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

Engineering notes

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.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

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.

Links and sources

• Official / arXiv page
• PDF from original source

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