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Description
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.
Introduction to AI in Automotive Systems.- Machine Learning Foundations.- Vehicle Dynamics Fundamentals.- AI-Enhanced Vehicle Safety Systems.- Deep Learning for Vehicle State Estimation.
Dr. Baris 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.
