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公开(公告)号：US10732634B2
公开(公告)日：2020-08-04
申请号：US15640842
申请日：2017-07-03
申请人： Baidu USA LLC
发明人： Yiqing Yang , Siyang Yu , Xuan Liu , Yu Cao , Zhang Li , Jun Zhan , Guang Yang
摘要： An event queue is maintained to store IO events generated from a number of sensors and timer events generated for a number of autonomous driving modules. For each of the events pending in the event queue, in response to determining that the event is an IO event, the data associated with the IO event is stored in a data structure associated with the sensor in a global store. In response to determining that the event is a timer event, a worker thread associated with the timer event is launched. The worker thread executes one of the autonomous driving modules triggered or initiated the timer event. Input data is retrieved from the global store and provided to the worker thread to allow the worker thread to process the input data.
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2.发明申请公开(公告)号：US20190318267A1
公开(公告)日：2019-10-17
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：US10031526B1
公开(公告)日：2018-07-24
申请号：US15640885
申请日：2017-07-03
申请人： Baidu USA LLC
发明人： Zhang Li , Jun Zhan , Yiqing Yang , Xuan Liu , Yu Cao , Siyang Yu , Guang Yang
摘要： Described is a system (and method) for generating a driving scenario for an autonomous driving simulator. The system may use a camera mounted to a vehicle as a cost effective approach to obtain real-life driving scenario data. The system may then analyze the two-dimensional image data to create a three-dimensional driving simulation. The analysis may include detecting objects (e.g. vehicles, pedestrians, etc.) within the two-dimensional image data and determining movements of the object based on a position, trajectory, and velocity of the object. The determined information of the object may then be projected onto a map that may be used for generating the three-dimensional driving simulation. The use of cost-effective cameras provides the ability to obtain vast amounts of driving image data that may be used to provide an extensive coverage of the potential types of driving scenarios an autonomous vehicle may encounter.
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4.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：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：US10747228B2
公开(公告)日：2020-08-18
申请号：US15640875
申请日：2017-07-03
申请人： Baidu USA LLC
发明人： Jun Zhan , Yiqing Yang , Siyang Yu , Xuan Liu , Yu Cao , Zhang Li , Guang Yang
摘要： An autonomous driving system includes a number of sensors and a number of autonomous driving modules. The autonomous driving system further includes a global store to store data generated and used by processing modules such as sensors and/or autonomous driving modules. The autonomous driving system further includes a task scheduler coupled to the sensors, the autonomous driving modules, and the global store. In response to output data generated by any one or more of processing modules, the task scheduler stores the output data in the global store. In response to a request from any of the processing modules for processing data, the task scheduler provides input data stored in the global store to the processing module. The task scheduler is executed in a single thread that is responsible for managing data stored in the global store and dispatching tasks to be performed by the processing modules.
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