Baidu USA LLC

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

Jingao Wang

Guang Yu Yang

Fan Zhu

Qi Kong

Dong Li

Jiangtao Hu

Yifei Jiang

Qi Luo

Liyun Li

Jinghao Miao

Xiang Yu

Sen Hu

Jiaming Tao

Yu Wang

Shu Jiang

Jinyun Zhou

Jiaxuan Xu

Yuchang Pan

Zhenguang Zhu

Li Zhuang

Shuang Wu

Liangliang Zhang

Haoyang Fan

Jiarui He

Zhuang Li

Runxin He

Weicheng Zhu

I-Hsuan Yang

Chunming Zhao

Shiyuan Fang

Yajia Zhang

Zhongpu Xia

Xiaoxin Fu

Hongye Li

Lin Ma

Xin Xu

Shaoshan Liu

James P Peng

Wenli Yang

Youling Zou

Yu Long Cao

Kecheng Xu

Hui Jiang

Xiangquan Xiao

Longtao Lin

Lei Ma

Yu Cao

Fuxiao Xin

Zongbao Feng

Guang Yang

A method of navigating an autonomous driving vehicle (ADV) includes determining a target function for an open space model based on one or more obstacles and map information within a proximity of the ADV, then iteratively performing first and second quadratic programming (QP) optimizations on the target function. Then, generating a second trajectory based on results of the first and second QP optimizations to control the ADV autonomously using the second trajectory. The first QP optimization is based on fixing a first set of variables of the target function. The second QP optimization is based on maximizing a sum of the distances from the ADV to each of the obstacles over a plurality of points of the first trajectory, and minimizing a difference between a target end-state of the ADV and a determined final state of the ADV using the first trajectory.

End dynamics and constraints relaxation algorithm on optimizing an open space trajectory

Systems and methods are disclosed for collecting driving data from simulated autonomous driving vehicle (ADV) driving sessions and real-world ADV driving sessions. The driving data is processed to exclude manual (human) driving data and to exclude data corresponding to the ADV being stationary (not driving). Data can further be filtered based on driving direction: forward or reverse driving. Driving data records are time stamped. The driving data can be aligned according to the timestamp, then a standardized set of metrics is generated from the collected, filtered, and time-aligned data. The standardized set of metrics are used to grade the performance the control system of the ADV, and to generate an updated ADV controller, based on the standardized set of metrics.

Cross-platform control profiling tool for autonomous vehicle control

Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV). A control input is received from an ADV perception and planning system. The control input is translated in a control command to a control subsystem of the ADV. A reference actuation output is obtained from a storage of the ADV. The reference actuation output is a smoothed output that accounts for second order actuation dynamic delays attributable to the control subsystem actuator. Based on a difference between the control input and the reference actuation output, adaptive gains are determined and applied to the input control signal to reduce error between the control output and the reference actuation output.

Model reference adaptive control algorithm to address the vehicle actuation dynamics

Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV) and increasing control system bandwidth by accounting for time-latency in a control subsystem actuation system. A control input is received from an ADV's autonomous driving system. The control input is translated into a control command of the control subsystem of the ADV. A reference actuation output and a predicted actuation output are generated corresponding to a by-wire ('real') actuation action for the control subsystem. A control error is determined between the reference actuation action and the by-wire actuation action. A predicted control error is determined between the predicted actuation action and the between the by-wire actuation action. Adaptive gains are determined and applied to the by-wire actuation action to generate a second by-wire actuation action.

Extended model reference adaptive control algorithm for the vehicle actuation time-latency

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 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

Generating control effort to control an autonomous driving vehicle (ADV) includes determining a direction (forward or reverse) in which the ADV is driving and selecting a driving model and a predictive model based upon the direction. In a forward direction, the driving model is a dynamic model, such as a 'bicycle model,' and the predictive model is a look-ahead model. In a reverse direction, the driving model is a hybrid dynamic and kinematic model and the predictive model is a look-back model. Current and predicted lateral error and heading error are determined using the driving model and predictive model, respectively. A linear quadratic regulator (LQR) uses the current and predicted lateral error and heading errors to determine a first control effort An augmented control logic determines a second, additional, control effort, to determine a final control effort that is output to a control module to drive the ADV.

Low-speed, backward driving vehicle controller design

In one embodiment, an autonomous driving system of an autonomous driving vehicle perceives a driving environment surrounding the autonomous driving vehicle traveling along a path, including perceiving an obstacle in the driving environment. The system detects a vertical acceleration of the autonomous driving vehicle based on sensor data obtained from a sensor on the autonomous driving vehicle. The system further calibrates the perceived obstacle based on the vertical acceleration of the autonomous driving vehicle. The system then controls the autonomous driving vehicle to navigate through the driving environment in view of the calibrated perceived obstacle.

Obstacle perception calibration system for autonomous driving vehicles

Based on sensor data obtained from a variety of sensors, a driving environment surrounding an autonomous driving vehicle (ADV) is perceived, including perceiving and identifying one or more obstacles. A trajectory is planned based on the perception data according to a set of rules to drive the ADV navigating through the driving environment. Trajectory data representing the trajectory is generated, where the trajectory data includes information indicating target or expected vehicle states at different points in time along the trajectory. The trajectory data is then transmitted in a sequence or a stream of one or more controller area network (CAN) messages to an electronic control unit (ECU) of the ADV over a CAN bus. The ECU is configured to generate and issue one or more control commands (e.g., throttle, brake, steering commands) based on the trajectory data to drive the ADV according to the trajectory.

Communications protocols between planning and control of autonomous driving vehicle

In response to a search query received from a client, a content item is identified from a content database based on one or more keywords associated with the search query. In addition, a search is performed in an image store to identify a list of image candidates based on one or more keywords associated with the search query. An image is selected from the list of image candidates using an image selection algorithm. Metadata associated with the content provider is inscribed onto the selected image to generate a customized image. The customized image is integrated with the content item to generate a composite content item such as a poster. As a result, the content represented by the composite content item is closely tied to the content provider. The composite content item is then transmitted to the client.

Method and system for dynamically overlay content provider information on images matched with content items in response to search queries

In one embodiment, a set of parameters representing a first state of an autonomous driving vehicle (ADV) to be simulated and a set of control commands to be issued at a first point in time. In response, a localization predictive model is applied to the set of parameters to determine a first position (e.g., x, y) of the ADV. A localization correction model is applied to the set of parameters to determine a set of localization correction factors (e.g., &#x394;x, &#x394;y). The correction factors may represent the errors between the predicted position of the ADV by the localization predictive model and the ground truth measured by sensors of the vehicle. Based on the first position of the ADV and the correction factors, a second position of the ADV is determined as the simulated position of the ADV.

Dynamic model with learning based localization correction system

A social driving style learning framework or system for autonomous vehicles is utilized, which can dynamically learn the social driving styles from surrounding vehicles and adopt the driving style as needed. Each of the autonomous vehicles within a particular driving area is equipped with the driving style learning system to perceive the driving behaviors of the surrounding vehicles to derive a set of driving style elements. Each autonomous vehicle transmits the driving style elements to a centralized remote server. The server aggregates the driving style elements collected from the autonomous vehicles to determine a driving style corresponding to that particular driving area. The server transmits the driving style back to each of the autonomous vehicles. The autonomous vehicles can then decide whether to adopt the driving style, for example, to follow the traffic flow with the rest of the vehicles nearby.

Group driving style learning framework for autonomous vehicles

Systems and methods are disclosed for identifying time-latency and subsystem control actuation dynamic delay due to second order dynamics that are neglected in control systems of the prior art. Embodiments identify time-latency and subsystem control actuation delays by developing a discrete-time dynamic model having parameters and estimating the parameters using a least-squares method over selected crowd-driving data. After estimating the model parameters, the model can be used to identify dynamic actuation delay metrics such as time-latency, rise time, settling time, overshoot, bandwidth, and resonant peak of the control subsystem. Control subsystems can include steering, braking, and throttling.

Autonomous vehicle actuation dynamics and latency identification

In one embodiment, an intelligent and prompt alarm system is designed on autonomous driving vehicles to help autonomous driving vehicles to communicate to human drivers more vigilantly and promptly, and to improve human driver's performance to take over when an autonomous driving failure occurs. In one embodiment, an alarm system can be developed several levels: 1) basic warning, 2) risk warning, and 3) emergency/take-over alarming.

Alarm system of autonomous driving vehicles (ADVs)

According to one embodiment, an ADV is controlled according to a first trajectory planned during a first planning cycle. A control error is determined which represents a drifting error at a first location of the ADV at a first point in time at the end of the first planning cycle. A second point in time is selected on the first trajectory. A second trajectory is generated from a second location on the first trajectory corresponding o the second point in time as a starting location of the second trajectory for a second planning cycle as a next planning cycle. A segment of the first trajectory between the first point in time and the second point in time is combined with the second trajectory to generate a third trajectory for the second planning cycle. The ADV is driven and controlled according to the third trajectory corresponding to the second planning cycle.

Method and system for stitching planning trajectories from consecutive planning cycles for smooth control execution of autonomous driving vehicles

Growing community of inventors

Sunnyvale, CA, United States of America

Jingao Wang