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.

翻译：在自动驾驶领域，已有许多用于目标检测、语义分割等任务的优秀感知模型，但如何有效利用这些感知模型进行车辆规划仍是一大挑战。传统的自动驾驶车辆轨迹预测方法不仅需要遵循交通规则以避免碰撞，还需沿指定路线到达目的地。本文提出了一种基于Transformer的无需规则的端到端自动驾驶轨迹预测网络——目标点注意力Transformer网络（TAT）。我们利用注意力机制实现了预测轨迹与感知特征以及目标点之间的交互。实验证明，所提出的方法优于现有的条件模仿学习和基于GRU的方法，显著减少了事故发生率并提高了路线完成度。我们在CARLA模拟器中对复杂城市闭环驾驶场景进行了评估，并取得了最先进的性能。