Lane Detection using Deep Learning

[xXBasti]

Objective: Detect the Road Lanes

State-of-the-art Convolutional Neural Networks can be used to detect road lanes.

Different approaches and techniques can be implemented to train a NN that can detect lines. Several aspects need to be tried and decided on:

Representation:

• Output of coefficient corresponding to lane boundaries
• Output of drivable area in image
• Segmentation of lane boundaries (can be combined with scene segmentation for other tasks)

Network architecture

• Convolutional CNN outputting a set of coefficents using normal FCN layers at the head (easiest)
• Convolutional CNN that upsample a feature map to generate the drivable road area (medium)
• Segmentation network (bottleneck at feature map, upsampling afterwards again) (hardest)

Data sources:

• Generation of coefficients by our current line detection algorithm (can use existing rosbags for generation)
• Use real-world datasets like Berkeley Deep Drive which contain drivable area and lane information (however, these might not directly be transferable to our domain)
• Use our environment generator (originally for Gazebo). A possibility is to render the track in 3D software like Blender to create a more realistic image. This requires investigation into the influence of the Domain Adaptation problem in this domain.

Constraints:

• The network has to run at least at 25FPS on our on-board hardware (A Jetson TX2)
• This also includes communication with the ROS core running on the main Intel NUC board

Benchmarks:

• Comparison with our current Line Detection approach
• Driving in simulation (requires investigation into domain adaptation)