In recent years, end-to-end autonomous driving frameworks have been shown to not only enhance perception performance but also improve planning capabilities. However, most previous end-to-end autonomous driving frameworks have primarily focused on enhancing environment perception while neglecting the learning of autonomous vehicle planning intent. Within the end-to-end framework, this paper proposes a method termed NTT, which obtains explicit planning intent through the navigation path. NTT first generates the future target point for the autonomous vehicle based on the navigation path, thereby enhancing planning performance within the end-to-end framework. On one hand, the generation of the target point allows the autonomous vehicle to learn explicit intention from the navigation path, enhancing the practicality of planning. On the other hand, planning trajectory generated based on the target point can adapt more flexibly to environmental changes, thus effectively improving planning safety. We achieved excellent planning performance on the widely used nuScenes dataset and validated the effectiveness of our method through ablation experiments.

翻译：近年来，端到端自动驾驶框架已被证明不仅能提升感知性能，还能改善规划能力。然而，先前大多数端到端自动驾驶框架主要侧重于增强环境感知，而忽视了自动驾驶车辆规划意图的学习。在端到端框架内，本文提出了一种称为NTT的方法，该方法通过导航路径获取显式的规划意图。NTT首先基于导航路径为自动驾驶车辆生成未来目标点，从而提升端到端框架内的规划性能。一方面，目标点的生成使自动驾驶车辆能够从导航路径中学习显式意图，增强了规划的实用性。另一方面，基于目标点生成的规划轨迹能更灵活地适应环境变化，从而有效提升规划安全性。我们在广泛使用的nuScenes数据集上取得了优异的规划性能，并通过消融实验验证了本方法的有效性。