公开(公告)号：US20210139038A1

公开(公告)日：2021-05-13

申请号：US16682445

申请日：2019-11-13

申请人： Baidu USA LLC

发明人： Yu WANG ,  Qi LUO ,  Shu JIANG ,  Jinghao MIAO ,  Jiangtao HU ,  Jingao WANG ,  Jinyun ZHOU ,  Runxin HE ,  Jiaxuan XU

IPC分类号： B60W50/00 ,  G05D1/00 ,  B60W30/18

摘要： In one embodiment, a method of generating control effort to control an autonomous driving vehicle (ADV) includes determining a gear position (forward or reverse) in which the ADV is driving and selecting a driving model and a predictive model based upon the gear position. In a forward gear, the driving model is a dynamic model, such as a “bicycle model,” and the predictive model is a look-ahead model. In a reverse gear, the driving model is a hybrid dynamic and kinematic model and the predictive model is a look-back model. A 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, and an augmented control logic determines a second, additional, control effort, to determine a final control effort that is output to a control module of the ADV to drive the ADV.

公开(公告)号：US20210116916A1

公开(公告)日：2021-04-22

申请号：US16659963

申请日：2019-10-22

申请人： Baidu USA LLC

发明人： Runxin HE ,  Yu WANG ,  Jinyun ZHOU ,  Qi LUO ,  Jinghao MIAO ,  Jiangtao HU ,  Jingao WANG ,  Jiaxuan XU ,  Shu JIANG

IPC分类号： G05D1/00 ,  G05D1/02

摘要： 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.

公开(公告)号：US20200342693A1

公开(公告)日：2020-10-29

申请号：US16397633

申请日：2019-04-29

申请人： Baidu USA LLC

发明人： Shu JIANG ,  Qi LUO ,  Jinghao MIAO ,  Jiangtao HU ,  Weiman LIN ,  Jiaxuan XU ,  Yu WANG ,  Jinyun ZHOU ,  Runxin HE

IPC分类号： G07C5/08 ,  G06N20/00 ,  G05D1/00

摘要： An autonomous driving vehicle (ADV) receives instructions for a human test driver to drive the ADV in manual mode and to collect a specified amount of driving data for one or more specified driving categories. As the user drivers the ADV in manual mode, driving data corresponding to the one or more driving categories is logged. A user interface of the ADV displays the one or more driving categories that the human driver is instructed collect data upon, and a progress indicator for each of these categories as the human driving progresses. The driving data is uploaded to a server for machine learning. If the server machine learning achieves a threshold grading amount of the uploaded data to variables of a dynamic self-driving model, then the server generates an ADV self-driving model, and distributes the model to one or more ADVs that are navigated in the self-driving mode.