Chen, Weihuang (Xi'an Jiaotong University), Kong, Fanjie (Xi'an Jiaotong University), Chen, Liming (Xi'an Jiaotong University), Wang, Shen'ao (Xi'an Jiaotong University), wang, zhiping (Xi'an Jiaotong University), Sun, Hongbin (Xi’an Jiaotong University)

EATNet: Efficient Axial Transformer Network for End-To-End Autonomous Driving

Scheduled for presentation during the Invited Session "Learning-empowered Intelligent Transportation Systems: Foundation Vehicles and Coordination Technique II" (WeBT1), Wednesday, September 25, 2024, 15:10−15:30, Salon 1

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

In recent years, end-to-end autonomous driving has garnered significant attention from researchers and has witnessed rapid advancements. However, existing methods encounter 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.