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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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DSC-LLM:驾驶场景背景基于表示的轨迹预测框架与风险因素推理使用LLMs

Sunghun Kim1, Joobin Jin2, Seokjun Hong2

  • 1Department of AI and Big Data, Soonchunhyang University, 22 Soonchunhyang-ro, Asan 31538, Republic of Korea.

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概括
此摘要是机器生成的。

本研究引入了一个多式联运框架,用于在城市地区准确预测自动驾驶轨迹. 它使用驾驶场景上下文 (DSC) 和多式联运大型语言模型 (MLLM) 进行可解释的,风险意识的运动解释.

关键词:
自主驾驶系统 自主驾驶系统驾驶场景 背景 背景多式多样的LLM法学士课程意识到风险的推理.轨迹的预测和预测.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 在复杂的城市环境中自动驾驶需要精确的轨迹预测.
  • 可解释的上下文证据对于理解和信任预测的车辆运动至关重要.

研究的目的:

  • 为驾驶场景环境 (DSC) 意识的轨迹预测开发一个多式联运框架.
  • 通过使用上下文线索,为预测的运动提供风险意识的解释.

主要方法:

  • 时间对象状态 (轨迹,速度,曲率,运动状态) 与语义相机图像的整合.
  • 一个由四个模块组成的框架:行为特征提取,场景上下文提取,DSC增强预测和通过多式大型语言模型 (MLLM) 进行风险意识推理.

主要成果:

  • 该框架在Rank2Tell数据集上取得了竞争性表现,其ADE为10.972,FDE为13.701,RMSE为8.782.
  • 定性评估表明,DeepSeek-R1-Distill-Qwen-7B提供了最连贯和最符合背景的解释.

结论:

  • 提出的方法证明了在复杂的城市环境中实现自动驾驶的可行性和适用性.
  • 将DSC意识的预测与可解释的推理相结合,为城市自主导航提供了一个实用和透明的解决方案.