Target-point Attention Transformer: A novel trajectory predict network for end-to-end autonomous driving

The network of end-to-end automatic driving algorithms can be divided into perception network part and planning network part. Most of the research on the end-to-end automatic driving algorithm focuses on the part of the perception network, while the improvement of the planning network is less. However, the existing planning network can not effectively use the perceptual features, which may lead to traffic accidents. 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). Leveraging the attention mechanism, our proposed model facilitates interaction between the predicted trajectory and perception features, along with target-points. Comparative evaluations with existing conditional imitation learning and GRU-based methods show the superior performance of our approach, particularly in reducing accident occurrences and improving route completion. Extensive assessments conducted in complex closed-loop driving scenarios within urban settings, utilizing the CARLA simulator, affirm the state-of-the-art proficiency of our proposed method.