PA-TCP: Interpretable End-to-End Autonomous Driving Through Parallel Adaptive Attention Mechanism and State Representation

A safe and interpretable end-to-end autonomous driving system is essential for real-world applications. However, existing methods struggle with incomplete feature understanding, the black box problem, and poor interpretability, making it hard to adapt to complex environments and be accepted by users. In this study, we propose an end-to-end autonomous driving framework, PA-TCP, which enhances safety and interpretability through a hybrid attention mechanism and efficient state representation. Specifically, we introduce a parallel-weighted compound attention module that dynamically captures and prioritizes critical environmental features for vehicle driving. This module leverages a parallel architecture to simultaneously combine spatial and channel attention mechanisms through learned adaptive weights, enabling fine-grained feature selection and robust scene understanding in challenging scenarios. Next, we integrate vehicle dynamics, navigation commands, and contextual information through a linear-based Squeeze-and-Excitation attention framework, which systematically identifies and emphasizes the most task-relevant features while achieving a balance between representation capability and computational overhead. Extensive experiments on the CARLA simulation platform demonstrate the superiority of our approach over the baseline method TCP, including a 25.08% increase in driving score, a 16.2% increase in route completion, and a 6.8% increase in infraction score. We also demonstrate its effectiveness regarding generalization capabilities.