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RAG-based explainable prediction of road users behaviors for automated driving using knowledge graphs and large language models

A research paper proposing an explainable road users' behavior prediction system using Knowledge Graphs and Large Language Models.

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RAG-based explainable prediction of road users behaviors for automated driving using knowledge graphs and large language models

By Mohamed Manzour Hussien, Angie Nataly Melo, Augusto Luis Ballardini, Carlota Salinas, Rubén Izquierdo, Miguel Ángel SoteloExpert Systems with Applications
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The authors propose a system that integrates Knowledge Graphs and Large Language Models to predict road user behaviors. They use Retrieval Augmented Generation techniques and combine Knowledge Graph Embeddings with Bayesian inference for inductive reasoning.

The system is applied to two use cases: pedestrian crossing actions and lane change maneuvers, achieving state-of-the-art performance.

Abstract

The authors propose a system that integrates Knowledge Graphs and Large Language Models to predict road user behaviors. They use Retrieval Augmented Generation techniques and combine Knowledge Graph Embeddings with Bayesian inference for inductive reasoning. The system is applied to two use cases: pedestrian crossing actions and lane change maneuvers, achieving state-of-the-art performance.

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explainable airoad user behavior predictionknowledge graphs and large language modelsinductive reasoningautonomous drivingKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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