Low-Cost Monocular End-to-End Autonomous Driving With Enhanced Interpretability

In recent years, end-to-end autonomous driving has achieved remarkable progress, yet challenges remain in achieving efficient deployment. Existing methods often rely on multi-modal sensor fusion, which incurs high costs and computational demands, limiting their large-scale adoption in low-cost scenarios. Reducing system cost while maintaining performance has consistently been a key focus of the industry. Moreover, the opaque nature of end-to-end models, due to the lack of intermediate representations, limits interpretability and hampers root-cause analysis when misjudgments occur in complex environments, thereby posing safety risks. To address these challenges, this paper proposes a lightweight and interpretable end-to-end autonomous driving architecture that utilizes only monocular camera images as input. The aim is to enhance decision-making efficiency and trajectory planning accuracy while ensuring system safety. The proposed architecture comprises two core components. First, in the scene perception part, a depth information supervision module is introduced to enable hierarchical geometric perception, and an online vector map construction module is designed to build structured environmental representations at low cost. This replaces high-definition maps with explicit constraint expressions, thereby improving model interpretability and decision-making reliability. Second, in the planning part, a dual-layer prediction framework is developed to decouple speed and waypoint prediction. It independently forecasts vehicle velocity and waypoints, while an optimized decoder structure produces fine-grained spatiotemporal trajectories, enhancing planning capabilities in complex traffic scenarios. Experimental results on multiple benchmarks from the CARLA Leaderboard 1.0 demonstrate that the proposed method significantly improves system efficiency while maintaining advanced decision-making performance. It outperforms existing multi-sensor fusion approaches, showcasing strong potential for practical deployment.