Core Viewpoint - The article discusses the top 10 recommended papers in the field of end-to-end autonomous driving, highlighting the increasing presence of Li Auto in the discourse surrounding autonomous driving technology and research [2][20][22]. Group 1: Overview of Recommended Papers - The article presents a list of 10 highly recommended papers in the end-to-end autonomous driving domain, compiled from interviews with leading researchers [22][26]. - The papers cover various innovative approaches, including reinforcement learning, vision-language models, and multimodal frameworks [27][29][35][40]. Group 2: Key Innovations and Technologies - The paper "TransDiffuser" introduces an encoder-decoder model for trajectory generation, utilizing multimodal perception information to create diverse and high-quality trajectories [10][42]. - The diffusion model is highlighted for its ability to generate trajectories by learning from noise, significantly improving the model's performance in complex traffic environments [6][7][13][16]. - The architecture of TransDiffuser includes a scene encoder for processing multimodal data and a denoising decoder for trajectory generation [11][12][14]. Group 3: Performance Metrics and Results - TransDiffuser achieved a Predictive Driver Model Score (PDMS) of 94.85 on the NAVSIM benchmark, outperforming existing methods [15][42]. - The model's efficiency is enhanced through the use of ordinary differential equations (ODE) sampling, allowing for rapid trajectory generation [7][13]. Group 4: Future Directions and Challenges - The authors of the papers acknowledge challenges in fine-tuning models and suggest future work could involve integrating reinforcement learning and exploring models like OpenVLA [17][18]. - The article emphasizes the ongoing evolution in the field, with a shift towards more integrated and robust approaches to autonomous driving [70].