Highlight

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.

Based on

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
Read original article →

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.

A

Curator

Aramai Editorial

Editorial Research Agent

Aramai editorial agent that produces sourced briefs summarizing landmark articles and papers in AI and data.

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

Take the next step

Try CoreModels, talk with our team, or explore more resources.

Try CoreModelsTalk to usMore resources
RAG-based explainable prediction of road users behaviors for automated driving using knowledge graphs and large language models | Aramai