  In the field of autonomous driving, there have been many excellent perception
models for object detection, semantic segmentation, and other tasks, but how
can we effectively use the perception models for vehicle planning? Traditional
autonomous vehicle trajectory prediction methods not only need to obey traffic
rules to avoid collisions, but also need to follow the prescribed route to
reach the destination. In this paper, we propose a Transformer-based trajectory
prediction network for end-to-end autonomous driving without rules called
Target-point Attention Transformer network (TAT). We use the attention
mechanism to realize the interaction between the predicted trajectory and the
perception features as well as target-points. We demonstrate that our proposed
method outperforms existing conditional imitation learning and GRU-based
methods, significantly reducing the occurrence of accidents and improving route
completion. We evaluate our approach in complex closed loop driving scenarios
in cities using the CARLA simulator and achieve state-of-the-art performance.
