Core Viewpoint - The article presents a framework for generating safe trajectories based on discrete diffusion without the need for gradient calculations, aiming to enhance the efficiency and safety of autonomous driving systems [1][2]. Group 1: Framework Overview - The core value lies in introducing discrete ideas into trajectory generation for assisted driving, moving beyond traditional reinforcement learning and continuous diffusion models [2]. - The framework consists of two main phases: goal-oriented trajectory generation and safety-guided regeneration, both of which do not require gradient calculations [5][10]. Group 2: Phase One - Goal-Conditioned Generation - The objective is to generate a diverse set of complete trajectory plans reflecting different high-level driving intentions, crucial for scenarios requiring extensive decision-making [3]. - The workflow includes generating candidate target points, ensuring diversity through non-maximum suppression (NMS), generating complete trajectories for each candidate, and selecting the best trajectory based on a global scoring system [4]. Group 3: Phase Two - Safety-Guided Regeneration - This phase focuses on iteratively correcting trajectories identified as potentially unsafe through a dialogue between the generated model and an external safety oracle [5][10]. - The process involves trajectory evaluation, safety anchor search, and trajectory inpainting to ensure the final trajectory is safe and coherent [7][8][9]. Group 4: Challenges and Innovations - The article highlights challenges in relying solely on reinforcement learning due to issues like reward hacking and the difficulty of ensuring safety in imitation learning [11]. - It emphasizes the need for a unified architecture that efficiently integrates multimodal inputs while maintaining safety and performance, leading to the development of the ReflectDrive framework [13].