论文标题
感知，预测和计划：通过可解释的语义表示的安全运动计划
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
论文作者
论文摘要
在本文中，我们提出了一个新颖的端到端可学习网络，该网络对自动驾驶车辆进行联合感知，预测和运动计划，并产生可解释的中间表示。与现有的神经运动计划者不同，我们的运动计划成本与我们的看法和预测估计相一致。这是通过一种新型的可区分语义占用表示来实现的，该表示过程明确用作运动计划过程的成本。我们的网络是从人类示范中端到端学习的。大型手动驱动数据集和闭环模拟中的实验表明，所提出的模型在模仿人类行为的同时，在产生更安全的轨迹的同时，明显超过了最先进的计划者。
In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations. Unlike existing neural motion planners, our motion planning costs are consistent with our perception and prediction estimates. This is achieved by a novel differentiable semantic occupancy representation that is explicitly used as cost by the motion planning process. Our network is learned end-to-end from human demonstrations. The experiments in a large-scale manual-driving dataset and closed-loop simulation show that the proposed model significantly outperforms state-of-the-art planners in imitating the human behaviors while producing much safer trajectories.
