Semi-Local 3D Lane Detection and Uncertainty Estimation

TL;DR

This paper introduces a semi-local BEV-based deep learning approach for 3D lane detection that effectively handles complex topologies and provides reliable uncertainty estimates, achieving state-of-the-art results.

Contribution

It presents a novel semi-local BEV tile representation and a combined parametric and embedding model for 3D lane detection with uncertainty estimation.

Findings

Achieves state-of-the-art camera-based 3D lane detection accuracy.

Demonstrates robust generalization to complex lane geometries and different cameras.

Provides well-calibrated uncertainty estimates that reflect detection noise.

Abstract

We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation. Our method is based on a semi-local, BEV, tile representation that breaks down lanes into simple lane segments. It combines learning a parametric model for the segments along with a deep feature embedding that is then used to cluster segment together into full lanes. This combination allows our method to generalize to complex lane topologies, curvatures and surface geometries. Additionally, our method is the first to output a learning based uncertainty estimation for the lane detection task. The efficacy of our method is demonstrated in extensive experiments achieving state-of-the-art results for camera-based 3D lane detection, while also showing our ability to generalize to complex topologies, curvatures and road geometries as well as to different cameras. We also demonstrate how our uncertainty estimation aligns with the empirical error statistics indicating that it is well calibrated and truly reflects the detection noise.