Anytime Lane-Level Intersection Estimation Based on Trajectories of Other Traffic Participants
This addresses the challenge of outdated maps for automated vehicles, providing a real-time, map-free solution for intersection understanding.
The paper tackles the problem of estimating lane-level intersection topology without map priors by observing trajectories of other traffic participants, achieving 99.9% accuracy on topology estimation in 36ms and an average error of 15cm for lane course estimation in 140ms on simulated data, with similar precision on real-world intersections.
Estimating and understanding the current scene is an inevitable capability of automated vehicles. Usually, maps are used as prior for interpreting sensor measurements in order to drive safely and comfortably. Only few approaches take into account that maps might be outdated and lead to wrong assumptions on the environment. This work estimates a lane-level intersection topology without any map prior by observing the trajectories of other traffic participants. We are able to deliver both a coarse lane-level topology as well as the lane course inside and outside of the intersection using Markov chain Monte Carlo sampling. The model is neither limited to a number of lanes or arms nor to the topology of the intersection. We present our results on an evaluation set of 1000 simulated intersections and achieve 99.9% accuracy on the topology estimation that takes only 36ms, when utilizing tracked object detections. The precise lane course on these intersections is estimated with an error of 15cm on average after 140ms. Our approach shows a similar level of precision on 14 real-world intersections with 18cm average deviation on simple intersections and 27cm for more complex scenarios. Here the estimation takes only 113ms in total.