2025年的理想还在不断突破，年度成果一览......

Core Insights - Li Auto has successfully entered the domestic smart driving tier one since the mass production of its end-to-end + VLM dual system last year, maintaining a leading position in both academic work and mass production solutions [3][4] - The company is transitioning from a new energy vehicle brand to an AI enterprise, driven by advancements in embodied intelligence and large models [3] - The VLA driver model, featuring innovative architecture, enhances capabilities in spatial understanding, reasoning, communication, memory, and behavior [3][4] VLA & VLM - ReflectDrive introduces discrete diffusion for reflective vision-language-action models in autonomous driving, aiming for scalable and efficient trajectory generation [8][13] - OmniReason establishes a temporal-guided framework for VLA, emphasizing causal reasoning in diverse driving scenarios [11][16] - LightVLA presents a differentiable token pruning framework to enhance efficiency in VLA models, achieving significant reductions in computational load while improving success rates [14][17] - DriveAgent-R1 focuses on human-like driving decisions, introducing a hybrid thinking architecture that adapts to complex environments [19] End-to-End Trajectory Generation - World4Drive is an open-source VLA dataset covering diverse driving scenarios across 148 cities in China, ensuring high-quality and representative data [21][25] - TransDiffuser enhances trajectory generation through a novel end-to-end framework that integrates multimodal driving intentions without relying on perception annotations [23][26] World Models - RLGF proposes a reinforcement learning framework for generating driving videos, addressing geometric distortion issues in autonomous driving [29][34] - GeoDrive innovatively incorporates 3D point cloud rendering into the generation paradigm, improving spatial consistency and controllability [40] Other Innovations - TokenFLEX introduces a unified training framework for dynamic visual token inference, enhancing model robustness across varying token counts [50] - RuscaRL addresses exploration bottlenecks in reinforcement learning, promoting independent learning through structured external support [56]