Vehicle lane placement
First Claim
1. A system, comprising a computer having a processor and a memory, the memory storing instructions executable by the processor including instructions to:
- receive video data from a video data source affixed to a vehicle;
receive light detecting and ranging (LIDAR) data from a LIDAR data source affixed to the vehicle;
send the video data to a recurrent neural network (RNN) that includes feedback elements to provide feedback from a first layer to a second layer to identify a roadway feature;
send the video data to a dynamic convolutional neural network (DCNN) to apply a convolution filter to the video data to generate a convolution image and a pooling filter to the convolution image to develop a feature map to identify the feature;
send the LIDAR data to the RNN to identify the feature;
send the LIDAR data to the DCNN to identify the feature;
send a RNN output value and a DCNN output value to a softmax decision network to aggregate the RNN output value and the DCNN output value;
determine a softmax output based on whether the RNN output value and the DCNN output value are within a range;
determine a vehicle positional location on the roadway of the vehicle based on the softmax output;
compare the vehicle positional location on the roadway relative to an edge of the roadway and an image from a set of training data images to determine an error rate;
determine a change in a weight value for the RNN and the DCNN from the error rate;
apply the change in the weight value to the RNN and the DCNN; and
control at least a steering, a braking and an acceleration of the vehicle from at least the vehicle positional location of the vehicle.
1 Assignment
0 Petitions
Accused Products
Abstract
A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. One general aspect includes sends video data and light detecting and ranging (LIDAR) data to a recurrent neural network (rnn) that includes feedback elements to identify a roadway feature. The system also sends the data to a dynamic convolutional neural network (dcnn) to identify the feature. Output values are sent to a softmax decision network to aggregate the rnn and the dcnn output values and determine a vehicle positional location on the roadway.
-
Citations
20 Claims
-
1. A system, comprising a computer having a processor and a memory, the memory storing instructions executable by the processor including instructions to:
-
receive video data from a video data source affixed to a vehicle; receive light detecting and ranging (LIDAR) data from a LIDAR data source affixed to the vehicle; send the video data to a recurrent neural network (RNN) that includes feedback elements to provide feedback from a first layer to a second layer to identify a roadway feature; send the video data to a dynamic convolutional neural network (DCNN) to apply a convolution filter to the video data to generate a convolution image and a pooling filter to the convolution image to develop a feature map to identify the feature; send the LIDAR data to the RNN to identify the feature; send the LIDAR data to the DCNN to identify the feature; send a RNN output value and a DCNN output value to a softmax decision network to aggregate the RNN output value and the DCNN output value; determine a softmax output based on whether the RNN output value and the DCNN output value are within a range; determine a vehicle positional location on the roadway of the vehicle based on the softmax output; compare the vehicle positional location on the roadway relative to an edge of the roadway and an image from a set of training data images to determine an error rate; determine a change in a weight value for the RNN and the DCNN from the error rate; apply the change in the weight value to the RNN and the DCNN; and control at least a steering, a braking and an acceleration of the vehicle from at least the vehicle positional location of the vehicle. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A system, comprising a computer having a processor and a memory, the memory storing instructions executable by the processor including instructions to:
-
receive video data from a camera affixed to a vehicle; receive light detecting and ranging (LIDAR) data from a LIDAR image capture device affixed to the vehicle; send the video data to a recurrent neural network (RNN) that includes feedback elements that enable signals from a first layer to be fed back to a second layer to identify a roadway feature; send the video data to a dynamic convolutional neural network (DCNN) to apply a convolution filter to the video data to generate a convolution image and a pooling filter to the convolution image to develop a feature map to identify the roadway feature; send the LIDAR data to the RNN to identify the feature; send the LIDAR data to the DCNN to identify the feature; send a RNN output value and a DCNN output value to a softmax decision network to determine a softmax output from at least the RNN output value and the DCNN output value; determine a vehicle position on the roadway of the vehicle from the softmax output; and control at least a steering, a braking and an acceleration of the vehicle from at least the vehicle positional location of the vehicle. - View Dependent Claims (11)
-
-
12. A method, comprising:
-
receiving video data from a video data source on a vehicle; receiving light detecting and ranging (LIDAR) data from a LIDAR data source on the vehicle; sending the video data to a recurrent neural network (RNN) that includes feedback elements that enable signals from a first layer to be fed back to a second layer to identify a roadway feature; sending the video data to a dynamic convolutional neural network (DCNN) to apply a convolution filter to the video data to generate a convolution image and a pooling filter to the convolution image to develop a feature map to identify the roadway feature; sending the LIDAR data to the RNN to identify the roadway feature; sending the LIDAR data to the DCNN to identify the roadway feature; sending a RNN output value and a DCNN output value to a softmax decision network to determine a softmax output from at least the RNN output value and the DCNN output value; determining a vehicle positional location on the roadway of the vehicle from the softmax output; comparing the vehicle positional location on the roadway and an image from a set of training data images to determine an error rate; determining a change in a weight value for the RNN and the DCNN from the error rate; applying the change in the weight value to the RNN and the DCNN; and control at least a steering of the vehicle from at least the vehicle positional location of the vehicle. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
-
Specification