  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.
