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公开(公告)号：US20170169705A1
公开(公告)日：2017-06-15
申请号：US14969151
申请日：2015-12-15
Applicant: NISSAN NORTH AMERICA, INC.
Inventor： ALI MORTAZAVI , VIKRAM KRISHNAMURTHY
CPC classification number: G01C21/34 , G01C21/3492 , G08G1/096716 , G08G1/096725 , G08G1/096741 , G08G1/09675 , G08G1/096775 , G08G1/096791 , H04L67/12 , H04W4/44 , H04W84/005 , H04W84/18
Abstract: A method and apparatus for traffic signal timing estimation are disclosed. Traffic signal timing estimation may include a vehicle identifying transportation network information representing a vehicle transportation network including an intersection. The transportation network information may include expected traffic control device state information corresponding to the intersection, such as an artificial neural network based machine learning model trained for the intersection. The vehicle may identify a route through the vehicle transportation network that includes the intersection based on the expected traffic control device state information and may control the traversal of the vehicle transportation network by the vehicle based on the expected traffic control device state information to minimize one or more operational cost metrics. Training the model may include identifying training data from input data previously generated and stored by a traffic control device controller of the intersection.
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公开(公告)号：US20170153642A1
公开(公告)日：2017-06-01
申请号：US14954220
申请日：2015-11-30
Applicant: Nissan North America, Inc.
Inventor： ROBERT SCHEEPJENS , ALI MORTAZAVI , VIKRAM KRISHNAMURTHY
CPC classification number: G05D1/0212 , B60W30/00 , G01C21/3446 , G05D1/0223 , G05D2201/0213
Abstract: A method and apparatus for traffic signal timing estimation are disclosed. Traffic signal timing estimation may include a vehicle identifying transportation network information representing a vehicle transportation network including an intersection. The transportation network information may include expected traffic control device state information corresponding to the intersection, such as a support vector regression based machine learning model trained for the intersection. The vehicle may identify a route through the vehicle transportation network that includes the intersection based on the expected traffic control device state information and may control the traversal of the vehicle transportation network by the vehicle based on the expected traffic control device state information to minimize one or more operational cost metrics. Training the model may include identifying training data from input data previously generated and stored by a traffic control device controller of the intersection.
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公开(公告)号：US20170154529A1
公开(公告)日：2017-06-01
申请号：US14954083
申请日：2015-11-30
Applicant: Nissan North America, Inc.
Inventor： YUE ZHAO , ALI MORTAZAVI
IPC: G08G1/0968 , G05D1/02 , G08G1/01 , B60W30/12
CPC classification number: G08G1/0145 , B60W30/12 , B60W2600/00 , G08G1/0112 , G08G1/163 , G08G1/167
Abstract: A host vehicle receives remote vehicle spatial state information for a remote vehicle and identifies vehicle transportation network information representing a portion of a transportation network based on that information. At least one initial probability value is generated based on comparing the spatial state information and the transportation network information at an initial time point, each initial probability value indicating a likelihood that the remote vehicle is following a lane within the transportation network. A deviation between adjacent values for the spatial state information relative to the transportation network information is generated for a plurality of time points. For each single lane and deviation, the likelihood that the remote vehicle is following the lane using a new probability value based on the deviation is updated, a trajectory using the updated likelihood is generated, and the host vehicle traverses the transportation network using the transportation network information and trajectory.
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