论文标题
使用马尔可夫链蒙特卡洛采样和B-Spline改进的快速车道级交点估计
Fast Lane-Level Intersection Estimation using Markov Chain Monte Carlo Sampling and B-Spline Refinement
论文作者
论文摘要
估计当前场景并了解潜在的操作是自动车辆的重要功能。大多数方法在很大程度上依赖地图的正确性，但忽略了过时的信息的可能性。 我们提出了一种能够估算车道的方法，而无需依赖任何地图。该估计仅基于其他交通参与者的轨迹，因此能够合并复杂的环境。特别是，我们能够在交通繁忙和阻塞的情况下估计场景。 该算法首先通过马尔可夫链蒙特卡洛采样估算了粗车道级相交模型，并通过使用非线性最小二乘配方将车道路线与测量结果对齐，并通过对后面进行了完善。我们将车道建模为1D立方B分段，并在实时内实现小于10厘米的错误率。
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 Most approaches rely heavily on the correctness of maps, but neglect the possibility of outdated information.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.
