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  • Latent Chain-of-Thought World Modeling for End-to-End Driving
    In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered Our approach unifies CoT reasoning and decision making by representing both in an action-aligned latent space
  • Latent Chain-of-Thought World Modeling for End-to-End Driving
    In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered Our approach unifies CoT reasoning and decision making by representing both in an action-aligned latent space
  • Latent Chain-of-Thought World Modeling for End-to-End Driving
    In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered Our approach unifies CoT reasoning and decision making by representing both in an action-aligned latent space
  • FutureX：通过潜思维链世界模型增强端到端自动驾驶能力 - 知乎
    思维链 （CoT）通过预测一系列未来思考过程，并以此指导轨迹优化，提供一种有前景的解决方案。 本文提出 FutureX，这是一个由 CoT 驱动的流程，它通过未来场景潜推理和轨迹优化来增强端到端规划器，使其能够执行复杂的运动规划。 具体而言， 自动思考开关 会检查当前场景，并决定是否需要额外的推理来生成更高质量的运动规划。 一旦 FutureX 进入思考模式， 潜世界模型 就会执行 CoT 指导的推演，预测未来场景表示，使摘要网络能够进一步优化运动规划。 否则，FutureX 将以即时模式运行，对相对简单的场景进行前向传播，生成运动规划。
  • Latent Chain-of-Thought World Modeling for End-to-End Driving
    In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered Our approach unifies CoT reasoning and decision making by representing both in an action-aligned latent space
  • Latent Chain-of-Thought World Modeling for End-to-End Driving
    In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered
  • Latent Chain-of-Thought World Modeling for End-to-End Driving
    NVIDIA and Stanford University researchers, in collaboration with UT Austin, introduce LCDrive, a framework for end-to-end autonomous driving that utilizes latent Chain-of-Thought (CoT) world modeling instead of natural language
  • Latent Chain-of-Thought World Modeling for End-to-End Driving
    In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent language that captures possible outcomes of the driving actions being considered Our approach unifies CoT reasoning and decision making by representing both in an action-aligned latent space
  • FutureX: Enhance End-to-End Autonomous Driving via Latent Chain-of . . .
    In this survey, we provide a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving
  • Enhancing End-to-End Autonomous Driving with Latent World Model
    This paper proposed the LAtent World model (LAW), a method of self-supervised learning designed for end-to-end autonomous driving The primary breakthrough lies in employing a latent world model to forecast upcoming scene characteristics, utilizing existing features and ego trajectories





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