Fast Lane-Level Intersection Estimation using Markov Chain Monte Carlo Sampling and B-Spline Refinement

TL;DR

This paper introduces a real-time, map-independent lane estimation method for automated vehicles that uses traffic trajectories and B-spline refinement, effective even in heavy traffic and occlusions.

Contribution

The approach estimates lane intersections without map prior by combining MCMC sampling with B-spline refinement, handling complex environments and occlusions.

Findings

Achieves less than 10cm error rate in lane estimation.

Operates in real-time with no reliance on map data.

Effective in heavy traffic and occluded scenarios.

Abstract

Estimating the current scene and understanding the potential maneuvers are essential capabilities of automated vehicles. Most approaches rely heavily on the correctness of maps, but neglect the possibility of outdated information. We present an approach that is able to estimate lanes without relying on any map prior. The estimation is based solely on the trajectories of other traffic participants and is thereby able to incorporate complex environments. In particular, we are able to estimate the scene in the presence of heavy traffic and occlusions. The algorithm first estimates a coarse lane-level intersection model by Markov chain Monte Carlo sampling and refines it later by aligning the lane course with the measurements using a non-linear least squares formulation. We model the lanes as 1D cubic B-splines and can achieve error rates of less than 10cm within real-time.