By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights.
Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles.
Ranked #6 on Lane Detection on LLAMAS
The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available.
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
Ranked #9 on Lane Detection on LLAMAS