Core Insights - The article discusses advancements in self-supervised autonomous driving technologies, highlighting two significant frameworks: AD-GS and FiM, which improve scene rendering and trajectory prediction respectively [1][7]. Group 1: AD-GS Framework - The AD-GS framework combines learnable B-spline curves and trigonometric functions for motion modeling and object-aware segmentation, achieving a PSNR of 29.16 on the KITTI dataset, outperforming existing methods like PVG which had a PSNR of 27.13 [1][5]. - Key contributions of AD-GS include a novel motion modeling method, a scene modeling approach that distinguishes between objects and background, and the design of visibility and physical rigidity regularization to enhance performance [5][6]. Group 2: FiM Framework - The FiM framework introduces a trajectory prediction method that utilizes reward-driven intent reasoning and a bidirectional selective state space model, achieving a Brier Score of 0.6218 on the Argoverse 1 dataset, which is the best single model performance [7][12]. - Significant contributions of FiM include redefining trajectory prediction from a planning perspective, developing a reward-driven intent reasoning mechanism, and enhancing prediction accuracy through a hierarchical DETR-like decoder [10][12]. Group 3: IANN-MPPI Framework - The IANN-MPPI framework enhances model predictive path integral methods for autonomous driving, achieving a success rate of 67.5% in dense traffic scenarios, which is a 22.5% improvement over non-interactive baselines [7][20]. - Key innovations include a real-time, fully parallel interactive trajectory planning method and the introduction of spline-based priors to improve lane-changing behavior [17][21].