FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network

Understand

The search for predictive models that generalize to the long tail of sensor inputs is the central difficulty when developing data-driven models for autonomous vehicles.

• In this paper, we use lane detection to study modeling and training techniques that yield better performance on real world test drives.
• On the modeling side, we introduce a novel fully convolutional model of lane detection that learns to decode lane structures instead of delegating structure inference to post-processing.
• In contrast to previous works, our convolutional decoder is able to represent an arbitrary number of lanes per image, preserves the polyline representation of lanes without reducing lanes to polynomials, and draws lanes iteratively without requiring the computational and temporal complexity of recurrent neural networks.