot according to the selected parking-trajectory.

Parking-trajectory generation method combined with offline and online solutions

According to various embodiment, described herein are methods and systems for reliably detecting malfunctions in a variety of software or hardware components in an autonomous driving vehicle (ADV). In one embodiment, a redundant system can be provided on an independent computing device in an ADV to check for malfunctions in a number of software or hardware components. When no malfunction occurs in the ADV, an autonomous driving system (ADS) in the ADV operates to drive the ADV, while the redundant system can monitor the ADS in a standby mode. In the event of a malfunction, the redundant system can take over the control of the ADV, and take appropriate actions based on a severity level of the malfunction.

Fail-safe handling system for autonomous driving vehicle

Various embodiments of the invention enable an ADV to dynamically adjust its behaviors to emulate behaviors of a vehicle operated by a human driver when the ADV encounters an obstacle. A dynamic cost function can be used to collect real-time values of a set of parameters, and use the real-time values to constantly adjust a preferred safety distance where the ADV can be stopped ahead of the obstacle. An method includes determining a first distance to the obstacle in response to detecting an obstacle ahead of the ADV; and for each of a number of iterations, collecting a real-time value for each of a set of parameters, determining an offset to the first distance using the real-time value for each of the set of parameters, calculating a second distance based on the first distance and the offset, and controlling the ADV in view of the second distance using an expected value of each of the set of parameters, such that the ADV can stop at a point having the second distance to the obstacle.

Implementation of dynamic cost function of self-driving vehicles

A lateral control error of a steering system of an ADV is determined, which includes iteratively performing following operations for a predetermined time period. Whether the ADV is moving within a predetermined proximity of a current moving direction is determined. Next, whether a road condition of a road on which the ADV is driving satisfies a predetermined road condition is determined. Then, a first steering feedback of the ADV in response to a prior steering control command is measured. Thereafter, the lateral control error is determined based on at least a portion of the first steering feedback over the predetermined time period. Further, a steering command in view of the lateral control error of the steering system is generated. Finally, the steering command is applied to control the ADV to compensate the lateral control error of the steering system.

Feedback based real time steering calibration system

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

Techniques are described for invoking and switching between chatbots of a chatbot system. In some embodiments, the chatbot system is capable of routing an utterance received while a user is already interacting with a first chatbot in the chatbot system. For instance, the chatbot system may identify a second chatbot based on determining that (i) such an utterance is an invalid input to the first chatbot or (ii) that the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. Identifying the second chatbot can involve computing, using a predictive model, separate confidence scores for the first chatbot and the second chatbot, and then determining that a confidence score for the second chatbot satisfies one or more confidence score thresholds. The utterance is then routed to the second chatbot based on the identifying of the second chatbot.

Routing for chatbots

According to various embodiments, described herein are systems and methods for training machine learning (ML) models to generate real-time scores to predict the probabilities of task completion. In one embodiment, an exemplary method includes the operations of receiving, from a data store, a set of features and a workflow for training a first type of ML models, the workflow specifying a data source, a number of stages and associated parameters for training the ML models; retrieving, from the data source, training data for the set of features; and segmenting the training data into different segments. The method further includes the operations of training a separate first type of ML model using each of the different segment of the training data in accordance with the workflow; and persisting the first type of trained ML models into the data storage. The method also includes using a trained ML model to generate probability scores and displaying the scores to users in real-time.

System and method for generating scores for predicting probabilities of task completion

The disclosure describes various embodiments for monitoring safety of an autonomous driving vehicle (ADV). In one embodiment, a method includes the operations of receiving, by a vehicle controller, one or more error message from a patrol module, the one or more error messages generated by an autonomous driving system of the ADV operating in an autonomous mode, the patrol module monitoring the autonomous driving system; evaluating a status of the autonomous driving system based on the one or more error messages; and keeping the ADV in the autonomous mode or switching it to a manual mode based on the status of the autonomous driving system.

Automatic driving safety interaction system

In response to a request to pull over an ADV at a destination point at a side of a lane, a path including a first segment, a second segment and a transition point is planned. The transition point is determined based on at least one of a distance to the destination point or a predetermined distance to a boundary of the side of the lane. The first segment from a start point to the transition point is generated by using a quadratic programming (QP) operation. The second segment from the transition point to the destination is generated based on a shape of the boundary. The ADV is controlled to pull over to the destination point according to the planned path.

Pull over method based on quadratic programming for path planning

According to one embodiment, in response to determining that an obstacle blocks at least a portion of a current lane in which an ADV is driving, an obstacle boundary of the obstacle is determined based on the size and shape of the obstacle. A lane configuration is determined based on map data of a map corresponding to a road associated with the lanes. A passing lane boundary that can be utilized by the ADV is determined based on the lane configuration of the road and the obstacle boundary of the obstacle. A passable area is calculated within the passing lane boundary based on a size of the ADV. The passable area is utilized by the ADV to pass the obstacle without collision. Thereafter, a trajectory is planned within the passable area boundary to control the ADV to pass the obstacle.

Method for determining passable area in planning a path of autonomous driving vehicles

In one embodiment, a method includes the operation of segmenting a raw reference line into a plurality of reference line segments, including a first reference line segment, a second reference segment, and a third reference line segment in a sequential order, in response to receiving the raw reference line representing a route from a first location to a second location associated with an autonomous driving vehicle (ADV). The method further includes the operations of smoothing the first reference line segment and the third reference line segment using a Quadratic programming (QP) spline smoother; and smoothing the second reference line segment using a spiral smoother. Smoothed reference line segments from the plurality of reference line segments are connected to generate a smoothed reference line, which is to be used as a reference line of the route to control the ADV.

QP spline path and spiral path based reference line smoothing method for autonomous driving

In one embodiment, a method for generating a reference line for operating an autonomous driving vehicle includes determining a first ending reference point having a smallest curvature among a plurality of points within a first defined distance along a path, generating a first reference line based on a first initial reference point and the first ending reference point, determining a second ending reference point having a smallest curvature among a plurality of points within a second defined distance along the path, generating a second reference line based on the first and second ending reference points and an end section of the first reference line, connecting the first and second reference lines, and controlling the autonomous driving vehicle along the connected first reference line and the second reference line.

Multi-point enforced based stitch method to connect two smoothed reference lines

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

In one embodiment, an autonomous driving system perceives a driving environment surrounding an ADV based on sensor data obtained from various sensors, including identifying one or more objects. For each of the objects, an arc curve is generated connecting the object and a current location of the ADV using a predetermined algorithm. A curvature of the arc curve is calculated. One of the arc curves is selected in which the curvature of the selected arc curve satisfies a predetermined condition, such as the lowest curvature amongst all objects. A reference line is then generated by connecting the selected object and the current location of the ADV. The connected line between the selected object and the ADV is utilized as a part of a reference line. The reference line is utilized to generate a trajectory to drive the ADV.

Method and system for generating reference lines for autonomous driving vehicles

Growing community of inventors

Beijing, China

Xin Xu