会自检的VLA！ReflectDrive：更安全更高效scaling的端到端框架（理想&清华）

Core Viewpoint - ReflectDrive is a novel learning framework that integrates a reflective mechanism to achieve safe trajectory generation through discrete diffusion, addressing the challenges in end-to-end autonomous driving systems [4][46]. Group 1: Introduction and Background - Autonomous driving is leading the transportation industry towards a safer and more efficient future, with end-to-end (E2E) systems becoming a mainstream alternative to traditional modular designs [4]. - Visual-Language-Action (VLA) models combine pre-trained knowledge from visual-language models (VLM) to enhance adaptability in complex scenarios [4][5]. - Current learning-based methods have not resolved core challenges in imitation learning driving systems, particularly in encoding physical rules like collision avoidance [4][5]. Group 2: ReflectDrive Framework - ReflectDrive proposes a new learning framework that utilizes a discrete diffusion reflective mechanism for safe trajectory generation [3][12]. - The framework begins by discretizing the two-dimensional driving space to construct an action codebook, allowing fine-tuning of pre-trained diffusion language models for planning tasks [3][14]. - The reflective mechanism operates without gradient calculations, enabling iterative self-correction inspired by spatiotemporal joint planning [3][8]. Group 3: Methodology and Mechanism - The reflective inference process consists of two stages: target condition trajectory generation and safety-guided regeneration [20][25]. - The framework integrates safety metrics to evaluate generated multimodal trajectories, identifying unsafe path points through local search methods [8][25]. - The iterative optimization loop continues until the trajectory is deemed safe or computational limits are reached, ensuring high efficiency in real-time performance [31][32]. Group 4: Experimental Results - ReflectDrive was evaluated on the NAVSIM benchmark, demonstrating significant improvements in safety metrics such as collision rates and compliance with drivable areas [32][38]. - The introduction of the safety-guided regeneration mechanism led to substantial enhancements in safety indicators, with notable increases in DAC (3.9%), TTC (1.3%), NC (0.8%), and EP (7.9%) compared to the baseline [37][38]. - When using ground-truth agent information, ReflectDrive's performance approached human driving levels, achieving NC of 99.7% and DAC of 99.5% [38][39]. Group 5: Conclusion - ReflectDrive effectively integrates a reflective mechanism with discrete diffusion for safe trajectory generation, validated by its performance on the NAVSIM benchmark [46].