- 专利标题: Training algorithm for collision avoidance
-
申请号: US15007024申请日: 2016-01-26
-
公开(公告)号: US10474964B2公开(公告)日: 2019-11-12
- 发明人: Ashley Elizabeth Micks , Jinesh J Jain , Harpreetsingh Banvait , Kyu Jeong Han
- 申请人: Ford Global Technologies, LLC
- 申请人地址: US MI Dearborn
- 专利权人: FORD GLOBAL TECHNOLOGIES, LLC
- 当前专利权人: FORD GLOBAL TECHNOLOGIES, LLC
- 当前专利权人地址: US MI Dearborn
- 代理机构: Stevens Law Group
- 代理商 David R. Stevens
- 主分类号: G06N20/00
- IPC分类号: G06N20/00 ; B60W30/09 ; G06F17/50 ; G06K9/00 ; G06K9/66 ; G06N3/08 ; H04R29/00
摘要:
A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a lane-splitting vehicle. The location of the lane-splitting vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of a lane-splitting vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a lane-splitting vehicle based on actual sensor outputs input to the machine learning model.
公开/授权文献
- US20170213149A1 Training Algorithm for Collision Avoidance 公开/授权日:2017-07-27
信息查询