Vehicle lane placement

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.

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.