Ford Using Deep Learning for Lane Detection

Researchers from the recently expanded Ford Research and Innovation Center in Palo Alto, California developed a new sub-centimeter accurate approach to estimate a moving vehicle’s position within a lane in real-time. To achieve this level of precision the researchers trained a deep neural network, aptly named DeepLanes, to process input images from two laterally-mounted down-facing cameras – each recording at an average 100 frames/s.

The team trained their neural network on an NVIDIA DIGITS DevBox with the cuDNN-accelerated Caffe deep learning framework.

“Our unified framework approach is a simple, end-to-end solution that does not depend on tedious pre-processing, post-processing or hand-crafted features,” says the team of researchers. But it was only after a thorough evaluation of the results that they could proudly claim, “we are able to estimate the lane position in 99% of the cases with less than five pixel error”.

Ford Research Lane Detection with Deep Learning
This figure shows lane marker positions estimated by the DeepLanes network. The network is able to detect lane markers that are in the shadow as well as parallel, broken and scattered markers.

In the coming years the team expects their speedy and scalable DeepLanes technique can be applied to a variety of other automotive functions as well – anything from improved real-time navigation systems to fully automated driving features.

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