EATNet: Efficient Axial Transformer Network for End-to-end Autonomous Driving

Engineering notes

Extensive experiments demonstrate that EATNet, with only a quarter of the parameters of comparable multi-modal models, achieves competitive or even superior performance on the closed-loop CARLA simulator compared to other baselines.

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

In recent years, end-to-end autonomous driving has garnered significant attention from researchers and has witnessed rapid advancements. However, existing methods en-counter challenges such as high computational demands, slow training and inference speeds, which hinder their real-world deployment. To tackle this issue, we introduce the Efficient Axial Transformer Network (EATNet), a lightweight multi-modal autonomous driving framework based on cross-axial Transformers. By effectively integrating lidar and multi-view RGB features, this model utilizes an enhanced lightweight cross-axial Transformer to minimize model size and computational requirements. Extensive experiments demonstrate that EATNet, with only a quarter of the parameters of comparable multi-modal models, achieves competitive or even superior performance on the closed-loop CARLA simulator compared to other baselines.

Links and sources

• Official / arXiv page

Comments