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)
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:
Network architecture
Data sources:
Constraints:
Benchmarks: