Bridging Simulation and Reality: Geometry and Decision Alignment for Autonomous Driving

Bridging the gap between simulation and reality remains a fundamental challenge for end-to-end autonomous driving. Existing approaches primarily focus on appearance-level features. This often leads to suboptimal transfer, where visually aligned models still produce inconsistent or unsafe behaviors in the real world. In this paper, we propose a unified domain adaptation framework that jointly aligns perception and decision processes through geometry-aware and vector-space representations. At the perception level, we introduce a Geometry-Aware Perception Alignment (GAPA) module that enforces cross-domain consistency in both explicit geometry and implicit structural representations extracted from latent features. This reduces domain discrepancy by targeting scene structure rather than appearance. At the decision level, we propose a Latent Vector Space Guidance Decision Alignment (LVDA) module that aligns vector-conditioned policy distributions. By modeling driving decisions as conditioned on structured representations of map topology and multi-agent interactions, we minimize discrepancies between source and target action distributions via adversarial and structural alignment. To further enhance stability, we introduce a progressive adversarial transfer strategy that improves cross-domain feature alignment and training stability. Extensive experiments on a newly partitioned nuScenes dataset demonstrate that Bridge-AD achieves excellent performance, effectively narrowing the gap between simulation and real-world autonomous driving.