- 专利标题: MACHINE-LEARNING BASED SYSTEM FOR PATH AND/OR MOTION PLANNING AND METHOD OF TRAINING THE SAME
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申请号: US16810552申请日: 2020-03-05
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公开(公告)号: US20210276598A1公开(公告)日: 2021-09-09
- 发明人: Elmira AMIRLOO ABOLFATHI , Mohsen ROHANI , Jason Philip KU , Jun LUO
- 申请人: Elmira AMIRLOO ABOLFATHI , Mohsen ROHANI , Jason Philip KU , Jun LUO
- 申请人地址: CA North York; CA Gatineau; CA Toronto; CA Toronto
- 专利权人: Elmira AMIRLOO ABOLFATHI,Mohsen ROHANI,Jason Philip KU,Jun LUO
- 当前专利权人: Elmira AMIRLOO ABOLFATHI,Mohsen ROHANI,Jason Philip KU,Jun LUO
- 当前专利权人地址: CA North York; CA Gatineau; CA Toronto; CA Toronto
- 主分类号: B60W60/00
- IPC分类号: B60W60/00 ; B60W30/09 ; G06K9/00
摘要:
A system and method for path and/or motion planning and for training such a system are described. In one aspect, the method comprises generating a sequence of predicted occupancy grid maps (OGMs) for T-T1 time steps based on a sequence of OGMs for 0-T1 time steps, a reference map of an environment in which an autonomous vehicle is operating, and a trajectory. A cost volume is generated for the sequence of predicted OGMs. The cost volume comprises a plurality of cost maps for T-T1 time steps. Each cost map corresponds to a predicted OGM in the sequence of predicted OGMs and has the same dimensions as the corresponding predicted OGM. Each cost map comprises a plurality of cells.
Each cell in the cost map represents a cost of the cell in corresponding predicted OGM being occupied in accordance with a policy defined by a policy function.
Each cell in the cost map represents a cost of the cell in corresponding predicted OGM being occupied in accordance with a policy defined by a policy function.
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