Application of LLM in Vehicle Dynamics and Control Modeling, Encompassing Human-Vehicle Interaction: A Thorough Reference for Researchers, Engineers,
暫譯: 大型語言模型在車輛動力學與控制建模中的應用:涵蓋人車互動的全面參考書籍,供研究人員與工程師使用

Aykent, Baris

  • 出版商: Springer
  • 出版日期: 2026-08-25
  • 售價: $6,640
  • 貴賓價: 9.5$6,308
  • 語言: 英文
  • 頁數: 216
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 303228399X
  • ISBN-13: 9783032283993
  • 相關分類: Large language model
  • 海外代購書籍(需單獨結帳)

相關主題

商品描述

This book provides a forward-looking guide on how Large Language Models (LLMs) are transforming the field of vehicle dynamics and control. It offers a practical roadmap for engineers and researchers to leverage AI for designing, simulating, and optimizing vehicle systems. This book directly addresses the challenge of moving beyond traditional, time-consuming modeling techniques to embrace a more efficient, data-driven, and interactive approach.

Key Topics and Their Relevance

Foundation in Vehicle Dynamics: The book begins by establishing a strong foundation in vehicle dynamics, including the core principles of longitudinal, lateral, and vertical motion, as well as classic control systems like ABS and ESC. This is crucial for understanding the traditional context before exploring how LLMs can augment these processes.

LLMs in the Automotive Workflow: The readers will learn how to integrate LLMs into every stage of the development cycle, from data preprocessing and analysis to generating simulation code and dynamic scenarios. This is important because it shows how LLMs act as a powerful co-pilot, automating repetitive tasks and accelerating innovation.

Human-Vehicle Interaction (HVI): A dedicated section explores the cutting-edge use of LLMs to interpret driver state and intentions through technologies like eye and head tracking. This is highly relevant as it demonstrates how AI can lead to safer, more personalized, and intuitive driving experiences.

Real-World Implementation with MLOps: The book tackles the practicalities of deploying these advanced models on a vehicle's embedded systems. It covers critical topics such as model compression, edge computing, and MLOps workflows using Docker.

This book is for a target audience of professionals and students in automotive engineering, control systems, and data science who want to understand and implement the latest AI technologies to shape the future of smart vehicles.

商品描述(中文翻譯)

本書提供了一個前瞻性的指南,說明大型語言模型(LLMs)如何改變車輛動力學和控制領域。它為工程師和研究人員提供了一個實用的路線圖,以利用人工智慧設計、模擬和優化車輛系統。本書直接針對如何超越傳統、耗時的建模技術,採用更高效、數據驅動和互動的方法的挑戰。

關鍵主題及其相關性

車輛動力學基礎:本書首先建立了車輛動力學的堅實基礎,包括縱向、橫向和垂直運動的核心原則,以及像ABS和ESC這樣的經典控制系統。這對於理解傳統背景至關重要,然後再探討LLMs如何增強這些過程。

LLMs在汽車工作流程中的應用:讀者將學習如何將LLMs整合到開發週期的每個階段,從數據預處理和分析到生成模擬代碼和動態場景。這一點非常重要,因為它顯示了LLMs如何作為強大的副駕駛,自動化重複性任務並加速創新。

人車互動(HVI):專門的部分探討了LLMs在通過眼睛和頭部追蹤等技術解釋駕駛者狀態和意圖方面的前沿應用。這是非常相關的,因為它展示了人工智慧如何導致更安全、更個性化和直觀的駕駛體驗。

與MLOps的實際應用:本書探討了在車輛嵌入式系統上部署這些先進模型的實際問題。它涵蓋了關鍵主題,如模型壓縮、邊緣計算和使用Docker的MLOps工作流程。

本書的目標讀者是汽車工程、控制系統和數據科學的專業人士和學生,他們希望理解和實施最新的人工智慧技術,以塑造智能車輛的未來。

作者簡介

Dr. Barış Aykent is a researcher specializing in vehicle dynamics, control systems, artificial intelligence, and human-machine interaction. He has authored books on machine learning applications in mechanical vibrations and AI-driven vehicle simulation software, alongside numerous academic articles on automotive safety and intelligent control. His work integrates advanced control methods--such as reinforcement learning, model predictive control, and robust adaptive control--into applications for electric vehicles, UAVs, and driving simulators. Currently, he leads innovative projects bridging engineering, AI, and driver behavior analysis, contributing both to academic research and real-world automotive technologies.

作者簡介(中文翻譯)

巴里斯·艾肯特博士是一位專注於車輛動力學、控制系統、人工智慧及人機互動的研究者。他著有關於機器學習在機械振動應用及人工智慧驅動的車輛模擬軟體的書籍,並發表了多篇有關汽車安全和智能控制的學術文章。他的研究將先進的控制方法整合進電動車、無人機(UAV)和駕駛模擬器的應用中,包括強化學習、模型預測控制和穩健自適應控制等技術。目前,他領導著創新的專案,將工程、人工智慧和駕駛行為分析結合起來,為學術研究和現實世界的汽車技術做出貢獻。