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公开(公告)号：US11409284B2
公开(公告)日：2022-08-09
申请号：US16413315
申请日：2019-05-15
申请人： Baidu USA LLC
发明人： Runxin He , Jinyun Zhou , Qi Luo , Shiyu Song , Jinghao Miao , Jiangtao Hu , Yu Wang , Jiaxuan Xu , Shu Jiang
IPC分类号： G01C22/00 , G05D1/00 , G05D1/02 , G06F16/9537
摘要： In one embodiment, an open space model is generated for a system to plan trajectories for an ADV in an open space. The system perceives an environment surrounding an ADV including one or more obstacles. The system determines a target function for the open space model based on constraints for the one or more obstacles and map information. The system iteratively, performs a first quadratic programming (QP) optimization on the target function based on a first trajectory while fixing a first set of variables, and performs a second QP optimization on the target function based on a result of the first QP optimization while fixing a second set of variables. The system generates a second trajectory based on results of the first and the second QP optimizations to control the ADV autonomously according to the second trajectory.
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12.发明授权公开(公告)号：US11328219B2
公开(公告)日：2022-05-10
申请号：US15952089
申请日：2018-04-12
申请人： Baidu USA LLC
发明人： Liangliang Zhang , Siyang Yu , Dong Li , Jiangtao Hu , Jiaming Tao , Yifei Jiang
摘要： 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.
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公开(公告)号：US11269329B2
公开(公告)日：2022-03-08
申请号：US16659040
申请日：2019-10-21
申请人： Baidu USA LLC
发明人： Shu Jiang , Qi Luo , Jinghao Miao , Jiangtao Hu , Jiaxuan Xu , Jingao Wang , Yu Wang , Jinyun Zhou , Runxin He
摘要： 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., Δx, Δy). 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.
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公开(公告)号：US11199846B2
公开(公告)日：2021-12-14
申请号：US16204941
申请日：2018-11-29
申请人： Baidu USA LLC
发明人： Qi Luo , Jiaxuan Xu , Kecheng Xu , Xiangquan Xiao , Siyang Yu , Jinghao Miao , Jiangtao Hu
摘要： In an embodiment, a learning-based dynamic modeling method is provided for use with an autonomous driving vehicle. A control module in the ADV can generate current states of the ADV and control commands for a first driving cycle, and send the current states and control commands to a dynamic model implemented using a trained neural network model. Based on the current states and the control commands, the dynamic model generates expected future states for a second driving cycle, during which the control module generates actual future states. The ADV compares the expected future states and the actual future states to generate a comparison result, for use in evaluating one or more of a decision module, a planning module and a control module in the ADV.
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公开(公告)号：US11180165B2
公开(公告)日：2021-11-23
申请号：US16727799
申请日：2019-12-26
申请人： Baidu USA LLC
发明人： Jinyun Zhou , Shu Jiang , Jiaming Tao , Qi Luo , Jinghao Miao , Jiangtao Hu , Jiaxuan Xu , Yu Wang
摘要： In one embodiment, an autonomous driving vehicle (ADV) operates in an on-lane mode, where the ADV follows a path along a vehicle lane. In response to determining that the ADV is approaching a dead-end, the ADV switches to an open-space mode. While in the open-space mode, the ADV conducts a three-point turn using a series of steering and throttle commands to generate forward and reverse movements until the ADV is within a) a threshold heading, and b) a threshold distance, relative to the vehicle lane. The ADV can then return to the on-lane mode and resume along the vehicle lane away from the dead-end.
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公开(公告)号：US11073831B2
公开(公告)日：2021-07-27
申请号：US16163500
申请日：2018-10-17
申请人： Baidu USA LLC
发明人： Yifei Jiang , Liangliang Zhang , Dong Li , Jiaming Tao , Jiangtao Hu
摘要： In one embodiment, perception data is received from a number of autonomous driving vehicles (ADVs) over a network. The perception data includes information describing a set of trajectories that a number of vehicles having driven through a road segment of a road and perceived by one or more ADVs using their respective sensors driving on the same road segment. In response to the perception data, an analysis is performed on the perception data, i.e., the trajectories, to determine one or more lanes within the road segment. For each of the lanes, a lane reference line associated with the lane is calculated based on the trajectories within the corresponding lane. The lane metadata describing the lane reference lines for the one or more lanes are stored in a lane configuration data structure such as a database. The lane configuration database can then be utilized for autonomous driving at real-time subsequently without having to use a high definition map.
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公开(公告)号：US11055857B2
公开(公告)日：2021-07-06
申请号：US16206430
申请日：2018-11-30
申请人： Baidu USA LLC
发明人： Liangliang Zhang , Hongyi Sun , Dong Li , Jiangtao Hu , Jinghao Miao
IPC分类号： G06T7/246 , G06K9/00 , G06T7/90 , G06K9/46 , G06K9/62 , G06N3/04 , G05D1/00 , G05D1/02 , G06N3/08
摘要： In an embodiment, a method for representing a surrounding environment of an ego autonomous driving vehicle (ADV) is described. The method represents the surrounding environment using a first set of features from a definition (HD) map and a second set of features from a target object in the surrounding environment. The first set of features are extracted from the high definition map using a convolutional neural network (CNN), and the second set of features are handcrafted features from the target object during a predetermined number of past driving cycles of the ego ADV. The first set of features and the second set of features are concatenated and provided to a number of fully connected layers of the CNN to predict behaviors of the target object. In one embodiment, the operations in the method can be repeated for each driving cycle of the ego ADV.
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公开(公告)号：US10852736B2
公开(公告)日：2020-12-01
申请号：US15944783
申请日：2018-04-03
申请人： Baidu USA LLC
发明人： Jiaming Tao , Yifei Jiang , Liangliang Zhang , Dong Li , Jiangtao Hu , Fan Zhu
IPC分类号： G05D1/02 , G01C21/34 , G08G1/0965
摘要： In one embodiment, a system sends current location information of the ADV to an alert service over a network, where the alert service is communicatively coupled to a number of ADVs. The system receives a broadcasted alert signal from the alert service, where the alert service has determined that the ADV is or will be located in an alert area, and the location of the alert area is determined based on a location of a dispatched vehicle having a higher priority of traveling. In response to receiving the broadcast alert signal, the system examines a current state and the current location of the ADV in view of the alert area to determine whether the ADV should overtake or yield the alert area based on a set of rules. The system generates a trajectory to control the ADV to navigate the alert area based on the examination.
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公开(公告)号：US10816990B2
公开(公告)日：2020-10-27
申请号：US15851535
申请日：2017-12-21
申请人： Baidu USA LLC
发明人： Dong Li , Liangliang Zhang , Yifei Jiang , Jiangtao Hu
摘要： According to some embodiments, a system generates a driving trajectory from a starting point to a destination point for an ADV. In one embodiment, the system calculates a first trajectory based on a map and a route information. The system generates a path profile based on the first trajectory, traffic rules, and blocking obstacles perceived by the ADV. The system determines non-blocking obstacles perceived by the ADV. The system generates a speed profile of the ADV for the path profile based on the non-blocking obstacles to identify a speed of the ADV to avoid the blocking obstacles in view of the non-blocking obstacles. The system generates a second trajectory based on the path profile and the speed profile to control the ADV autonomously according to the second trajectory.
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公开(公告)号：US10775801B2
公开(公告)日：2020-09-15
申请号：US15916156
申请日：2018-03-08
申请人： Baidu USA LLC
发明人： Liangliang Zhang , Haoyang Fan , Dong Li , Jiangtao Hu , Yifei Jiang
IPC分类号： B60W40/072 , G05D1/02
摘要： A station-time (S-T) graph may be obtained in response to a first reference line representing a path from a first location to a second location associated with an autonomous driving vehicle (ADV). One or more kernels may be applied to the S-T graph. Each of the one or more kernels may indicate a plurality of points on the S-T graph. One or more constraints may be applied to the S-T graph. Each of the one or more constraints may indicate a condition for points in the S-T graph. A set of speeds for portions of the path is determined based on the one or more kernels and the one or more constraints.
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