Geometrically Aligned Diffusion for End-to-End Autonomous Driving

While generative diffusion models have emerged as a powerful paradigm for multimodal trajectory generation, their deployment in safety-critical autonomous driving is fundamentally bottlenecked by a critical misalignment: unconstrained statistical generation frequently violates physical, geometric, and kinematic constraints. To address this, we propose STEER, a unified framework that systematically embeds physical inductive biases into the generative lifecycle through a two-fold alignment strategy. At the macro-geometric level, STEER grounds the generative manifold in physical space by introducing Scene-Conditioned Anchor generation (SCA) for topology-compliant prior initialization, while leveraging Graph Attention Agent Interaction (GAI) to enforce spatial locality and prevent attention entropy collapse during iterative denoising. At the micro-kinematic level, STEER guarantees high-order trajectory feasibility and deterministic execution by coupling Adaptive Dual-Path Refinement (ADPT) with a Hierarchical Trajectory Selection (HTS) mechanism. Specifically, ADPT fuses global intent with local autoregressive modeling for kinematic smoothness, while HTS decodes the generative density through joint optimization over predictive utility, topological boundaries, and motion feasibility. Extensive experiments on the NAVSIM benchmark demonstrate that STEER establishes a new state-of-the-art, successfully bridging statistical generative modeling with physically grounded embodied planning.