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公开(公告)号：US20230042001A1
公开(公告)日：2023-02-09
申请号：US17396405
申请日：2021-08-06
申请人： Baidu USA LLC
发明人： Shu JIANG , Qi LUO , Yu CAO , Weiman LIN
IPC分类号： B60W60/00 , B60W40/072 , B60W40/04
摘要： In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.
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公开(公告)号：US20230065284A1
公开(公告)日：2023-03-02
申请号：US17446652
申请日：2021-09-01
申请人： Baidu USA LLC
发明人： Shu JIANG , Weiman LIN , Yu CAO , Yu WANG , Qi LUO , Jiangtao HU , Jinghao MIAO
IPC分类号： B60W30/09 , B60W60/00 , B60W30/095
摘要： Systems, methods, and media for factoring localization uncertainty of an ADV into its planning and control process to increase the safety of the ADV. The uncertainty of the localization can be caused by sensor inaccuracy, map matching algorithm inaccuracy, and/or speed uncertainty. The localization uncertainty can have negative impact on trajectory planning and vehicle control. Embodiments described herein are intended to increase the safety of the ADV by considering localization uncertainty in trajectory planning and vehicle control. An exemplary method includes determining a confidence region for an ADV that is automatically driving on a road segment based on localization uncertainty and speed uncertainty; determining that an object is within the confidence region, and a probability of collision with the ADV based on a distance of the object to the ADV; and planning a trajectory based on the probability of collision, and controlling the ADV based on the probability of collision.
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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
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.
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公开(公告)号：US20240034353A1
公开(公告)日：2024-02-01
申请号：US17815881
申请日：2022-07-28
申请人： Baidu USA LLC
发明人： Yu CAO , Weiman LIN , Shu JIANG , Szu Hao WU , Jiangtao HU
CPC分类号： B60W60/0011 , B60W40/06
摘要： Embodiments of the invention are provided to automatically generate corner simulation scenarios. In an embodiment, an exemplary method includes performing the following operations for a predetermined number of iterations for each set of predefined parameters. The operations include generating a set of parameter values for the set of predefined parameters; determining whether the set of parameter values is valid or invalid based on a set of predefined metrics; and if the set of parameter values is valid, performing a simulation task to simulate a trajectory planner of the ADV in a simulation scenario configured by the set of parameter values. The method further includes calculating a performance score for the simulation task; and if the performance score of the simulation task is below a predetermined threshold, saving the set of parameter values in a storage, wherein the set of parameter values is used for re-tuning the trajectory planner.
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公开(公告)号：US20230046149A1
公开(公告)日：2023-02-16
申请号：US17398359
申请日：2021-08-10
申请人： Baidu USA LLC
发明人： Shu JIANG , Qi LUO , Yu CAO , Weiman LIN , Yu WANG , Hongyi SUN
摘要： According to various embodiments, systems, methods, and media for evaluating an open space planner in an autonomous vehicle are disclosed. In one embodiment, an exemplary method includes receiving, at a profiling application, a record file recorded by the ADV while driving in an open space using the open space planner, and a configuration file specifying parameters of the ADV; extracting planning messages and prediction messages from the record file, each extracted message being associated with the open space planner. The method further includes generating features from the planning message and the prediction messages in view of the specified parameters of the ADV; and calculating statistical metrics from the features. The statistical metrics are then provided to an automatic tuning framework for tuning the open space planner.
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公开(公告)号：US20220097728A1
公开(公告)日：2022-03-31
申请号：US17039685
申请日：2020-09-30
申请人： Baidu USA LLC
发明人： Weiman LIN , Yu CAO , Yu WANG , Qi LUO , Shu JIANG , Xiangquan XIAO , Longtao LIN , Jinghao MIAO , Jiangtao HU
摘要： Systems and methods are disclosed for optimizing values of a set of tunable parameters of an autonomous driving vehicle (ADV). The controllers can be a linear quadratic regular, a “bicycle model,” a model-reference adaptive controller (MRAC) that reduces actuation latency in control subsystems such as steering, braking, and throttle, or other controller (“controllers”). An optimizer selects a set tunable parameters for the controllers. A task distribution system pairs each set of parameters with each of a plurality of simulated driving scenarios, and dispatches a task to the simulator to perform the simulation with the set of parameters. Each simulation is scored. A weighted score is generated from the simulation. The optimizer uses the weighted score as a target objective for a next iteration of the optimizer, for a fixed number of iterations. A physical real-world ADV is navigated using the optimized set of parameters for the controllers in the ADV.
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公开(公告)号：US20240001966A1
公开(公告)日：2024-01-04
申请号：US17810012
申请日：2022-06-30
申请人： Baidu USA LLC
发明人： Shu JIANG , Yu CAO , Weiman LIN , Szu Hao WU , Jiangtao HU
CPC分类号： B60W60/00276 , G06V20/58 , G06V10/82 , B60W2554/4046
摘要： According to various embodiments, the disclosure discloses systems, methods and media for formulating training datasets for learning-based components in an autonomous driving vehicle (ADV). In an embodiment, an exemplary method includes allocating training datasets for training a learning-based model in the ADV, each training dataset being allocated to one of multiple predefined driving scenarios; determining a weight of each training dataset out of the training datasets; and optimizing the weight of each training dataset in one or more iterations according to a predetermined algorithm until a performance of the learning-based model reaches a predetermined threshold. The predetermined algorithm is one of a random search algorithm, a grid search algorithm, or a Bayesian algorithm.
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公开(公告)号：US20230391356A1
公开(公告)日：2023-12-07
申请号：US17805000
申请日：2022-06-01
申请人： Baidu USA LLC
发明人： Shu JIANG , Szu Hao WU , Yu CAO , Weiman LIN , Jiangtao HU
CPC分类号： B60W60/001 , G06V20/588 , G06V20/582 , G06K9/6257
摘要： According to some embodiments, systems, methods and media for dynamically generating scenario parameters for an autonomous driving vehicles (ADV) are described. In one embodiment, when an ADV enters a driving scenario, the ADV can invoke a map-based scenario checker to determine the type of scenario, and invokes a corresponding neural network model to generate a set of parameters for the scenario based on real-time environmental conditions (e.g., traffics) and vehicle status information (e.g., speed). The set of scenario parameters can be a set of extra constraints for configuring the ADV to drive in a driving mode corresponding to the scenario.
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公开(公告)号：US20230202517A1
公开(公告)日：2023-06-29
申请号：US17645857
申请日：2021-12-23
申请人： Baidu USA LLC
发明人： Shu JIANG , Weiman LIN , Yu CAO , Jiangtao HU , Jinghao MIAO
CPC分类号： B60W60/0011 , B60W60/0015 , B60W30/143 , B60W40/02 , G06V20/58 , G06N3/02 , B60W2555/60 , B60W2556/40
摘要： According to some embodiments, described herein is a method and a system for guaranteeing safety at a control level of an ADV when at least a portion of a planned path generated by a planning module of the ADV is uncertain due to traffics and/or road condition changes. The planning module, when generating a path, also generate a confidence level of each segment of the path based on one or more of perception data, map information, or traffic rules. The confidence levels are decreasing further away from the ADV. When the control module of the ADV obtains the path and the associated confidence levels, the control module issue control commands to track only one or two segments whose confidence levels exceeds a threshold hold, and issue default control commands for the rest of the path.
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公开(公告)号：US20230159047A1
公开(公告)日：2023-05-25
申请号：US17456545
申请日：2021-11-24
申请人： Baidu USA LLC
发明人： Shu JIANG , Zikang XIONG , Weiman LIN , Yu CAO , Qi LUO , Jiangtao HU , Jinghao MIAO
CPC分类号： B60W60/001 , B60W50/00 , G06N3/08 , B60W2050/0083 , B60W2050/0008
摘要： Described herein are a method of training a learning-based critic for tuning a rule-based motion planner of an autonomous driving vehicle, a method of tuning a motion planner using an automatic tuning framework that with the learning-based critic. The method includes receiving training data that incudes human driving trajectories and random trajectories derived from the human driving trajectories; training a learning-based critic using the training data; identifying a set of discrepant trajectories by comparing a first set of trajectories, and a second set of trajectories; and refining, at the neural network training platform, the learning-based critic based on the set of discrepant trajectories. The automatic tuning framework can remove human efforts in tedious parameter tuning, reduce tuning time, while retaining the physical and safety constraints of the ruled-based motion planner. Further, the automatic tuning framework can create personalized motion planners when the learning-based critic is trained using different human driving datasets.
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