Research on Transformer Models for End-to-End Control in Autonomous Driving

This study addresses the challenge of trajectory tracking control in autonomous vehicles. Traditional hierarchical control methods often require manual parameter tuning and struggle to adapt to complex, multi-modal environments. To overcome these limitations, this paper proposes a Transformer-based end-to-end control model for autonomous driving. The model leverages self-attention mechanisms to dynamically fuse multi-modal inputs and capture long-term temporal dependencies. It consists of three main components: input encoding, multi-modal feature fusion, and control signal decoding. This paper evaluates the proposed model using datasets collected from the CARLA simulator and trains it with a hybrid training strategy training strategy. Experimental results show that this paper's approach outperforms the benchmark CNN-LSTM and PilotNet models, achieving performance improvements of 44% in control accuracy (MSE) and 63.5% in driving safety. Additionally, the proposed model demonstrates superior performance in control accuracy, driving safety, real-time response, robustness, and interpretability. Further analysis shows that incorporating multi-frame temporal inputs, an 8-head attention mechanism, and a cross-attention fusion strategy enhances model performance, highlighting its strong potential for real-world applications.