Behavioral Modelling in Automotive using Generative AI
Behavioral Modelling in Automotive using Generative AI

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Behavioral Modelling in Automotive using Generative AI

Introduction

Generative Artificial Intelligence (GenAI) is a rapidly evolving field of Artificial Intelligence (AI) focused on creating models that can generate new content based on patterns, structures, and features learned from a large number of datasets. The relevance of GenAI is substantial, as it offers numerous benefits and applications across various domains like research and development, content creation, conversational AI and many more.

The automotive industry is embarking on a new journey with GenAI, utilizing it as a powerful tool to overcome complex challenges and enhance customer satisfaction. GenAI can help design safer and more efficient vehicles, improve traffic safety, and reduce environmental impact. As AI continues to evolve, it will further revolutionize the automotive industry by enabling autonomous driving and creating new opportunities for customized driving experiences.

This blog explains the work done by TATA ELXSI in using GenAI for one of the automotive applications, which is: ‘human behavior modeling’. We modeled the behavior of humans, and how humans interact with vehicle electronic systems using GenAI. This enables one to capture more realistic test scenarios, which are independent of the hardware or software in vehicles. Pretraining is not required for such Large Language Models (LLMs) [1], as human behavioral aspects are already captured in essence, in LLMs.

Methodology

The architecture of the proposed GenAI framework for human behavioral modeling and test case generation is shown in Figure 1.

Figure 1. Proposed GenAI framework for behavior modeling

Figure 1. Proposed GenAI framework for behavior modeling

The initial step is to provide fine-tuned prompts as input to the LLMs. The prompts are aimed at asking interactive questions with the LLM to generate behavioral test cases for automotive testing. LangChain [2] is used to interact with the LLM. The generated test cases from the LLM model are then stored in a database. A Streamlit [3] based User Interface (UI) has been created which can interact with the previously created database as it enables the user to visualize the scenarios in an intuitive and interactive fashion. Subsequent section describes the detailed explanation of the modules described in Figure 1 in detail.

Attention

Attention

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