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Mejorar la percepción inteligente y la optimización de decisiones de escenarios de cruce de peatones en entornos de

Xiao Teng^1,2, Lin Huang³, Zhenjiang Shen^4,5

¹Faculty of Transdisciplinary Sciences, Institute of Philosophy in Interdisciplinary Sciences, Kanazawa University, Kanazawa, 920-1192, Japan.

Scientific reports

|August 25, 2025

Resumen

Este estudio utiliza modelos de lenguaje visual (VLM) para crear un sistema inteligente de cruce de peatones para la conducción autónoma. El sistema mejora la seguridad mediante el procesamiento de datos multimodales para una percepción precisa y la predicción de riesgos.

Área de la Ciencia:

Inteligencia artificial
Visión por computadora
Sistemas de transporte inteligentes

Sus antecedentes:

Los sistemas de conducción autónoma requieren una percepción y toma de decisiones sólidas para los escenarios de cruce de peatones.
Los métodos tradicionales luchan con la complejidad de entornos dinámicos y diversos comportamientos peatonales.
La integración de modelos avanzados de IA es crucial para mejorar la seguridad y la fiabilidad en el transporte inteligente.

Objetivo del estudio:

Desarrollar un sistema inteligente de escenarios de cruce de peatones utilizando modelos de lenguaje visual (VLM) para la conducción autónoma.
Mejorar la precisión de la percepción, la predicción de riesgos y la toma de decisiones en entornos de tráfico complejos.
Establecer un marco estandarizado basado en datos para la interacción de vehículos autónomos con peatones.

Principales métodos:

Clasificación de los escenarios de paso de peatones basados en las normas internacionales de conducción autónoma.
Desarrollo de instrucciones estandarizadas para que los VLM generen listas de verificación de escenarios estructurados en formato JSON.
Procesamiento multimodal de datos y modelado del comportamiento para la predicción del riesgo de colisión y recomendaciones de seguridad.

Principales resultados:

Logró una precisión de percepción del 93,05%.
Se han demostrado altos índices de coherencia para la predicción de riesgos (85,91%) y las reglas de toma de decisiones (87,72%).
Integra con éxito la percepción visual con el razonamiento para una toma de decisiones estructurada y explicable.

Conclusiones:

El sistema basado en VLM ofrece un marco técnico novedoso para mejorar la percepción, la predicción y la toma de decisiones en la conducción autónoma.
Este enfoque supera las limitaciones de los métodos tradicionales en el manejo de comportamientos peatonales complejos y entornos dinámicos.
El sistema proporciona una base para una navegación autónoma más segura y confiable en áreas de gran densidad peatonal.

Palabras clave:

Toma de decisiones sobre las acciones Pronóstico del riesgo de colisión Palabras urgentes Clasificación de los escenarios Listas de control estandarizadas Modelos de lenguaje visual

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