基于马尔可夫链蒙特卡罗抽样和B样条细化的车道级交叉口快速估计
Fast Lane-Level Intersection Estimation using Markov Chain Monte Carlo Sampling and B-Spline Refinement
摘要：
估计当前场景和理解潜在的机动是自动驾驶车辆的基本能力。大多数方法严重依赖于地图的正确性，但忽略了过时信息的可能性。我们提出了一种方法，能够估计车道，而不依赖于任何地图之前。该估计仅基于其他交通参与者的轨迹，从而能够结合复杂的环境。特别是，我们能够在交通繁忙和闭塞的情况下估计现场。该算法首先估计一个粗略的车道级交叉口模型的马尔可夫链蒙特卡罗采样和细化后，通过调整车道课程的测量，使用非线性最小二乘公式。我们将车道建模为1D三次B样条，并且可以在实时内实现小于10 cm的出错率。
英文摘要：
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 Most approaches rely heavily on the correctness of maps, but neglect the possibility of outdated information.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.
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