论文标题
半本地3D车道检测和不确定性估计
Semi-Local 3D Lane Detection and Uncertainty Estimation
论文作者
论文摘要
我们提出了一种基于相机的新型DNN方法，用于3D车道检测，并进行不确定性估计。我们的方法基于半本地的BEV，瓷砖表示，将车道分解为简单的车道段。它结合了学习段的参数模型以及深层嵌入，然后将其用于将细分聚集到完整的车道中。这种组合使我们的方法可以推广到复杂的车道拓扑，曲率和表面几何形状。此外，我们的方法是第一个为车道检测任务输出基于学习的不确定性估计的方法。在广泛的实验中证明了我们方法的功效，从而实现了基于摄像机的3D车道检测的最新结果，同时还显示了我们概括到复杂拓扑，曲率和道路几何形状以及不同相机的能力。我们还证明了我们的不确定性估计如何与经验误差统计数据保持一致，表明它经过了良好的校准并真正反映了检测噪声。
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.
