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Aprendizaje por refuerzo denso para la validación de la seguridad de vehículos autónomos
Shuo Feng1,2,3, Haowei Sun1, Xintao Yan1
1Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA.
Nature
|March 23, 2023
Resumen
La validación de la seguridad de los vehículos autónomos se acelera mediante la inteligencia artificial. El aprendizaje de refuerzo profundo denso (D2RL) capacita a los agentes para crear escenarios desafiantes, reduciendo drásticamente el tiempo de prueba.
Área de la Ciencia:
- Inteligencia artificial
- Sistemas autónomos
- Aprendizaje automático
Sus antecedentes:
- La validación de la seguridad de los vehículos autónomos es costosa y requiere mucho tiempo debido a los raros eventos críticos.
- Los métodos actuales se esfuerzan por probar de manera eficiente los escenarios críticos para la seguridad.
Objetivo del estudio:
- Desarrollar un entorno de pruebas inteligente para la validación acelerada de la seguridad de los vehículos autónomos.
- Para entrenar agentes de IA para identificar y ejecutar maniobras adversarias.
Principales métodos:
- Desarrolló un enfoque de aprendizaje por refuerzo profundo (D2RL).
- Agentes entrenados que usan datos de conducción naturalistas para aprender maniobras adversarias.
- Utilizó un entorno de realidad aumentada que combina elementos simulados y del mundo real para las pruebas.
Principales resultados:
- Los agentes entrenados con D2RL aceleraron el proceso de evaluación de la seguridad entre 10^3 y 10^5 veces.
- El enfoque mantuvo la imparcialidad en la validación del rendimiento de seguridad.
- Se ha demostrado su eficacia tanto en autopistas como en circuitos urbanos.
Conclusiones:
- D2RL ofrece un método altamente eficiente para la validación de la seguridad de los vehículos autónomos.
- Este enfoque reduce significativamente los costos económicos y de tiempo de las pruebas.
- D2RL es aplicable a la aceleración de las pruebas y la formación de otros sistemas autónomos de importancia crítica para la seguridad.
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